Grand Challenges in Construction Management

Introduction

Construction management (CM) is an important field of management practice oriented research into related issues at both the macro and micro sphere across the entire life cycle (CIOB, 2010) of the built environment. The macro sphere of CM can generally cover all management related issues on the built environment across its life-cycle stages; for example, the industry wide statistics, analysis and projections on codes and standards, building information management, procurement and contracts, supply network, workforce productivity, and workplace health and safety, etc. at national, regional and/or international level. The micro sphere of CM, on the other hand, covers specific issues relating to project delivery at various work stages; for example, project specific study on feasibility, cost plan, design justification, process schedule, risk assessment, quality and traceability assurance, productivity analysis, post occupancy evaluation, and service level agreements, etc. The cross-stage process of CM makes it possible to conduct inter-connected professional services for clients to have maximum value from investment.

Since the principles of scientific management was put forward by Taylor (1911), the theory has profound influence to the evolving theory of management science in over 10 decades, and there has been a significant theoretical evolution in the management world. As summarized by Jones and George (2016), the evolution of management theories in the past century has demonstrated a trans-disciplinary development across five fundamental theories, including the scientific management theory, the administrative management theory, the behavioral management theory, the management science theory, and the organizational environment theory. This theoretical evolution in management science has eventually formed an entire body of knowledge (BOK) for management practice in various professional fields across industry sectors, and consequently it has continuously provided strong theoretical support to CM oriented research and practice.

The purpose of this article is to describe a recent research undertaken by the author to identify grand challenges in construction management in response to present national/regional strategies for the construction industry in world top economies, and to inform decision making on further research and development as well as learning and training with regard to the enhancement of dependability and productivity in construction management services. In order to justify the need for this research, the author has looked into the gigantic accumulation of related resources as collected in not only civil engineering databases at ASCE (2019) and ICE (2019) but also multidisciplinary databases on the Web of Science platform (Clarivate, 2019), and the search term used is “challenges AND construction management.” It has been found that there is limited number of publications related to this topic, and it looks there is currently no publication dedicating to a discussion on grand challenges in CM focused research and development. From this point of view, this article has the opportunity to fill the gap in research into construction management.

This article is based on the author's observations through academic research and professional services in related areas around the world over the past three decades. Based on a further review on grand challenges and strategic priorities for the construction sector in six world's major economies, including United States, China, Japan, Germany, United Kingdom, and European Union, four grand challenges that reflect national/regional strategic priorities were eventually derived as significant ones. Table 1 is used to clarify the reflection of four perceived grand challenges in construction management to existing strategic proprieties in these world's major economies. It's the author's opinion that these four grand challenges are responsive to those national/regional strategies and therefore could be useful with further discussions to inform further research and development in construction management with regard to the pursuit of national/international leadership in this subject field.

According to this simplified independent response to the national/regional strategic priorities for the construction sector in the six world's major economies, this article aims to provide further discussions on the four grand challenges, and focuses on Frontiers of Research and Development in Construction Management with regard to the increasing need for service dependability and productivity in the development and operation of the built environment in both short and a relative longer term. This article therefore covers four topics on the four grand challenges in relation to three identified managerial domains on people/workforce, product/production, and processes, respectively, in the provision of CM services.

In developing the contents of this article, the four grand challenges are used for its structure to cover the following specific discussions:

• The grand challenge on the development and use of a theoretical CMBOK framework and its contents in accordance with research and practice accumulated across disciplines over long term in the past,

• Two grand challenges, in response to the needs for intelligent management systems and interdisciplinary digital innovations respectively, in order to improve the performance of constructions management services, and

• The grand challenge on technical enhancement for construction management in megaproject delivery in order to promote best practices through large-scale construction management services.

The relevance of these topics to the three management domains derived from an extensive review on five main management theories (see Figure 1) is further described in Table 1. This qualitative analysis helped the author to make decision on topics to discuss grand challenges in construction management.

Figure 1

This article is based on the author' recent research through the use of two methods, including extensive literature review and qualitative comparison study, while his long-term experiences and observations in academic research and professional practices were also incorporated. In addition, this article has been thoroughly improved according to a huge amount of insightful comments given by reviewers.

In this article, the author uses terms, including the dependability of construction management services and the dependable built environment, to incorporate the need for dependability (Chen, 2017) at life-cycle stages of the built environment. According to the only three results from searching on Google as of 3 November 2018, the term dependable built environment was also used in practice by the Stonepay Construction in Canada. For the built environment, dependability (BSI, 2014) can be regarded as a collective term for time-related quality characteristics with regard to the ability to perform as and when required throughout the life cycle of either a product or a service. It is the author's assumption that the incorporation of dependability with the technical term of built environment on the product side and construction management on the service side can facilitate an integrated measure that can cover engineering and management aspects connecting to dependability characteristics, which can include adaptability, availability, construability, durability, maintainability, recoverability, reliability, health and safety, security, supportability, and sustainability, etc. It is the author's opinion that the use of dependability for both built environment as the product and its life-cycle oriented services can effectively integrate various interconnected considerations and decision making on people/workforce, products/production, and processes in construction management for quantified quality assurance across its whole life cycle oriented services in the built environment. While numerous cases have shown that a lack of dependability may have significantly adverse impact through rewordings on productivity, it is important to give priority to dependability in the pursuit of productivity in construction management.

It is the author's expectation for this article to be useful for not only colleagues working on innovative research and development in the subject field of construction management, but also the specialty section on construction management at the Frontiers in Built Environment to be part of long-term leadership development in research and development for the construction management profession across the world. As the four described grand challenges were derived in response to current national/regional strategies for the construction sector in major economies, further research and development under the four grand challenges are also expected in order to timely and effectively support technical enhancements on both dependability and productivity in construction management services for the sustainable built environment.

Construction Management Body of Knowledge (CMBOK)

The first grand challenge to discuss is to establish a CMBOK, which can be useful for construction management oriented learning, practice, research and development in terms of both dependable professional services and continuous technical enhancements. Generally speaking, construction management is an elemental subject of the built environment discipline, and it is an essential professional service interactively connecting with other specific professions working inside the construction industry. It has extensive connections to other subject fields such as planning, design, and operation (CIOB, 2010) within the built environment sector, in addition to resources and manufacturing, computing and programming, transportation and supply management, legislations and governances from other adjunct sectors to support its work procedure at various stages. For professional construction management services, there are mature work procedures such as the definitive UK model Plan of Work (RIBA, 2013), the code of practice such as the CIOB (2014) Code of Practice for Project Management for Construction and Development, and regulations such as the Construction (Design and Management) Regulations 2015 (HSE, 2015) in the United Kingdom. Construction management is therefore a dedicated profession that provides services focusing on construction to satisfy both project-wide and industry-wide needs for professional management within the built environment.

Management Domains

The BOK for a specific subject field is generally essential and useful for both practice toward excellence and research for innovation. As a good example, the body of knowledge for project management (PMBOK) (PMI, 2017) has demonstrated the value of BOK in project management across industry sectors over the past two decades. It was originally published and continuously updated at the Project Management Institute (PMI, 2017) to guide practice in project management, and eventually adopted by the International Organization for Standardization (ISO, 2012) as an international standard for project management. In addition to professional guidance for general project management, PMI (2016) has continuously provided the extension to the PMBOK® Guide for construction management since 2003, and this provides useful reference to form the CMBOK. The author would like to determine CMBOK as a set of structured descriptions about professional knowledge and underpinned techniques to sustain dependable quality services of construction management at both macro and micro scale in the built environment.

In connection with the evolution of theories and technologies in engineering and management, the technical scope of construction management has been enlarged, and the delivered service focuses on enhanced dependence and efficiency across life-cycle work stages (CIOB, 2010). It is a matter of fact that an enormous amount of endeavors made by academics and practitioners in construction management worldwide is a huge contribution to form the CMBOK, although there is currently not a definitive framework, such as the PMBOK (PMI, 2016, 2017) and the Civil Engineering Body of Knowledge (CEBOK) (ASCE, 2008, 2019), for construction management in both project-wide and industry-wide scale. It is therefore a big task for further research into the cognitive framework of CMBOK and its elements and contents with regard to three identified cognitive domains, including people, products and processes (See Figure 1), to foster innovations in a more effective way for not only the dependence and efficiency of this professional service in practice, but also quality higher education (Pellicer et al., 2013) and continuing professional development (CPD) (Chen et al., 2006b) for professionals and students, respectively.

For the three technical domains mentioned above on people/workforce, products/production, and processes in construction management, Figure 1 is used to clarify its origin by illustrating the connections between management theories (Jones and George, 2016) to three identified main management domains that can be used to structure the construction management body of knowledge (CMBOK). It summarizes five representative toolkits derived from management theories that can support construction management on three managerial domains, which cover people/workforce, products/production, and processes. These managerial domains are identified through an extensive review into the five management theories with regard to fundamental issues that they are dealing with. As described by Oakland and Marosszeky (2017) and Kajima (2018) based on need for best practices, these domains are essential for total construction management (TCM) that enables a comprehensive use of all contemporary management theories to tackle issues on workforce, production, and processes across project life cycle in the built environment. In theory, TCM can be used as a term for the provision of all types of specific construction management services together under one contract, in comparison with construction management that refers to professional services in a generic term.

It is profoundly meaningful for the CM profession to have a cognitive framework of the body of professional knowledge, like the one ASCE and PMI have been working on for continuous professional development and further research. There are several critical reasons to justify its importance with regard to its value for different stakeholders involved in construction management. For stakeholders such as construction contractors, it could be a useful guide to take the initiative under the blue ocean strategy (Kim and Mauborgne, 2004), which is defined as the simultaneous pursuit of differentiation and low cost to open up a new market space and create new demand, to develop outstanding expertise above horizontal competition. In other words, a bespoke BOK can ensure a sound development of expertise in providing professional services in one or more specific areas within an entire knowledge map. For clients who invest in the construction project, it is a useful benchmark for them to evaluate the performance of construction management at various project stages. In addition to its value for construction management practice, the cognitive framework of construction management body of knowledge can also be useful to inform higher education (Arditi and Polat, 2010; Nguyen et al., 2017), professional training, as well as academic and professional research in terms of well-scoped depth and long-term continuous professional development. It is therefore considerate to develop a cognitive framework of BOK for the construction management profession.

Cognitive Domains

The structure of CMBOK, as one particular focus of this article to promote an initiative development for the whole construction management profession, can be outlined by using related cognitive domains, also called knowledge areas or domains, as structural elements of professional knowledge for the framework. For the structure of CMBOK, it therefore becomes critical to define its cognitive domains and their contents in order to establish such a framework of professional knowledge.

