About this Research Topic
Managing existing civil infrastructure is challenging due to evolving functional requirements, material aging, climate change, and code adjustments reflecting aspects such as loading modifications. With economic, environmental and material resources becoming increasingly scarce, more sustainable solutions for practical asset management are required. Conservative approaches and assumptions in construction design and practice often result in hidden reserve structural capacity of infrastructure assets. Interpreting monitoring data has the potential to unlock this untapped capacity, thus improving decision-making without putting users at risk. For example, better knowledge of the structural performance through monitoring may be leveraged to extend service durations, optimize structural rehabilitation, focus inspection and prioritize maintenance activities.
Given the ever-growing set of tools available to engineers to measure and inspect structures, such as local and distributed sensing, more precise quantification of the performance of existing structures is possible. Thus, data-based reasoning enables risk-informed decisions for optimal management of existing infrastructure systems and networks, reducing significantly the inherent costs and environmental impacts.
This research topic focuses on data-interpretation methods for infrastructure management. Contributions are invited to propose novel strategies to use field measurements of existing structures for an informed decision-making at both infrastructure and network scales, enabling better asset management. Sensor data may help engineers to reduce uncertainties in multi-criteria approaches for decision-making that include, in addition to structural performance and risk, sustainability and resilience goals. Contributions that involve full-scale field measurements are encouraged. Topics of interest include but are not limited to the following:
• Sustainable and resilient asset management using multi-criteria approaches
• Data-interpretation methods for structural identification
• Full-scale monitoring applications
• Measurement-system-design strategies
• Digital twins of complex civil systems
• Data fusion for efficient infrastructure management
• Risk-informed infrastructure management
• Performance-based rehabilitation planning
• Novel structural-sensing technologies
Given the ever-growing set of tools available to engineers to measure and inspect structures, such as local and distributed sensing, more precise quantification of the performance of existing structures is possible. Thus, data-based reasoning enables risk-informed decisions for optimal management of existing infrastructure systems and networks, reducing significantly the inherent costs and environmental impacts.
This research topic focuses on data-interpretation methods for infrastructure management. Contributions are invited to propose novel strategies to use field measurements of existing structures for an informed decision-making at both infrastructure and network scales, enabling better asset management. Sensor data may help engineers to reduce uncertainties in multi-criteria approaches for decision-making that include, in addition to structural performance and risk, sustainability and resilience goals. Contributions that involve full-scale field measurements are encouraged. Topics of interest include but are not limited to the following:
• Sustainable and resilient asset management using multi-criteria approaches
• Data-interpretation methods for structural identification
• Full-scale monitoring applications
• Measurement-system-design strategies
• Digital twins of complex civil systems
• Data fusion for efficient infrastructure management
• Risk-informed infrastructure management
• Performance-based rehabilitation planning
• Novel structural-sensing technologies
Keywords: Structural sensing, Infrastructure system management, Optimal sensor placement, Data-driven decision making, Performance-based asset management
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.
