Abstract
Survivors trapped in void spaces formed when buildings collapse in an earthquake may be saved if search and rescue (SAR) operations are quick. A novel computational approach aims to provide building information that can guide SAR teams, thus minimizing their risk and accelerating operations. The inputs are an 'as-built' BIM model of the building before an earthquake and a partial 'as-damaged' BIM model of the exterior components after the earthquake derived from a terrestrial laser scan. A large set of possible collapse patterns is generated before the earthquake. After the event, the pattern with geometry most similar to that of the 'as-damaged' exterior BIM can be selected rapidly. This paper details the selection methods, which use least sum of point distances and Modal Assurance Criteria (MAC) algorithms, and illustrates their operation on a series of simulated computer models of collapsed structures, thus demonstrating the potential feasibility of the proposed approach.
Original language | English |
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Pages (from-to) | 65-76 |
Number of pages | 12 |
Journal | Advanced Engineering Informatics |
Volume | 30 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2016 |
Keywords
- Building Information Modeling
- Collapse
- Disasters
- Earthquakes
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Information Systems