Guest Editors
Dr. Pengwei Guo
Email: p.guo-1@tudelft.nl
Affiliation: Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft 2628 CN, Netherlands
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Research Interests: structural health monitoring, machine-learning-driven smart material design and property characterization, life-cycle assessment of concrete
Assoc. Prof. Xiao Tan
Email: xiaotan@hhu.edu.cn
Affiliation: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 211100, China
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Research Interests: fiber-optic sensor, data-driven design, life cycle assesment
Dr. Soroush Mahjoubi
Email: mahjoubi@mit.edu
Affiliation: Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, 02142, United States
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Research Interests: AI-enabled structural health monitoring, distributed fiber optic sensors, AI-guided design of sustainable materials
Summary
The urgent demand for sustainable construction has placed life cycle assessment (LCA) at the forefront of evaluating environmental performance in concrete structures. Traditional LCA approaches often rely on generalized inventory data, limited experimental datasets, and static models, which restrict their ability to capture the complexity of material interactions and structural service life. Recent advances in data-driven methods, powered by machine learning, big data analytics, and digital twins, are reshaping how LCA is applied to concrete. These approaches enable the integration of diverse data sources, ranging from laboratory experiments and field monitoring to large-scale databases, allowing for predictive modeling of carbon footprint, embodied energy, and long-term durability. This special issue aims to highlight cutting-edge research that bridges civil engineering, materials science, and artificial intelligence to advance data-driven LCA for concrete structures.
Topics include but are not limited to:
· Data-driven frameworks for LCA of concrete structures
· Development and utilization of large-scale datasets for concrete LCA
· Interpretable and explainable models for strength–sustainability trade-offs
· Integration of digital twins with LCA for real-time monitoring and prediction
· Multi-scale durability and service-life modeling linked with environmental impact
· Uncertainty quantification and sensitivity analysis in data-driven LCA
Keywords
sustainable concrete, life cycle assessment, machine learning and artificial intelligence, carbon footprint and embodied energy, strength–sustainability trade-offs
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