The definition of cognitive domains relies on a comprehensive coverage to both explicit and tacit knowledge (Dalkir, 2017), which is required for quality services in specific professional areas such as civil engineering, construction management, and project management, respectively. One particular effort to identify all cognitive domains relating to construction management is to review possible ones adopted in existing BOKs such as CEBOK (ASCE, 2008, 2019) and PMBOK (PMI, 2016, 2017) and to make holistically consolidated connections to construction management profession. In the meantime, the review needs to incorporate a thorough consideration on work procedure such as the Plan of Work (RIBA, 2013) with regard to interconnections among identified cognitive domains; moreover it is always necessary to incorporate learning from the best practice in CM. At the initial stage of CMBOK development, as described here, a technical review into the two existing BOKs was conducted through a comprehensive comparison between them and this comparison study can help to develop the CMBOK framework at initial stage.

The sources of knowledge are critical for the establishment and justification of a dependable cognitive framework of professional knowledge. Two reference knowledge frameworks including CEBOK (ASCE, 2008, 2019) and PMBOK for construction management (PMI, 2016) are recommended for both the structure and contents development of CMBOK. Through the author's initial study on an outline structure of CMBOK, a preliminary framework of 18 cognitive domains under 3 management domains is put forward in Table 2. This preliminary main structure was developed in comparison with the two highly relevant cognitive frameworks, i.e., CEBOK and PMBOK for CM, respectively, and through evidence based learning from various sources, including:

• Standard industrial classifications such as the UK Standard Industrial Classification of Economic Activities (ONS, 2009),

• Databases run by both professional bodies, including the ASCE Civil Engineering Database and the ICE Virtual Library, and professional publishers, including Elsevier, Emerald, McGraw-Hill, Sage, Springer, Taylor & Francis, and Wiley, etc.,

• Books published in construction management by world-renowned scholars,

• Professional reports by top construction management organizations such as high-ranked main construction contractors (EMAP, 2017, 2018; ENR, 2018),

• Degree programmes accredited by professional bodies such as the American Council for Construction Education (ACCE, 2017), the Chartered Institute of Building (CIOB, 2018a), and the Royal Institution of Chartered Surveyors (RICS, 2018), and

• The author's extensive observations on site and online in relation to construction management for numerous projects worldwide.

The 18 cognitive domains proposed as the preliminary main structure of CMBOK (see Table 2) are divided into three categories in terms of management domains, including People/Workforce, Product/Production, and Processes, which are consistent with the three generic management domains identified through the author's extensive review on five main management theories (see Figure 1). The incorporation of the three management domains into structuring CMBOK here is based on the following considerations:

• CM practices are well-guided and supported by generic management theories,

• The development of CM theories is based on both management science and professional practices,

• The three management domains can provide an extensive coverage to CMBOK related issues in not only professional practices, but also education, training, research and development, and

• The need for and potentials of further research and development to sustain dependable CM services for the sustainable built environment.

Details about how the preliminary main structure of CMBOK (see Table 2) was defined under these four considerations are describe below in response to the three management domains.

For the management domain on People/Workforce in the case of construction management, four cognitive domains are proposed here for the CMBOK framework, and these include the management of Enterprises in relation to construction organizations, the management of Knowledge to support construction practices, the management of Stakeholders involved in various construction operations, and the management of Workforce to undertake construction activities at both project and non-project scale. The choice and definitions of People/Workforce related cognitive domains for CMBOK are essentials for developing its technical details relating to the constitution and competence of workforce working on either building or infrastructure project at various stages where construction management services are in need.

For the management domain on Product/Production in the case of construction management, seven cognitive domains are proposed here for the CMBOK framework, and these cover the management of construction project at various work stages across the whole life to cover the management of Cost across project life cycle; the management of Design upon interconnected technical outcomes across three stages covering concept design, developed design, and technical design; the management of Engineering solutions during construction, maintenance, repair, refurbishment, decommissioning and demolition; the management of Facilities in terms of relevant professional services at operation stage; the management of Plant at both construction and operation stage, the management of Quality of built assets in construction, maintenance, repair, refurbishment; the management of the use of Resources in relation to key considerations on quality, quantity, and embodied energy, etc. The choice and definitions of Product/Production related cognitive domains for CMBOK are essentials for developing its technical details relating to the physical products and production of buildings and infrastructures that construction management profession can support.

For the management domain of Processes in the case of construction management, seven cognitive domains are proposed for the CMBOK framework cover the management of Communications, the management of Supply network, the use of Management systems for various purposes on either individual issues such as environment, health, quality, safety, and welfare, or all tasks of the entire project; the management of Procurement through sound procedure and effective contract control; the management of Risk and Time at various work stages and scopes across project life cycle; and the management of Changes and Emergency whenever unexpected issues may occur in the whole project life. The choice and definitions of Processes related cognitive domains for CMBOK are essential for developing its technical details relating to a whole range of processes of making the built environment built through construction management services.

In addition to the main structure of CMBOK framework, there is another aspect constituting the entire challenge of developing CEBOK, for which the substructure of the main knowledge framework under the 18 cognitive domains described above needs to be defined. It is the author's consideration that the definition of the substructure system also relies on all knowledge sources identified for the proposed preliminary main structure of CMBOK.

A further review into three key sources of information was conducted to detect whether the 18 cognitive domains proposed as the preliminary main structure of CMBOK could be recognized at a satisfactory level. Table 3 presents a comparison from this review, in support of three sets of key references selected from CIOB, British Standards Institution (BSI), and ISO, respectively.

The three sets of key references (see Table 3) selected for this comparison study include:

• CIOB (2014) Code of practice for project management for construction and development, in harness with several other related publications including the Design Manager's Handbook (Eynon, 2013), CIOB (2015) Time and Cost Management Contract suite, CIOB (2018b) report on Improving Quality in the Built Environment, CIOB (2018c) Guide to Good Practice in the Management of Time in Major Projects, and CIOB (2018d) course on Accident Investigation and Root Cause Analysis.

• BSI (2006) Guide to project management in the construction industry, and

• ISO (2017a) standards for construction, in harness with several other related ISO standards with regard to improving the performance of construction management services, for which relevant issues on quality, productivity, and sustainability, etc. (Bernold and AbouRizk, 2010) need to be dealt with.

It looks from this comparison study that all 18 cognitive domains proposed to structure the CMBOK are well-covered by three sets of key references in terms of various issues in construction management practice. This coverage indicates a good justification associated with the preliminary main structure of CMBOK proposed here. Therefore, the CMBOK framework proposed in Table 2 is recommended for further use to fulfill the gap on the shortage of such a BOK currently available for construction management at various scales in terms of practices, education and training, and research and development.

For CEBOB, in addition to its structure system, it is a continued process supported by professionals to add contents into this structured knowledge system. The contents of CEBOK under an established knowledge structure are gigantic in terms of the amount of knowledge accumulated from long-term practice and research in construction management across industry sectors worldwide. From this point of view, it is ideal to have an international organization to lead the development and maintenance of CMBOK, which can be widely recognized and used for construction management.

Intelligent Construction Management System

The second grand challenge to discuss is the establishment and utilization of intelligence pervasive construction management systems to thoroughly support project-based practices at workplaces across work stages. From the author's point of view, an intelligence pervasive management system is a computer aided management system that incorporates the use of experts' knowledge and artificial intelligence (AI) based on the collection and process of relevant data and information from the real world. With regard to the importance of Industry 4.0, the construction industry is now under rapid transformations to adopt digital technologies that enable innovations in products and processes across the supply chain network, and it has become inevitable to incorporate new concepts driven by digital technologies into traditional management systems such as management information system (MIS) and enterprise management system (EMS) for construction management.

It has been always an inspiring learning experience for the author to look into progresses on research and development in the theory and solutions of AI since he put forward the concept of intelligent methods for construction engineering and management (Chen and Xu, 1996; Xu and Chen, 1997). In connection with the fast development of information and communication technologies (ICT) since 1990s, recent research and development in applied AI solutions for enhanced practice in construction management has shown insightful perspectives and effective progressions. For example:

• The use of artificial neural network (ANN) (Waziri et al., 2017) across various predication related issues in construction management,

• The use of knowledge sustained analytic network process (ANP) (Chen, 2007) to evaluate options for informed decision making in construction management, and

• The integration with automation technology to deploy single-task and multi-task robots (Skibniewski, 1988; Castro-Lacouture et al., 2007; Bock and Linner, 2016; Black and Pettitt, 2018) in construction projects.

It has been further emphasized by Blanco et al. (2018) recently regarding the adoption of applied AI in construction management in terms of project planning optimization, constructability justification, materials and inventory management, and risk and safety management; and these applications all demand a systems-based approach (Briesemeister, 2018) to improve the quality and productivity of construction management. It looks that an integrative use of data, information and knowledge (DIK) (Allee, 1997; Walker, 2016) is now just in front of the construction management profession, while the DIK set can be collected from either construction projects or construction professionals in dealing with specific issues such as those listed in Table 2 in a more accurate and efficient manner.

In response to the increasing need for comprehensive use for DIK in construction management, a new concept about intelligent construction management system (iCMS) is described below in terms of its concept, purpose and value, conceptual model, and two tier systems. Hopefully this description could be useful for further considerations, discussions, and research and development in this area.

Concept

An iCMS is a construction management system aided by AI techniques that can trigger human/rational learning, thinking and acting (Russell and Norvig, 2010) in connection to integrated DIK clusters and their elemental sets (see Table 4), which are specifically structured on a spectrum of expected helpfulness to assist the use of human intelligence at an enhanced consistent level in CM practice.

The entire DIK set need to be carefully collected with continuous updating manually and/or automatically from various sources under the CMBOK framework, and this ensures the system performs effective practical support connecting with experts' wisdom, which can be derived from computing processes at different scopes covering project/programme level, enterprise/group level, and local/regional level, etc. in a high efficient manner toward excellent professional performance. The iCMS needs to be well-connected with DIK sources covering the management team and a range of databases and knowledge bases to ensure dependable support to construction management practice.

Purpose and Value

The purpose of incorporating an iCMS in practice is to equip the project-oriented management team with dependable technical toolkits for better CM service delivery. The iCMS toolkit aims to facilitate the integrative use of not only expertise and knowledge accumulated from the past practice, but also data and information collected from the on-going project in real time. From the best practice point of view, specific DIK sets are required to deal with critical problems relating to the management of people/workforce, product/production, and processes across project stages.

