Advances in Data-Driven Prediction and Life Cycle Assessment for Infrastructure Materials
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".
Deadline for manuscript submissions: 30 April 2027 | Viewed by 217
Special Issue Editors
Interests: explainable AI; intelligent construction and operation; information fusion; infrastructure monitoring; nondestructive testing; fiber optic sensors; life cycle assessment
Special Issues, Collections and Topics in MDPI journals
Interests: AI-enhanced acoustic diagnosis; structural health monitoring; intelligent operation and maintenance of infrastructure
Special Issues, Collections and Topics in MDPI journals
Interests: AI-guided design of sustainable materials; AI-enabled structural health monitoring; distributed fiber optic sensors
Interests: artificial intelligence; computer vision; structural health monitoring; digital twins; life cycle assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The urgent demand for sustainable construction has placed data-driven prediction and life cycle assessment (LCA) at the forefront of evaluating environmental performance in infrastructure materials. 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 infrastructure materials. 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 infrastructure materials.
Topics include, but are not limited to, the following:
- Data-driven frameworks for LCA of infrastructure materials
- Development and utilization of large-scale datasets for infrastructure materials LCA
- Interpretable AI models for strength–sustainability trade-offs of infrastructure materials
- Knowledge-guided reasoning for infrastructure materials design
- 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
We look forward to receiving your contributions.
Dr. Xiao Tan
Dr. Dan Li
Dr. Soroush Mahjoubi
Dr. Pengwei Guo
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- sustainable concrete
- life cycle assessment
- machine learning and artificial intelligence
- carbon footprint and embodied energy
- strength–sustainability trade-offs
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