Automating Postearthquake Recovery in Construction: Leveraging BIM, Deep Learning, and Web Map Services for Efficient Solutions
Journal
Journal of Computing in Civil Engineering
Journal Volume
39
Journal Issue
3
ISSN
0887-3801
1943-5487
Date Issued
2025-03-01
Author(s)
Abstract
In response to the increasing complexity encountered in postearthquake recovery within the construction sector, there is a pressing need for more accurate and efficient methods. Leveraging emerging technologies to automate processes is a feasible approach to tackle these challenges effectively. This study focuses on integrating building information modeling (BIM), deep learning (DL), and web map services (WMS) to accelerate and improve the recovery planning for earthquake-damaged buildings. This integrated approach not only supports sustainable construction goals but also has the potential to reduce planning time by 10%-15%, depending on project situations, while decreasing the resources required. The research used a BIM-based platform, applying a vision transformer for image classification and Detectron2 for instance segmentation, to accurately identify damage in structural elements and streamline damage evaluation process through automation. Implementing a BIM-and WMS-based plugin further enhances this process, enabling automated and data-driven recovery planning incorporating sustainability considerations. A case study demonstrated this integrated BIM-DL-WMS approach on an actual recovery project. The result underlines the potential of these technologies to revolutionize postearthquake recovery efforts in the construction industry. They make the recovery process more efficient, accurate, and sustainable. © 2025 American Society of Civil Engineers.
Subjects
Building information modeling
Data-driven recovery planning
Deep learning
Postearthquake recovery
Sustainable construction
Web map service
Publisher
American Society of Civil Engineers (ASCE)
Type
journal article