The value of iCMS for construction management practice is its function to precisely perform calculated decision making support through the effective and efficient use of DIK. This calculation can be made through co-computing processes over a gigantic body of chaotic interactions among workforce, production, and processes within the dynamic project environment across work stages. Therefore, the iCMS is to provide a systematic approach to better using DIK that quantify chaotic interactions in construction projects. In terms of the need for continuous excellent performance across construction management teams, the iCMS is to run through computing systems as an essential smart assistant.

Conceptual Model

A conceptual model of iCMS is illustrated in Figure 2. The intelligence expected from such a system for construction management can be achieved in its capacity on the integrative use of relevant DIK through a formal work procedure based on individual management systems such as environmental management system (Chen and Li, 2006), health and safety management system (Griffith, 2010), knowledge management system (Anumba et al., 2005), quality management system (Thorpe and Sumner, 2004; Rumane, 2018), and project management system (Chen, 2018), etc., which can be dynamically connected inside individual project-oriented computer systems in connections with building information models (BIM) for continued accurate inputs and useful outcomes.

Figure 2

This conceptual model illustrated in Figure 2 consists of four functional entities that are connected through DIK and/or supporting procedures, and these entities include CMBOK, CM practices, intelligent CM systems, and AI technologies and other digital technologies.

A two-way connection between entity CMBOK and entity CM Practices is set up to describe their relationship, which covers the two aspects, including:

• CMBOK as a collection of well-structured professional knowledge is to accumulate relevant DIK from CM practices, and the inputs under a given CMBOK structure are collected from CM practices, which covers all individual practices on construction management; and

• CMBOK would influence/support CM practices at various scales through purpose driven application/reuse and/or continuous learning/training based on its classified collections.

For the entity of intelligent CM systems, it has two sub-entities forming an entire tier system, which is described in the next section. This entity is integrated with three other entities in the following ways:

• CMBOK provides the structure and contents of DIK for individual intelligent CM systems,

• CM Practices can be assisted/supported through the use of individual intelligent CM systems, and

• AI technologies and other digital technologies are adopted to support individual intelligent CM systems with expected functions.

It is anticipated that this conceptual model can be useful to clarify the concept of iCMS and its research and development toward intelligent construction management.

Tier Systems

Exploratory research facing grand challenge on intelligence systems for construction management can be conducted through two stages, including the initial stage on sub-systems, i.e., individual management systems, and the secondary stage on an entire iCMS system, i.e., an integration of all its elemental sub-systems. They are called Tier 1 systems and Tier 2 systems, respectively, here based on different functions that individual systems can provide. Generally speaking, Tier 1 systems are sub-sets of Tier 2 systems. The research and development of the two types of systems highly rely on thorough system analysis and design, reliable inputs, and effective use of DIK within the system to support usages in construction management upon various issues and situations. A brief description about the two types of systems is given below for discussion toward further actions in research and development.

Tier 1 systems: Individual intelligent management systems. These systems are designed, developed and implemented for construction management in specific areas as listed in Table 2. For example, the system for purposes on managing people/workforce, product/production, and processes, respectively, could be:

• An intelligent EMS for managing people/workforce involved in construction projects or project enterprise at various levels in an organization of either clients' or contractors',

• An intelligent cost/quality management system for managing product/production targeted by either an organization of clients' through the investment into their capital projects, or an organization of contractors' through the delivery of entrusted construction projects, and

• An intelligent information and communication management system for managing processes deployed within an organizational environment of construction projects on either clients' or contractors' side.

The intelligent functionality and capability of these individual management systems need to be casted by incorporating a number of interconnected inputs on DIK under the CMBOK framework. There have been initiatives in research and development in Tier 1 systems. For example, Grahovac and Devedzic (2010) presented an expert system to support decision making in cost management; although it was not for construction cost management, their experiment has indicated a promising application of intelligent cost management system in construction, and such an application can be hybrid by incorporating the use of other relevant techniques such as the research that Chou et al. (2015) have conducted by interactively using Genetic Algorithm (GA), ANN, and Case-based Reasoning (CBR) to predict project award price, and this hybrid use of intelligent methods can be further integrated with BIM (Allplan, 2018) for intelligent cost management at various project stages. By learning lessons from other sectors, such as the healthcare sector where it was found that an intelligent cost management system, for example, can leverage relevant data to swiftly identify opportunities so as to achieve savings of $10 per member per month (PMPM) with a conservative calculation (MedSolutions, 2013), it can be anticipated with a positive sensation that the adoption of individual intelligent management systems in construction can significantly improve construction management on various aspects.

According to the concept of iCMS, the use of well-developed practical solutions of AI is essential for Tier 1 systems. In addition to the example given above, it is obvious through literature review that initiatives in research and development in Tier 1 systems have widely developed through the use of AI techniques in natural language processing, knowledge representation, automated reasoning, machine learning, computer vision, and robotics (Russell and Norvig, 2010) for construction management. The following are good examples for Tier 1 systems:

• A natural language processing system to extract precursors and outcomes from unstructured injury reports (Tixier et al., 2016),

• A knowledge extraction and representation system for narrative analysis in the construction industry (Yeung et al., 2014),

• An expert system for crack diagnosis in cast-in-place reinforced concrete structures (Chen et al., 1999),

• A construction noise prediction model based on case-based reasoning in the preconstruction phase (Kwon et al., 2017),

• A machine-learning model for estimating construction costs (Rafiei and Adeli, 2018),

• A computer vision-based workforce activity assessment in construction (Luo et al., 2018),

• A robotic approach to construction waste recycling (Wang et al., 2019).

The adoptions of individual AI techniques in these exploratory research and development have demonstrated the effectiveness of Tier 1 systems to support construction management, and these systems also shows potentials for integration to form a more powerful iCMS toward Tier 2 systems.

Tier 2 systems: Integrated intelligent management system. In principle, an integration of several individual Tier 1 systems can be further developed by using groupware (collaborative software) technology to form collaboration systems (Nunamaker et al., 2014), and this integration is expected to enable more functional intelligent construction management system. From a conceptual point of view, the integration of selected Tier 1 systems can yield one Tier 2 system that can support construction management activities in a transdisciplinary way in multiple functional areas covered by Tier 1 systems, respectively. There are different levels of system integrations, for example, it can be either an integration of several individual Tier 1 systems for project-specific intelligent management based on individual contracts with clients, or the integration of all individual types of Tier 1 systems to form all-round intelligent management for general use by main contractors. In other words, for a bespoke iCMS, the scope of system integration may reflect the scope of construction management contract for which the system is expected to work for in specific projects. On the other hand, there is another type of Tier 2 systems, for example, the i-Construction system (STI, 2017b), which is developed as an entire system with sub-systems to perform designed functions for generic use to support CM activities across all types of projects. In comparison with groupware integration that satisfy specific needs for construction management at suitable workplaces in the project environment, the new development of an entire intelligent system faces more challenges in terms of the incorporation of complex sub-systems and human intelligence with thorough connections to CMBOK and DIK in a general way to make enable all necessary functions for construction management.

There are specific issues to be dealt with in the research and development for iCMS. For example, it is essential to not only sufficiently describe DIK and sources for collection, but also accurately collect and effectively use them in order to make the system useful. By learning lessons from construction management practice in relation to the use of computer systems such as a BIM system, for which a comprehensive collection of relevant data and information from construction stage may not be typically well-functioned inside the system and consequently leave data gaps less filled across design, construction and operation stage. Therefore, a set of essential questions and answers under systematic considerations connecting to multiple disciplines within construction projects may help system design, development and usage.

Digital Innovations

The third grand challenge to discuss is the digital innovation throughout its project-oriented work procedure. The need for and demand on innovative reengineering (Love and Li, 1998) in construction management has progressively emerged and increased through the requirement and provision of dependable management services in the construction sector. In the meantime, the availability of advanced technical solutions in sciences and technologies has greatly supported (with huge potentials) the technical enhancement of construction management by incorporating interdisciplinary digital innovations. The discussion here focuses on adopting advanced technologies in relation to the theories and solutions of informatics and automation for further development in construction management.

Construction Informatics

Construction informatics is the interdisciplinary science of the application of advanced computing and ICT to construction engineering and management. The adoption of technical solutions based on ICT driven research and development has been significantly increasing in construction management over the past several decades. For example, technologies such as artificial intelligence (AI), artificial reality (AR), which is the author's term to cover virtual reality (VR), augmented reality (AR), mixed reality (MR), BIM; geographic information systems (GIS), global positioning system (GPS), management information system (MIS), and process simulation (PS) etc. have all been applied in research and development for practice enhancement in management on people/workforce, products/production and processes in building and infrastructure projects. Table 5 makes a summary of exemplar research and development initiatives focused on adopting various digital innovations for construction management through research and development. These initiatives were identified from not only relevant strategies and practices by top international construction contractors, including Balfour Beatty, Bechtel, Kajima, Laing O'Rourke, and Skanska, but also some representative academic research published in China, European Union, UK, and USA.

These technical innovations have demonstrated anticipated results such as significantly improved work efficiency via multidisciplinary construction information management although there are still potentials for further research and development in terms of system functionality and interoperability. On the other hand, it is also necessary for interdisciplinary digital innovation to incorporate useful ideas from various professions relating to project management. For example, it is an unresolved question regarding how the incorporation of economics, psychology and sociology (Harty, 2005; Winch, 2009; Mascia, 2012; Walker, 2015) focusing on the performance of individuals and project team into ICT systems for construction management can make further technical enhancement. Further interdisciplinary digital innovations through the use of theory and techniques in construction informatics may therefore need to consider both tangible and intangible aspects within project environment and related scenarios.

From a research and development point of view, there are a number of areas in interdisciplinary digital innovation for enhanced practice and learning in construction management, and these areas can be connected to the three identified domains (see Figure 1) of construction management with regard to innovations in:

• People/Workforce management. For example, DIK collection, analysis, and integrative usage,

• Product/Production management. For example, Performance (on Cost, Quality, and Schedule, etc.) modeling, monitoring and assessment, and

• Processes management. For example, Process (for Construction Engineering and Management) reengineering design and coordinated implementation.

In the area of applied AI with regard to the need for alternative research approaches (AlSehaimi et al., 2013) as well as the provision of useful technical solutions, the deep learning technology has indicated a promising future for adoption in order to improve the productivity of construction management professionals, and the technical enhancement needs to be pursued toward accuracies in management in terms of interconnected working efficiency within networked teams to effectively tackle persistent problems relating to major issues on cost, quality and time in project delivery.

