A Machine Learning-Based Safety Assessment Framework for Roadway Construction Projects in Flood-Prone Regions
Author:
Affiliation:
1. Ph.D. Student, Dept. of Construction Management, Louisiana State Univ., Baton Rouge, LA.
2. Associate Professor, Dept. of Construction Management, Louisiana State Univ., Baton Rouge, LA.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784485248.051
Reference12 articles.
1. Chavez, M. S. (2016). Hurricane Preparedness in a Construction Site: a Framework to Assess the Construction Companies’ Current Practices [Florida International University]. https://doi.org/10.25148/etd.FIDC001187.
2. Ontology-based semantic modelling to support knowledge-based document classification on disaster-resilient construction practices;Dhakal S.;International Journal of Construction Management,2020
3. Ham Y. Lee S. J. and Chowdhury A. G. (2017). Imaging-to-Simulation Framework for Improving Disaster Preparedness of Construction Projects and Neighboring Communities. Congress on Computing in Civil Engineering Proceedings 2017-June(Fermino 2013) 230–237. https://doi.org/10.1061/9780784480830.029.
4. Investigations of plate-type windborne debris—Part II: Computed trajectories
5. AI-based risk assessment for construction site disaster preparedness through deep learning-based digital twinning;Kamari M.;Automation in Construction,2022
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