About this Research Topic

Background

This special edition aims to present a diverse array of advanced techniques in sustainable structural engineering, showcasing innovations in design, optimization, modeling, and Structural Health Monitoring (SHM). This research topic explores innovative design approaches that integrate sustainability principles into structural engineering within the built environment. Traditionally, structural engineering has focused on optimizing performance metrics such as strength-to-weight ratios and efficiency.

However, amidst growing global concerns for sustainability, there is a pressing need to develop methodologies that not only enhance structural performance but also minimize environmental impact across the entire life cycle of buildings and infrastructure. SHM plays a crucial role in this context by providing real-time data on structural integrity, enabling proactive maintenance, and extending the lifespan of structures.

Structures in the built environment play a crucial role in global sustainability efforts, accounting for a substantial portion of energy consumption and greenhouse gas emissions. Addressing these challenges requires novel approaches that harmonize structural excellence with environmental stewardship. This research topic aims to gather contributions exploring how advanced computational tools, including machine learning, can facilitate these objectives effectively. Additionally, it will highlight the integration of SHM systems to ensure the longevity and safety of sustainable structures.

This Research Topic aims to foster discussions on the latest advancements and practical applications of advanced techniques in sustainable structural engineering, including machine learning and Structural Health Monitoring (SHM). Insights into how these innovations can be effectively applied to achieve eco-friendly built environments are intended to be provided by highlighting recent research results and practical experiences.

Contributions demonstrating the transformative potential of these techniques in enhancing both the technical performance and environmental sustainability of structures are encouraged. Advancements in Finite Element Analysis (FEA), Structural Reliability, Computational Engineering, Machine Learning, and SHM that contribute to optimizing structural designs for reduced environmental impact are sought to be showcased. The integration of sustainability principles into structural optimization methodologies, along with the proactive maintenance enabled by SHM, is considered pivotal in addressing global imperatives such as climate change and resource conservation. Successful applications and methodologies are aimed to be showcased to inspire innovative approaches that balance structural efficiency with ecological responsibility.

Researchers and practitioners are invited to contribute original research articles, reviews, and perspectives that advance the field’s understanding and application of sustainable structural engineering and SHM. Together, a future can be paved where structural designs not only meet technical demands but also contribute positively to environmental stewardship.

Authors are invited to contribute original research articles, reviews, and perspectives that advance the integration of sustainability principles, Structural Health Monitoring (SHM), and machine learning into structural design and computational engineering. Topics of interest include but are not limited to:

• Innovative Methods in Structural Design: Novel approaches in Finite Element Analysis (FEA), computational modeling, and machine learning aimed at optimizing structural designs while minimizing environmental impact.

• Design Procedures for Reuse and Recycling: Targeting steelwork/concrete reusing or recycling, as well as LCA-based optimization frameworks.

• Reliability-Based Design for Sustainability: Development and application of reliability-based design methodologies and machine learning algorithms to enhance the durability and resilience of eco-friendly structures.

• Integration of Green Materials and Technologies: Exploration of sustainable materials and construction techniques, coupled with machine learning, that contribute to energy efficiency and environmental sustainability in structural engineering.

• Structural Health Monitoring (SHM) and Sustainability: Implementation of SHM systems to provide real-time data on structural integrity, enabling proactive maintenance and extending the lifespan of structures, thereby supporting sustainability goals.

• Multi-Objective Optimization: Techniques for balancing competing objectives such as structural performance, cost-effectiveness, and environmental footprint in design optimization, leveraging machine learning.

• Case Studies and Applications: Real-world applications demonstrating successful implementation of sustainable structural optimization, SHM, and machine learning in diverse engineering projects.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Sustainable Structural Engineering, Advanced Optimization Techniques, CAD Tools, Simulation Techniques, Environmental Sustainability, Machine Learning, Structural Health Monitoring (SHM), Smart Materials, Real-time Monitoring

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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