Construction Automation

Construction automation is regarded as the use of automatic equipment or machines in construction. The automation technology has been increasingly studied and adopted in the construction sector (Bock, 2015; Chen et al., 2018a; Hawksworth et al., 2018) to improve not only the quality of products but also the efficiency of processes as well as resources use. For construction management, new industrialized construction engineering solutions have been introduced to practices, and these include:

• Design for Manufacturing and Assembly (DfMA) (BCA, 2016),

• Design for deconstruction/disassembly (DfD) (Rios et al., 2015),

• Digital production systems such as 3D printing (Bechtel, 2018; De Laubier et al., 2018; García de Soto et al., 2018; Skanska, 2018),

• Digital data collection and construction verification systems such as drones (Skanska, 2018), and 3D scanning (Faro, 2019),

• Autonomous construction equipment (Black and Pettitt, 2018) and vehicles (Bechtel, 2018), and

• Construction site automation (Kajima, 2018).

These industry led initiatives have shown progressive achievements and new opportunities for lean construction (Alarcón, 1997) toward a wide range of technical enhancements at interdisciplinary scale that integrates construction engineering and construction management across the supply network. For both research and practice to be well-prepared for this disruptive innovation in the construction sector, it is important to learn lessons from the manufacture industry where the automation technology and industrialized automation systems have been very well-developed and widely used in a process of continuous innovations over the past many decades, and this can significantly change the landscape of construction management in terms of professional competence and resources efficiency oriented performance toward the best practice. The adoption of automatic systems in construction projects brings the opportunity for professionals to reconstruct (Morris, 2013) management procedure and associated activities and systems.

For interdisciplinary digital innovation in construction management, it has been learnt from past research and development that both decent deep learning from and efficient DIK sharing in multiple disciplinary areas are necessary and beneficial for a dedicated construction management team; in addition, a fast paced innovation process driven by well-organized collaboration under the Blue Ocean strategy (Kim and Mauborgne, 2004) can be achieved through team-based multi-disciplinary learning, which can foster as well as enable powerful integrations of new digital DIK compounds into existing construction management. For example, the Asta Powerproject (Elecosoft, 2018), which was originally developed for construction planners to develop and maintain project schedules by using the critical path method (CPM) like other same type of software packages for managing construction schedule, has now evolved to be a powerful 4D planning solution by linking project plans made by construction planners to 3D models provided by design engineers so as to match the need for adopting BIM in construction management. The multidisciplinary connection realized in this software package has demonstrated the advantage of further research and development in leading the way forwards. A continuous leadership in research and development is therefore inevitable by providing new solutions on not only Construction Informatics but also Construction Automation, and those innovative digital solution can satisfy new needs for digitalization across various interconnected disciplines within the construction project environment to serve more (wider and deeper) in required management services.

Megaproject Delivery

The fourth grand challenge to discuss is the delivery of megaproject with regard to satisfactory performance toward targets on life-cycle cost, quality of the built environment, resources use, and staged schedule, etc. A megaproject is a large-scale capital project typically costing more than US $1 billion (PricewaterhouseCoopers, 2014), and it can be considered as a subset of major projects which have a wide scope of project costing above US100m for example. Megaproject has its uniqueness in terms of their significant and substantial impacts on social, technical, economic, environmental and political (STEEP) spheres. Generally speaking, these impacts are related to the big set of designed functions and service capacities under the purpose to transform public services and to deliver a robust infrastructure system to serve people and/or the nature in both short and longer term. There have been a huge amount of lessons learnt from past practices on megaproject development and operation across the world. One of the significant and persistent problems in megaproject delivery is the overruns on cost and time against estimated budget and schedule, and this has been widely recognized as specific megaproject risks (Flyvbjerg et al., 2003) and the need for mature risk management (Jia et al., 2013). It is therefore a grand challenge for further research and development to explore better technical solutions that are capable to support significant performance improvement on construction management in megaproject delivery. From this point of view, the description here about the grand challenge in megaproject delivery focuses on two relevant issues, including megaproject knowledge, and knowledge-driven solutions.

Megaproject Knowledge

It is evident through the author's extensive literature review and observations from both research and practices that a new research frontier in megaproject management with regard to scope, contents and potentials in relation to challenges, methodologies and solutions has emerged from a series of initiatives in focused research and practices. Table 6 is used to summarize the author's literature review in relation to megaproject knowledge, and it covers a range of topics about the performance of megaproject delivery. According to this summary, publications from research and practices around the world, especially from world's top economies, have accumulated a quick increase of megaproject knowledge in the past decade, and can strongly foster and support further research and development being embarked on this new frontier for research into megaproject delivery. As summarized in Table 6, accumulated knowledge for megaproject delivery can be classified in the following four categories of publications to form a new landscape of dedicated research:

• Books on various issues relating to megaproject sustainability with regard to STEEP issues;

• Books on methodological issues about successful megaproject delivery in relation to Decision making, Project finance, Project governance, Project leadership, Multi-stakeholder lessons learned, Project procurement, Management solutions, and Management theories;

• Themed collection of research articles in 12 international journals; and

• Practitioners' learning legacy on megaproject delivery.

The purpose of Table 6 is to develop a knowledge structure in terms of important issues relevant to performance improvement in megaproject delivery. Knowledge materials that were used to establish this structure include books, themed issues of journals, and practitioners' learning legacy. With regard to other types of relevant publications such as numerous individual articles published in journals and newspapers, in addition to case oriented study, a further review and collection can be conducted to verify and improve this structure, to enrich the collection of knowledge materials, and to incorporate megaproject knowledge into CMBOK. As it is always important to use DIK in both effective and efficient way in construction management practice, one challenge in front of further research and development is inevitably to focus on knowledge-driven solutions, which are expected to provide well-informed decision making support in areas relating to construction management. For megaproject, due to the scale and complexity of management, the value and power of structured knowledge reuse are expected and in need for proofs from future practices.

Knowledge-Driven Solutions

Knowledge-driven solutions refer to techniques and tools that can facilitate an effective and efficient use of knowledge in dealing with questions or problems in management practices. For megaproject delivery, it is assumed that one possible solution to effectively tackle the problem on overruns within an enlarged project scale, which is probably beyond the one where traditional theories, techniques and processes can work well for small-size projects, is to deploy significant technical enhancement through the development and enablement of re-engineering-led leadership and capability on various aspects of construction management. In addition to the adoption of digitalized tools to increase productivity at work stages, knowledge-driven solutions that can facilitate a thorough use of DIK in construction management are reckoned to improve the quality of professional services and expected outcomes with regard to contract, legislation, and professional code, etc.; and to increase the momentum that the use of DIK can add into the culture and impacts of construction management enterprises.

Figure 3 is used here as an example to illustrate how knowledge-driven solutions can be crafted for decision making support in a DIK immersive environment of megaproject management. It aims to establish possible connections among five solutions, including:

• Information models such as BIM and CIM to accumulate DIK from stakeholders and site of the megaproject,

• Megaproject case base to store DIK from current and past megaprojects,

• Analytic network process to reuse DIK to identify priorities among project related options,

• Artificial neural network to reuse DIK to derive conclusions for individual project related goals, and

• System dynamics to reuse DIK to predicate possibilities upon specific project related issues.

Figure 3

This figure also provides indications about all connections across exemplary solutions to clarify how stakeholders and experts can work together within DIK immersive project environment where various ways of using knowledge are made available and integrated toward better-performed services in construction management.

An example of research into the use of knowledge-driven solutions to deal with the big issue on cost and time overruns in megaproject delivery is the experimental case studies conducted by Boateng et al. (2017) who explored the integrative use of ANP (Saaty, 1996) and system dynamics (SD) (Sterman, 1992) to predict cost and time overruns in the Edinburgh Tram Network project, which has a total capital cost of approximately £776 million (Cardownie, 2017). The outcome from their experiment provided a promising accuracy above 80% in prediction on cost and time overruns, and the simulation method can be interactively used in project management to achieve optimized delivery processes across stages by timely adjusting errors that may cause problems. While it is necessary to reinforce structural and governance arrangements (Croft et al., 2016) in megaproject delivery, this research initiative and outcome is making a suggestion on the necessity to incorporate disruptive technical solutions in megaproject delivery, and has attracted practitioners' interest. As described by Davies et al. (2009), innovation through process-oriented systems integration by incorporating lean thinking (Womack and Jones, 1996) can significantly improve the performance of megaproject delivery. It is therefore anticipated that disruptive innovations such as the knowledge-driven systems through introducing the usages of new theories and technologies into megaproject management can effectively tackle the persistent problem such as the overruns on budget and schedule, and there is an array of value proposition canvases (Osterwalder et al., 2014) for reconstructing project management (Morris, 2013) toward proactive results through further practice-oriented exploratory research.

Regarding the challenge on megaproject delivery, as it is defined in this article as the one for best practices for which methods described under, the first three challenges need to be used in an integrative way.

Conclusions

This article provides brief discussions on four grand challenges connecting to further research and development in the subject field of construction management with regard to three technical domains on people/workforce, products/production and processes, respectively. In addition to an extensive review into current national/regional strategies for the construction sector in major economies, the four grand challenges were identified through the author's observations on academic research and professional practices in relation to the body of professional knowledge, intelligent management systems, interdisciplinary digital innovation, and megaproject delivery. It is the author's expectation that discussions presented here on the four grand challenges could be useful to inform further research and development at the Frontiers of Construction Management for the dependable built environment.

Contributions

The main contribution of this article is to provide an initial description about four identified grand challenges in construction management together with regard to further research and development under practice oriented strategies at various scales. In order to detail the four grand challenges in relation to leadership development and capability growth for the goals on both dependability and productivity in the provision of construction management services, this article has attempted to clarify four related issues for the advancement of construction management profession, and these include:

• A preliminary structure of cognitive domains of CMBOK,

• The definition with a conceptual model of iCMS,

• A research and development strategy oriented review to further digital innovations under the three knowledge domains of CM, and

• A detailed summary on the structure and tools of using knowledge materials to inform further research and practices for leadership development as well as capability growth in megaproject delivery.

The structure adopted to derive the four grand challenges is generic and could be useful for further discussions on other grand challenges.

Framework and Advantages

The connections among research and development activates across the four grand challenges can form a generic framework to advance the construction management profession. These connections need to include the use of the proposed CMBOK framework as a guideline to develop iCMS, make and perform constant interdisciplinary digital innovations, and conduct improved megaproject management. In return, continuous outcomes and usages of new technical solutions from research and development in iCMS and interdisciplinary digital innovations, as well as megaproject management practice can provide new compounds of CMBOK. The reason for setting up these coherent connections is that it is an essential requirement for such intelligent systems as well as interdisciplinary digital innovations to be both applicable and useful in construction management practices, including those in megaproject delivery. It is therefore an important task for construction management professionals to specify decent connections to CMBOK when a new technical solution is to be developed and adopted. The advantages of using CMBOK framework to guide and connect research and development activities in construction management can be recognized from various sides, and these include standardizations in not only research and development but also professional services, the enrichment of professional knowledge, constant CPD, and extensive interdisciplinary collaborations, etc.

