Machine Vision Enhanced Post Earthquake Inspection

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Sponsors:

National Science Foundation CMMI

Students:

Stephanie German, and Jong-Su Jeon

The objective of this work is to validate an automated framework for assessing the damage state and associated repair cost of inspected structures, based upon component condition information. Currently, post-earthquake inspection is performed by teams comprising licensed inspectors and/or structural engineers. Mobilizing structural inspectors and assessing the safety of buildings after earthquakes takes many weeks or months to complete. During that time, the affected population is homeless and/or jobless and the impact on the area’s economy is devastating. The critical role of post-earthquake inspection in hazard mitigation and the need for its fast performance in earthquake damaged areas has prompted several efforts towards automating structural safety assessment which have led to the creation of sensing-based evaluation methods.