Collaborative Research: Machine Vision Enhanced Post Earthquake Inspection and Rapid Loss Estimation

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The objective of this project is to develop and validate a computational framework for post-earthquake inspection of reinforced concrete (RC) frame buildings that will enable rapid, automated assessment of the damage state of the structure and of the cost and time required to repair the structure. The proposed automated procedure will start with collection of video frames using a high-resolution video camera mounted on an inspector?s hardhat. Then, state-of-the-art detection and extraction algorithms will be employed to detect RC columns and identify and characterize column damage. Component damage will be classified using empirically based models, and component damage will be used to determine the damage state of the building. Building damage state, configuration and type will be used to query a set of fragility curves defining the likelihood of building collapse during an aftershock and, thereby, provide an improved understanding of risk. Deliverables include a catalog of fundamental visible damage characteristics, model-based recognition and analysis tools, demonstration and validation via hardware, documentation of research results, engineering student education, and outreach seminars to building evaluators.If successful, the results of this research will provide the first robust method in the area of structural member and damage recognition from video. This scientific breakthrough will allow researchers to integrate this work in as-built building information modeling, project monitoring, virtual and augmented reality and other applications of importance to the engineering community. Also, this will be the first known study to quantitatively link visual damage in a building component (column or wall), to the likelihood of building collapse using robust probabilistic methods. The discoveries sought in this project are expected to serve as a foundation for a new knowledge base in damage assessment and to promote intellectual cross-pollination among the fields of computer vision and structural engineering. The results will be disseminated to allow the creation of commercial software that have increased precision, reduced cost, and work with reduced weight devices. Graduate and undergraduate engineering students and K-12 students will benefit through classroom instruction and involvement in the research.