Limitations

This article is based on the author's literature review and observations in construction management, and these were used in discussions on the four grand challenges through qualitative analysis. Although reviewers' comments have hugely helped him to conduct further research in order to illustrate a more vivid landscape with regard to the four identified grand challenges in construction management, there is limited time for him to make the description comprehensive with more details in both qualitative and quantitative way. Further research is inevitably necessary so as to not only describe the four grand challenges in more details, but also identify more technical challenges inside different scopes so as to pursue excellent professional services in construction management.

Remarks

Climate change and variability has been giving challenges to construction management professionals around the world over the past century (Stehr and von Storch, 2000). In the pursuit of dependability and productivity of professional services in the life cycle of the built environment, professionals need to have thorough considerations upon constant interactions of complex variables between the built environment and the social environment within the natural environment in both short and longer term. While the four grand challenges discussed in this article were derived from a literature review on current national/regional strategies for the construction industry in world top economies, the author would like to recommend further research and development for long-term competence enhancement in areas relating to the four grand challenges, and these advances include:

• A national/regional CMBOK to support the provision of construction management services in the ear of Industry 4.0, and it can be used to develop relevant standards for construction management,

• Knowledge-driven solutions for thorough use of DIK within an immersive environment for construction management, and

• Techniques and tools for construction management incorporating with site automation, and

• Theories, techniques and tools to tackle overrun problems in megaproject management.

About the Author

The author is specialty chief editor for the Construction Management section of Frontiers in Built Environment. He has relevant experiences accumulated from academic and professional services worldwide since later 1980s. He has engaged in more than 50 funded research projects totalling over £5m from research councils and industry partners in UK and internationally. In collaborations with colleagues worldwide, he has made contributions to more than 200 publications, including more than 50 publications collected on the Web of Science (Researcher ID C-1587-2010), in addition to some other publications collected at ORCID 0000-0003-0212-1140 and Google Scholars. He is member of the management committee of COST Action TU1003 (2011-2015) for research into the effective design and delivery of megaprojects in the European Union. He serves as member at several relevant technical committees, including committees on Airport Planning and Operations, and the CEBOK, respectively, at ASCE, and committees on Facilities Management, and Project, Programme and Portfolio Management, respectively, at BSI.

References

• 1

  ACCE (2017). Annual Report 2016–2017. San Antonio, TX: The American Council for Construction Education (ACCE).

  • Google Scholar

• 2

  AlarcónL. (1997). Lean Construction. London: CRC Press.

  • Google Scholar

• 3

  AlleeV. (1997). The Knowledge Evolution: The Knowledge Evolution: Expanding Organizational Intelligence. Newton, MA: Butterworth-Heinemann.

  • Google Scholar

• 4

  Allplan (2018). Intelligent Cost Management. München: Allplan Deutschland GmbH. Available online at: https://www.allplan.com/fileadmin/user_upload/countries/international/pdfs/references/Design2Cost_CaseStudy_Elwert-Grundschule_EN.pdf (accessed September 23, 2018).

  • Google Scholar

• 5

  AlonsoA.DallmeierF.ServatG. P. (2013). Monitoring Biodiversity: Lessons from a Trans-Andean Megaproject. Washington, DC: Smithsonian Institution Scholarly Press.

  • Google Scholar

• 6

  AlSehaimiA.KoskelaL.TzortzopoulosP. (2013). Need for alternative research approaches in construction management: case of delay studies. J. Manag. Eng.29:4. 10.1061/(ASCE)ME.1943-5479.0000148

  • CrossRef
  • Google Scholar

• 7

  AnumbaC. J.EgbuC. O.CarrilloP. M. (2005). Knowledge Management in Construction. Oxford: Blackwell Publishing Ltd.

  • Google Scholar

• 8

  ArditiD.PolatG. (2010). Graduate education in construction management. J. Profession. Iss. Eng. Edu. Pract.136:3. 10.1061/(ASCE)EI.1943-5541.0000014

  • CrossRef
  • Google Scholar

• 9

  ASCE (2008). Civil Engineering Body of Knowledge for the 21st Century, 2nd Ed. Reston, VA: American Society of Civil Engineers (ASCE).

  • Google Scholar

• 10

  ASCE (2019). Civil Engineering Database. Reston, VA: American Society of Civil Engineers (ASCE).

  • Pubmed Abstract
  • Google Scholar

• 11

  Balfour Beatty (2017). Innovation 2050: A Digital Future for the Infrastructure Industry. London: Balfour Beatty plc.

  • Google Scholar

• 12

  BCA (2016). BIM for DfMA (Design for Manufacturing and Assembly) Essential Guide. Singapore: Building and Construction Authority (BCA).

  • Google Scholar

• 13

  Bechtel (2018). The Bechtel Report 2018. Reston, VA: Bechtel Corporation.

  • Google Scholar

• 14

  BEIS (2018). Industrial Strategy: Construction Sector Deal. London: Department for Business, Energy & Industrial Strategy (BEIS).

  • Google Scholar

• 15

  BerechmanJ. (2018). The Infrastructure We Ride On: Decision Making in Transportation Investment. Berlin: Palgrave Macmillan, Springer.

  • Google Scholar

• 16

  BernoldL. E.AbouRizkS. M. (2010). Managing Performance in Construction. Hoboken, NJ: John Wiley & Sons, Inc.

  • Google Scholar

• 17

  BGS (2012). Accessing Subsurface Knowledge—ASK Network. Edinburgh: British Geological Survey (BGS).

  • Google Scholar

• 18

  BIS (2013). Construction 2025: Industrial Strategy for Construction—Government and Industry in Partnership. London: Department for Business, Innovation & Skills (BIS).

  • Google Scholar

• 19

  BlackE.PettittJ. (2018). Boston Dynamics is Putting its Robotic Dog, Spot, to Work on Construction Sites. Tokyo: CNBC LLC.

  • Google Scholar

• 20

  BlancoJ. L.FuchsS.ParsonsM.RibeirinhoM. J. (2018). Artificial Intelligence: Construction Technology's Next Frontier. New York, NY: McKinsey & Company. Available online at: https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/artificial-intelligence-construction-technologys-next-frontier (accessed September 23, 2018).

  • Google Scholar

• 21

  BMUB (2016). Climate Action Plan 2050: Principles and Goals of the German Government's Climate Policy. Berlin: Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB).

  • Google Scholar

• 22

  BMWi. (2018). Federal Government Adopts Artificial Intelligence Strategy. Berlin: Federal Ministry for Economic Affairs and Energy (BMWi).

  • Google Scholar

• 23

  BoatengP.ChenZ.OgunlanaS. (2017). Megaproject Risk Analysis and Simulation: A Dynamic Systems Approach. Bingley: Emerald Publishing Limited.

  • Google Scholar

• 24

  BockT. (2015). The future of construction automation: technological disruption and the upcoming ubiquity of robotics. Automat. Construct.59, 113–121. 10.1016/j.autcon.2015.07.022

  • CrossRef
  • Google Scholar

• 25

  BockT.LinnerT. (2016). Construction Robots: Elementary Technologies and Single-Task Construction Robots. Cambridge: Cambridge University Press.

  • Google Scholar

• 26

  BriesemeisterJ. K. (2018). Managing Complex Construction Projects: A Systems Approach. New York, NY: Auerbach Publications.

  • Google Scholar

• 27

  BrookesN.ChenZ.Lo StortoC. (2012). International Journal of Architecture, Engineering and Construction. Special Issue on Planning and Managing Sustainable Infrastructural Megaprojects: Call for Papers. Available online at: http://www.iasdm.org/journals/download/call-for-papers-pmsim.pdf (accessed September 23, 2018).

  • Google Scholar

• 28

  BrookesN.ChenZ.Lo StortoC. (2014). Editorial: Special issue on infrastructure megaprojects. Int. J. Architect. Eng. Construct.3:2. Available online at: http://www.iasdm.org/journals/index.php/ijaec/issue/view/10

  • Google Scholar

• 29

  BrookesN.ZhangS. (2014). Call for Papers on Megaprojects: Global Similarities and Global Differences. Available online at: http://blog.sciencenet.cn/blog-307182-823077.html (accessed September 23, 2018).

  • Google Scholar

• 30

  BrowneA. J. (2015). LAPSSET: The History and Politics of an Eastern African Megaproject. London: The Rift Valley Institute.

  • Google Scholar

• 31

  BrunnS. D. (2011). Engineering Earth: The Impacts of Megaengineering Projects. Dordrecht: Springer.

  • Google Scholar

• 32

  BSI (2006). PD 6079-4:2006 Project Management. Guide to Project Management in the Construction Industry. London: The British Standards Institution (BSI).

  • Google Scholar

• 33

  BSI (2014). BS EN 60300-1:2014 Dependability Management—Part 1: Guidance for Management and Application. London: The British Standards Institution (BSI).

  • Google Scholar

• 34

  CampbellS.De BeerJ.Van der MeulenM.MielbyS. B.Van CampenhoutI.VenvikG. (2017). COST Action TU1206 Final Achievement Report. Belgium: COST Association.

  • Google Scholar

• 35

  CardownieS. (2017). Witness Statement of Steve Cardownie. Edinburgh: The Edinburgh Tram Inquiry.

  • Google Scholar

• 36

  Castro-LacoutureD.BrysonL. S.MaynardC.Williams IIR. L.BosscherP. (2007). Concrete paving productivity improvement using a multi-task autonomous robot, in Proceedings of the 24th International Symposium on Automation and Robotics in Construction (ISARC) (Kochi), 223–228.

  • Google Scholar

• 37

  ChenQ.García de SotoB.AdeyB. T. (2018a). Construction automation: research areas, industry concerns and suggestions for advancement. Automat. Construct.94, 22–38. 10.1016/j.autcon.2018.05.028

  • CrossRef
  • Google Scholar

• 38

  ChenZ. (2007). Multi-criteria Decision-Making for the Sustainable Built Environment.Reading: University of Reading.

  • Google Scholar

• 39

  ChenZ. (2017). The principles of facilities management and case studies, in Proceedings of ARCOM and BEAM Centre Early Career Researcher and Doctoral Workshop on Building Asset Management, 2017-01-20. Glasgow: Glasgow Caledonian University.

  • Google Scholar

• 40

  ChenZ. (2018). Standards Planning and Approval: Project Management Systems. Internal document. Project no. 9019-02604. London: British Standards Institution (BSI).

  • Google Scholar

• 41

  ChenZ.AgapiouA.SmithS. (2016). Themed issue on megaproject management: call for papers. Proc. ICE Manag. Procurement Law171:2. 10.13140/RG.2.2.32373.37602

  • CrossRef
  • Google Scholar

• 42

  ChenZ.AgapiouA.SmithS. (2018b). Editorial: Themed issue on megaproject management. Proc. ICE Manag. Procurement Law171:2. 10.1680/jmapl.2018.171.2.43

  • CrossRef
  • Google Scholar

• 43

  ChenZ.ChenH.WangC. (1999). An expert system for crack diagnosis in cast-in-place reinforced concrete structures (in Chinese). Construct. Technol.28, 24–25.

  • Google Scholar

• 44

  ChenZ.LiH. (2006). Environmental Management in Construction: A Quantitative Approach. London and New York, NY: Taylor & Francis.

  • Google Scholar

• 45

  ChenZ.LiH.KongS. C. W.HongJ.XuQ. (2006a). E-commerce system simulation for construction and demolition waste exchange. Automat. Construct.15, 706–718. 10.1016/j.autcon.2005.09.003

  • CrossRef
  • Google Scholar

• 46

  ChenZ.LiH.KongS. C. W.XuQ. (2006b). An analytic knowledge network process for construction entrepreneurship education. J. Manag. Dev.25, 11–27.

  • Google Scholar

• 47

  ChenZ.MaitiS.AgapiouA. (2017). Evidence-based safety management in building refurbishment, in Proceedings of International Research Conference (11-12/09/2017) (Salford: International Council for Research and Innovation in Building and Construction (CIB) and University of Salford), 119–128.

  • Google Scholar

• 48

  ChenZ.WhiteheadC. (2015). Call for papers: themed issue on megaproject sustainability, in Proceedings of the Institution of Civil Engineers—Engineering Sustainability (London, UK).

  • Google Scholar

• 49

  ChenZ.XuW. (1996). Intelligent methods in building construction (in Chinese). Build. Construct.18, 44–47.

  • Google Scholar

• 50

  ChouJ.-S.LinC.-W.PhamA.-D.ShaoJ.-Y. (2015). Optimized artificial intelligence models for predicting project award price. Automat. Construct.54, 106–115. 10.1016/j.autcon.2015.02.006

  • CrossRef
  • Google Scholar

• 51

  CIOB (2014). Code of Practice for Project Management for Construction and Development, 5th Ed. Oxford: Wiley-Blackwell.

  • Google Scholar

• 52

  CIOB (2015). Time and Cost Management Contract Suite. Ascot: The Chartered Institute of Building (CIOB).

  • Google Scholar

• 53

  CIOB (2018a). Overseas Accredited Institutions. Ascot: The Chartered Institute of Building (CIOB).

  • Google Scholar

• 54

  CIOB (2018b). Improving Quality in the Built Environment. Ascot: The Chartered Institute of Building (CIOB).

  • Google Scholar

• 55

  CIOB (2018c). Guide to Good Practice in the Management of Time in Major Projects: Dynamic Time Modelling, 2nd Ed. Oxford: Wiley-Blackwell.

  • Google Scholar

• 56

  CIOB (2018d). Accident Investigation and Root Cause Analysis. Ascot: The Chartered Institute of Building (CIOB).

  • Google Scholar

• 57

  CIOB. (2010). CIOB's Professionalism: An Inclusive Definition of Construction Management. Ascot: Chartered Institute of Building (CIOB).

  • Google Scholar

• 58

  Clarivate (2019). Web of Science. London: Clarivate Analytics.

  • Google Scholar

• 59

  CroftC.BuckM.AdamsS. (2016). Lessons Learned from Structuring and Governance Arrangements: Perspectives at the Construction Stage of Crossrail. London: Crossrail Ltd.

  • Google Scholar

• 60

  Crossrail (2018). Crossrail Learning Legacy. London: Crossrail Ltd.

  • Google Scholar

• 61

  CSIC (2017). Annual Review 2017. Cambridge: The Cambridge Centre for Smart Infrastructure and Construction (CSIC).

  • Google Scholar

• 62

  DalkirK. (2017). Knowledge Management in Theory and Practice, 3rd Ed. Cambridge, MA: The MIT Press.

  • Google Scholar

• 63

  DaviesA.GannD.DouglasT. (2009). Innovation in megaprojects: systems integration at London Heathrow Terminal 5. California Manag. Rev.51, 101–125. 10.2307/41166482

  • CrossRef
  • Google Scholar

• 64

  De LaubierR.WunderM.WitthöftS.RothballerC. (2018). Will 3D Printing Remodel the Construction Industry?Boston, MA: The Boston Consulting Group (BCG).

  • Google Scholar

• 65

  DHS (2012). Office of Infrastructure Protection Strategic Plan: 2012–2016. Washington, DC: U.S. Department of Homeland Security (DHS).

  • Google Scholar

• 66

  DHS (2013). NIPP 2013: Partnering for Critical Infrastructure Security and Resilience. Washington, DC: U.S. Department of Homeland Security (DHS).

  • Google Scholar

• 67

  DongJ.TangL.GongW.UtiliS.CrostaG. (2018). Special Issue on Mega Engineering Projects in Challenging Geological Environments—A Modern Perspective: Call for Papers. Elsevier.

  • Google Scholar

• 68

  DOT (2018). Strategic Plan for FY 2018–2022. Washington, DC: U.S. Department of Transportation (DOT).

  • Google Scholar

• 69

  EC (2012). Strategy for the Sustainable Competitiveness of the Construction Sector and its Enterprises. Communication from the Commission to the European Parliament and the Council. Brussels: European Commission (EC).

  • Google Scholar

• 70

  Elecosoft (2018). Powerproject: The Power Behind Successful Projects. Haddenham: Elecosoft UK Ltd.

  • Google Scholar

• 71

  EMAP (2017). CN Specialists Index 2017. London: EMAP Ltd.

  • Google Scholar

• 72

  EMAP (2018). Construction Industry Company League Tables. London: EMAP Ltd.

  • Google Scholar

• 73

  ENR (2018). ENR 2018 Top 400 Contractors. New York, NY: Engineering News-Record (ENR).

  • Google Scholar

• 74

  EPA (2017). Policy on Water Infrastructure Sustainability. Washington, DC: United States Environmental Protection Agency (EPA).

  • Google Scholar

• 75

  ESRI (2002). GIS Solutions for Community Development. Redlands, CA: ESRI.

  • Google Scholar

• 76

  ESRI (2007). GIS Solutions for Surveying. Redlands, CA: ESRI.

  • Google Scholar

• 77

  ESRI (2008). GIS Solutions for Civil Engineering. Redlands, CA: ESRI.

  • Google Scholar

• 78

  ETI (2018). Edinburgh Tram Inquiry: Evidence. Edinburgh: The Edinburgh Tram Inquiry (ETI).

  • Google Scholar

• 79

  EynonJ. (2013). The Design Manager's Handbook. Chichester: John Wiley & Sons Ltd.

  • Google Scholar

• 80

  EynonJ. (2016). Construction Manager's BIM Handbook. Chichester: John Wiley and Sons Ltd.

  • Google Scholar

• 81

  FadaeeS.SchindlerS. (2017). Call for Papers on Megaprojects. Environment and Society: Advances in Research. Oxford, UK: Berghahn Books.

  • Google Scholar

• 82

  Faro (2019). BuildIT Construction. Lake Mary, FL: Faro Technologies, Inc.

  • Google Scholar

• 83

  FHWA (2017). e-Construction and Partnering: A Vision for the Future. Federal Highway Administration (FHWA), U. S. Departments of Transportation (DOT).

  • Google Scholar

• 84

  FischerM.RajagopalR.NikkhooP. (2018). Any Time Data Thinking Prototype for Construction Project Managers. Stanford, CA: Stanford Center for Integrated Facility Engineering (CIFE).

  • Google Scholar

• 85

  FlyvbjergB. (2015). Call for papers—special issue on “Classics in Megaproject Management”. Int. J. Project Manag.33, 1–2. 10.1016/j.ijproman.2014.08.002

  • CrossRef
  • Google Scholar

• 86

  FlyvbjergB. (2017). The Oxford Handbook of Megaproject Management. Oxford: Oxford University Press.

  • Google Scholar

• 87

  FlyvbjergB.BruzeliusN.RothengatterW. (2003). Megaprojects and Risk: An Anatomy of Ambition. Cambridge: Cambridge University Press.

  • Google Scholar

• 88

  FlyvbjergB.TurnerJ. R. (2018). Do classics exist in megaproject management?Int. J. Project Manag.36, 334–341. 10.1016/j.ijproman.2017.07.006

  • CrossRef
  • Google Scholar

• 89

  FrickK. T. (2016). Remaking the San Francisco–Oakland Bay Bridge: A Case of Shadowboxing with Nature. Abingdon and New York, NY: Routledge.

  • Google Scholar

• 90

  Future Cities Catapult (2017). Underground Asset Mapping in the UK. London: Urban Innovation Centre.

  • Google Scholar

• 91

  GallowayP. D.NielsenK. R.DignumJ. L. (2012). Managing Gigaprojects: Advice from Those Who've Been There, Done That. Reston, VA: American Society of Civil Engineers (ASCE).

  • Google Scholar

• 92

  García de SotoB.Agustí-JuanI.HunheviczJ.JossS.GraserK.HabertG.et al. (2018). Productivity of digital fabrication in construction: cost and time analysis of a robotically built wall. Automat. Construct.92, 297–311. 10.1016/j.autcon.2018.04.004

  • CrossRef
  • Google Scholar

• 93

  GilN.LundriganC.PintoJ. K.PuranamP. (2017). MegaProject Organization and Performance: The Myth and Political Reality. Newtown Square, PA: Project Management Institute, Inc.

  • Google Scholar

• 94

  GrahovacD.DevedzicV. (2010). COMEX: a cost management expert system. Expert Syst. Appl.37, 7684–7695. 10.1016/j.eswa.2010.04.073

  • CrossRef
  • Google Scholar

• 95

  GreimanV. A. (2013). Megaproject Management: Lessons on Risk and Project Management from the Big Dig. Hoboken, NJ: John Wiley and Sons, Inc.

  • Google Scholar

• 96

  GriffithA. (2010). Integrated Management Systems for Construction: Quality, Environment and Safety. London: Routledge.

  • Google Scholar

• 97

  GuoC.ShengZ. (2017). Infrastructure Mega-Project Management: Call for Papers. Beijing: Higher Education Press.

  • Google Scholar

• 98

  HaganG.BaltovM.BrookesN. (2012). Megaproject management: the state of the art. Organ. Technol. Manag. Construct.4, 567–569.

  • Google Scholar

• 99

  HardinB.McCoolD. (2015). BIM and Construction Management: Proven Tools, Methods, and Workflows, 2nd Ed. Indianapolis, IN: John Wiley and Sons, Inc.

  • Google Scholar

• 100

  HartL. (2015). Procuring Successful Mega-Projects: How to Establish Major Government Contracts without Ending up in Court. Surrey: Gower Publishing Ltd.

  • Google Scholar

• 101

  HartyC. (2005). Innovation in construction: a sociology of technology approach. Build. Res. Info.33, 512–522. 10.1080/0961321050028860

  • CrossRef
  • Google Scholar

• 102

  HatemD. J.CorkumD. H. (2010). Megaprojects: Challenges and Recommended Practices. Washington, DC: American Council of Engineering Companies.

  • Google Scholar

• 103

  HawksworthJ.BerrimanR.GoelS. (2018). An International Analysis of the Potential Long Term Impact of Automation. London, UK: PricewaterhouseCoopers LLP.

  • Google Scholar

• 104

  HSE (2015). Managing health and Safety in Construction: Construction (Design and Management) Regulations 2015. Bootle: Health and Safety Executive (HSE).

  • Google Scholar

• 105

  HURD (2017). The 13th Five-Year Plan for Construction Industry Development. Beijing: Ministry of Housing and Urban-Rural Development (HURD) of the People's Republic of China.

  • Google Scholar

• 106

  ICE (2019). ICE Library. London: Institution of Civil Engineers (ICE).

  • Google Scholar

• 107

  IPA (2016). Government Construction Strategy: 2016–2020. London: The Infrastructure and Projects Authority (IPA).

  • Google Scholar

• 108

  ISO (2008). ISO 22263:2008 Organization of Information about Construction Works—Framework for Management of Project Information. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 109

  ISO (2009a). ISO/TR 16738:2009 Fire-Safety Engineering—Technical Information on Methods for Evaluating Behaviour and Movement of People. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 110

  ISO (2009b). ISO 23601:2009 Safety Identification—Escape and Evacuation Plan Signs. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 111

  ISO (2011). ISO 21542:2011 Building Construction—Accessibility and Usability of the Built Environment. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 112

  ISO (2012). ISO 21500:2012 Guidance on Project Management. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 113

  ISO (2013). ISO 16354:2013 Guidelines for Knowledge Libraries and Object Libraries. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 114

  ISO (2015a). ISO 9001:2015 Quality Management Systems—Requirements. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 115

  ISO (2015b). ISO/TS 22318:2015 Societal Security—Business Continuity Management Systems—Guidelines for Supply Chain Continuity. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 116

  ISO (2017a). ISO and Construction. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 117

  ISO (2017b). ISO 15686-5:2017 Buildings and Constructed Assets—Service Life Planning—Part 5: Life-cycle Costing. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 118

  ISO (2017c). ISO 44001:2017 Collaborative Business Relationship Management Systems—Requirements and Framework. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 119

  ISO (2017d). ISO 20400:2017 Sustainable Procurement—Guidance. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 120

  ISO (2018a). Standards Catalogue: ISO/TC 59/SC 13 Organization and Digitization of Information about Buildings and Civil Engineering Works, Including Building Information Modelling (BIM). Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 121

  ISO (2018b). Standards Catalogue: ISO/TC 59/SC 18 Construction Procurement. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 122

  ISO (2018c). Standards Catalogue: ISO/TC 59/SC 14 Design life. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 123

  ISO (2018d). Standards Catalogue: ISO/TC 59/SC 17 Sustainability in Buildings and Civil Engineering Works. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 124

  ISO (2018e). Standards Catalogue: ISO/TC 267 Facility Management. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 125

  ISO (2018f). The Integrated Use of Management System Standards. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 126

  ISO (2018g). ISO 31000:2018 Risk Management—Guidelines. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 127

  ISO (2018h). ISO 22326:2018 Security and Resilience—Emergency Management—Guidelines for Monitoring Facilities with Identified Hazards. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 128

  ISO (2018i). Standards Catalogue: ISO/TC 92 Fire Safety. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 129

  ISO (2018j). Standards Catalogue: ISO/TC 195 Building Construction Machinery and Equipment. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 130

  ISO (2018k). Standards Catalogue: ISO/TC 260 Human Resource Management. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 131

  ISO (2018l). Standards Catalogue: ISO/TC 46 Information and Documentation. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 132

  ISO (2018m). Standards Catalogue: ISO/TC 207 Environmental Management. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 133

  ISO (2018n). Standards Catalogue: ISO/TC 269 Railway Applications. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 134

  ISO (2018o). Standards Catalogue: ISO/TC 301 Energy Management and Energy Savings. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 135

  ISO (2018p). Standards Catalogue: ISO/TC 258 Project, Programme and Portfolio Management. Geneva: International Organization for Standardization (ISO).

  • Google Scholar

• 136

  JiaG.NiX.ChenZ.HongB.ChenY.YangF.et al. (2013). Measuring the maturity of risk management in large-scale construction projects. Automat. Construct.34, 56–66. 10.1016/j.autcon.2012.10.015

  • CrossRef
  • Google Scholar

• 137

  JonesG. R.GeorgeJ. M. (2016). Essentials of Contemporary Management, 7th Ed. New York, NY: McGraw-Hill Education.

  • Google Scholar

• 138

  Kajima (2018). Kajima Corporate Report 2018. Tokyo: Kajima Corporation.

  • Google Scholar

• 139

  KimW. C.MauborgneR. (2004). Blue Ocean Strategy. Brighton, MA: Harvard Business Review Press.

  • Pubmed Abstract
  • Google Scholar

• 140

  KostkaG.FiedlerJ. (2016). Large Infrastructure Projects in Germany: Between Ambition and Realities. Berlin: Palgrave Macmillan, Springer.

  • Google Scholar

• 141

  Kwegyir-AffulC. (2018). Delivering Successful Megaprojects: Key Factors and Toolkit for the Project Manager. Amington: KAPM Services Limited.

  • Google Scholar

• 142

  KwonN.ParkM.LeeH.-S.AhnJ.KimS. (2017). Construction noise prediction model based on case-based reasoning in the preconstruction phase. J. Construct. Eng. Manag.143:04017008. 10.1061/(ASCE)CO.1943-7862.0001291

  • CrossRef
  • Google Scholar

• 143

  Laing O'Rourke (2017) Annual Report and Consolidated Financial Statements for the Year Ended 31 March 2017. Dartford: Laing O'Rourke.

  • Google Scholar

• 144

  LehtonenM.JolyP. B.AparicioL. (2017) Socioeconomic Evaluation of Megaprojects: Dealing with Uncertainties. London: Routledge.

  • Google Scholar

• 145

  LGA (2018). National Construction Category Strategy 2018. London: Local Government Association (LGA).

  • Google Scholar

• 146

  LiH.ChanN.HuangT.GuoH.L.LuW.SkitmoreM. (2009). Optimizing construction planning schedules by virtual prototyping enabled resource analysis. Automat. Construct.18, 912–918. 10.1016/j.autcon.2009.04.002

  • CrossRef
  • Google Scholar

• 147

  LiH.ChenZ.YongL.KongS.C.W. (2005). Application of integrated GPS and GIS technology for reducing construction waste and improving construction efficiency. Automat. Construct.14, 323–331. 10.1016/j.autcon.2004.08.007

  • CrossRef
  • Google Scholar

• 148

  LiH.GuoH. L. (2011). Editorial: Special issue on “Complexities in managing mega construction projects”. Int. J. Proj. Manag.29, 795–796.

  • Google Scholar

• 149

  LiY.LuY.CuiQ. (2016). Organizational Behaviour and Governance of Megaprojects: Call for Papers. Reston, VA: American Society of Civil Engineers (ASCE).

  • Google Scholar

• 150

  LocatelliG.GattiS.van MarrewijkA.PitsisT. S. (2017). Call for Papers: The Present and Future of Contracting and Megaprojects. London, UK: SAGE Publications.

  • Google Scholar

• 151

  LoveP. E. D.LiH. (1998). From BPR to CPR—conceptualising re-engineering in construction. Business Process Manag. J.4, 291–305. 10.1108/14637159810238192

  • CrossRef
  • Google Scholar

• 152

  LukS.WhitneyJ. (1993). Megaproject: Case Study of China's Three Gorges Project. London: M. E. Sharpe, Inc.

  • Google Scholar

• 153

  LuoH.XiongC.FangW.LoveP. E. D.ZhangB.XiO. (2018). Convolutional neural networks: computer vision-based workforce activity assessment in construction. Automat. Construct.94, 282–289. 10.1016/j.autcon.2018.06.007

  • CrossRef
  • Google Scholar

• 154

  MasciaS. (2012). Project Psychology. London: Routledge.

  • Google Scholar

• 155

  MedSolutions (2013). Why Intelligent Cost Management?Bluffton, SC: eviCore healthcare.

  • Google Scholar

• 156

  MerrowE. W. (2011). Industrial Megaprojects: Concepts, Strategies, and Practices for Success. Hoboken, NJ: John Wiley and Sons, Inc.

  • Google Scholar

• 157

  MerrowE. W.NandurdikarN. S. (2018). Leading Complex Projects: A Data-Driven Approach to Mastering the Human Side of Project Management. Hoboken, NJ: John Wiley and Sons, Inc.

  • Google Scholar

• 158

  Microsoft (2016). Reimagine Productivity with Microsoft Dynamics 365.Redmond, WA: Microsoft Corporation.

  • Google Scholar

• 159

  MorrisP. W. G. (2013). Reconstructing Project Management. Chichester: John Wiley and Sons, Inc.

  • Google Scholar

• 160

  NASEM (2007). Emerging Technologies for Construction Delivery. National Academies of Sciences, Engineering, and Medicine (NASEM). Washington, DC: The National Academies Press.

  • Google Scholar

• 161

  NBS Research (2018). Digital Transformation in Architecture. Microsoft Corporation and the Royal Institute of British Architects (RIBA).

  • Google Scholar

• 162

  NguyenL. D.ChihY.-Y.García de SotoB. (2017). Knowledge areas delivered in project management programs: exploratory study. J. Manag. Eng.33:1. 10.1061/(ASCE)ME.1943-5479.0000473

  • CrossRef
  • Google Scholar

• 163

  NorrisE.RutterJ.MedlandJ. (2013). Making the Games: What Government can Learn from London 2012. London: Institute for Government.

  • Google Scholar

• 164

  NunamakerJ.JrBriggsR.RomanoN.Jr. (2014). Collaboration Systems. New York, NY: Routledge.

  • Google Scholar

• 165

  OaklandJ.MarosszekyM. (2017). Total Construction Management. London: Routledge.

  • Google Scholar

• 166

  OchiengE.PriceA.MooreD. (2017). Major Infrastructure Projects: Planning for Delivery. London: Palgrave.

  • Google Scholar

• 167

  ODA (2011). Lessons Learned from the London 2012 Construction Project. London: Olympic Delivery Authority (ODA).

  • Google Scholar

• 168

  ODA (2015). Olympic Delivery Authority (ODA). London: Department for Culture, Media and Sport. Available online at: https://www.gov.uk/government/organisations/olympic-delivery-authority

  • Google Scholar

• 169

  ONS (2009). UK Standard Industrial Classification of Economic Activities 2007 (SIC 2007): Structure and Explanatory Notes.Hampshire: Palgrave Macmillan.

  • Google Scholar

• 170

  OsterwalderA.PigneurY.BernardaG.SmithA.PapadakosT. (2014). Value Proposition Design: How to Create Products and Services Customers Want. Hoboken, NJ: John Wiley and Sons.

  • Google Scholar

• 171

  PellicerE.YepesV.OrtegaA. J. (2013). Method for planning graduate programs in construction management. J. Profession. Iss. Eng. Edu. Pract.139:1. 10.1061/(ASCE)EI.1943-5541.0000120

  • CrossRef
  • Google Scholar

• 172

  PellsD. (2014). Call for Papers on Planning and Managing Infrastructure Megaprojects. Addison, TX: PM World Inc. Available online at: http://promaconafrica.org/index.php/2014/08/30/call-for-papers-on-managing-infrastructure-megaprojects/ (accessed February 27, 2019).

  • Google Scholar

• 173

  PlotchP. M. (2015). Politics across the Hudson: The Tappan Zee Megaproject. New Brunswick: Rutgers University Press.

  • Google Scholar

• 174

  PMI (2016). Construction Extension to the PMBOK® Guide. Newtown Square, PA: Project Management Institute (PMI).

  • Google Scholar

• 175

  PMI (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide), 6th Ed. Newtown Square, PA: Project Management Institute (PMI).

  • Google Scholar

• 176

  PricewaterhouseCoopers. (2014). Successful Capital Project Delivery: The Art and Science of Effective Governance. San Francisco, CA: PricewaterhouseCoopers LLP.

  • Google Scholar

• 177

  PriemusH.FlyvbjergB.van WeeB. (2008). Decision-making on Mega-Projects: Cost–benefit Analysis, Planning, and Innovation. Cheltenham: Edward Elgar Publishing.

  • Google Scholar

• 178

  PriemusH.van WeeB. (2013). International Handbook on Mega-Projects. Cheltenham: Edward Elgar Publishing.

  • Google Scholar

• 179

  PrykeS. (2017). Managing Networks in Project-Based Organisations. Oxford: John Wiley and Sons Ltd.

  • Google Scholar

• 180

  RafieiM. H.AdeliH. (2018). Novel machine-learning model for estimating construction costs considering economic variables and indexes. J. Construct. Eng. Manag.144:04018106. 10.1061/(ASCE)CO.1943-7862.0001570

  • CrossRef
  • Google Scholar

• 181

  RenH. (2012). Megaproject Management (in Chinese). Beijing: Science Press.

  • Google Scholar

• 182

  RIBA (2013). RIBA Plan of Work 2013 Overview. London: The Royal Institute of British Architects (RIBA).

  • Google Scholar

• 183

  RICS (2018). RICS Accredited Course. London: Royal Institution of Chartered Surveyors (RICS).

  • Google Scholar

• 184

  RiosF. C.ChongW. K.GrauD. (2015). Design for disassembly and deconstruction—challenges and opportunities. Procedia Eng.118, 1296–1304. 10.1016/j.proeng.2015.08.485

  • CrossRef
  • Google Scholar

• 185

  RossiE.StepicR. (2015). Infrastructure Project Finance and Project Bonds in Europe. Basingstoke: Palgrave Macmillan.

  • Google Scholar

• 186

  RumaneA. R. (2018). Quality Management in Construction Projects, 2nd Ed. Boca Raton, FL: CRC Press.

  • Google Scholar

• 187

  RussellS.NorvigP. (2010). Artificial Intelligence: A Modern Approach, 3rd Ed. Upper Saddle River, NJ: Pearson Education, Inc.

  • Google Scholar

• 188

  SaatyT. L. (1996). Decision making with Dependence and Feedback: The Analytic Network Process. Pittsburgh, PA: RWS Publications.

  • Google Scholar

• 189

  SantamaríaG. C. (2013). Urban Megaprojects: A Worldwide View. Bingley: Emerald Publishing Limited.

  • Google Scholar

• 190

  Scottish Parliament (2005). Building the Scottish Parliament: The Holyrood Project. Edinburgh: Scottish Parliament.

  • Google Scholar

• 191

  ShenL.ZengS.TamV. W.Y. (2017). Special edition: social responsibilities for the management of megaprojects. Int. J. Proj. Manag.35, 1336–1337. 10.1016/j.ijproman.2017.06.001

  • CrossRef
  • Google Scholar

• 192

  ShengZ. (2018). Fundamental Theories of Mega Infrastructure Construction Management: Theoretical Considerations from Chinese Practices. Cham: Springer.

  • Google Scholar

• 193

  SinghA.EbelingK. (1994). Construction process simulation using a standardized configuration and model. Proj. Manag. J.25, 45–52.

  • Google Scholar

• 194

  Skanska (2018). Annual Report 2017. Stockholm: Skanska AB.

  • Google Scholar

• 195

  SkibniewskiM. J. (1988). Framework for decision-making on implementing robotics in construction. J. Comput. Civil Eng.2:2. 10.1061/(ASCE)0887-3801(1988)2:2(188)

  • CrossRef
  • Google Scholar

• 196

  SmithD. (2018). Artificial Intelligence Enabling Machines to Learn: Experts Perspectives. London: Arup.

  • Google Scholar

• 197

  SöderlundJ.SankaranS.BiesenthalC. (2016). Call for Papers: Special Issue on Megaprojects—Symbolic and Sublime: An Organizational Theory Perspective. Newtown Square, PA: Project Management Institute, Inc.

  • Google Scholar

• 198

  SovacoolB. K.CooperC. J. (2013). The Governance of Energy Megaprojects: Politics, Hubris and Energy Security. Cheltenham and Northampton, MA: Edward Elgar Publishing.

  • Google Scholar

• 199

  StehrN.von StorchH. (2000). Eduard Brückner—The Sources and Consequences of Climate Change and Climate Variability in Historical Times. Heidelberg: Springer.

  • Google Scholar

• 200

  StermanJ. D. (1992). System Dynamics Modeling for Project Management. Cambridge, MA: Massachusetts Institute of Technology.

  • Google Scholar

• 201

  STI (2017a). Comprehensive Strategy on Science, Technology and Innovation for 2017. Tokyo: Council for Science, Technology and Innovation (STI).

  • Google Scholar

• 202

  STI (2017b). Integrated Innovation Strategy. Tokyo: Council for Science, Technology and Innovation (STI).

  • Google Scholar

• 203

  TaylorF. W. (1911). The Principles of Scientific Management. New York and London: Harper and Brothers Publishers.

  • Google Scholar

• 204

  The Press Office (2013). Presidential Policy Directive—Critical Infrastructure Security and Resilience. Washington, DC: The White House Office of the Press Secretary (The Press Office).

  • Google Scholar

• 205

  ThorpeB.SumnerP. (2004). Quality Management in Construction. Aldershot: Gower Publishing, Ltd.

  • Google Scholar

• 206

  TixierA.HallowellM. RRajagopalanB.BowmanD. (2016). Automated content analysis for construction safety: a natural language processing system to extract precursors and outcomes from unstructured injury reports. Automat. Construct.62. 45–56. 10.1016/j.autcon.2015.11.001

  • CrossRef
  • Google Scholar

• 207

  WalkerA. (2015). Project Management in Construction, 6th Ed. Chichester: John Wiley and Sons Ltd.

  • Google Scholar

• 208

  WalkerD. H. T. (2016). Reflecting on 10 years of focus on innovation, organisational learning and knowledge management literature in a construction project management context. Construct. Innovat.16, 114–126. 10.1108/CI-12-2015-0066

  • CrossRef
  • Google Scholar

• 209

  WangX.ChongH.-Y.KwakY. H. (2017). Special collection announcement: supply chain management in megaprojects. J. Manag. Eng.33:4. 10.1061/(ASCE)ME.1943-5479.0000516

  • CrossRef
  • Google Scholar

• 210

  WangZ.LiH.ZhangX. (2019). Construction waste recycling robot for nails and screws: computer vision technology and neural network approach. Automat. Construct.97, 220–228. 10.1016/j.autcon.2018.11.009

  • CrossRef
  • Google Scholar

• 211

  WaziriB. S.BalaK.BustaniS. (2017). Artificial neural networks in construction engineering and management. Int. J. Architect. Eng. Construct.6, 50–60. 10.7492/IJAEC.2017.006

  • CrossRef
  • Google Scholar

• 212

  WegrichK.KostkaG.HammerschmidG. (2017). The Governance of Infrastructure. Oxford: Oxford University Press.

  • Google Scholar

• 213

  WhyteJ.NikolićD. (2018). Virtual Reality and the Built Environment, 2nd Ed. Abingdon: Routledge.

  • Google Scholar

• 214

  WinchG. M. (2009). Managing Construction Projects, 2nd Ed. Chichester: John Wiley and Sons Ltd.

  • Google Scholar

• 215

  WomackJ. P.JonesD. T. (1996). Lean Thinking: Banish Waste and Create Wealth in Your Corporation. New York, NY: Simon and Schuster.

  • Google Scholar

• 216

  WuC.ChenD.HuangS. (2018). Complexity of Construction Mega Infrastructure Project: Call for Papers. London: Hindawi Publishing Corporation.

  • Google Scholar

• 217

  XuW.ChenZ. (1997). Intelligent Methods in Construction. Shanghai: Tongji University Press.

  • Google Scholar

• 218

  YangJ. (2017). Environmental Management in Mega Construction Projects. Singapore: Springer.

  • Google Scholar

• 219

  YeungC.L.CheungC.F.WangW.M.TsuiE. (2014). A knowledge extraction and representation system for narrative analysis in the construction industry. Expert Syst. Appl.41, 5710–5722. 10.1016/j.eswa.2014.03.044

  • CrossRef
  • Google Scholar