TY - GEN
T1 - Rapid damage assessment system of buildings after seismic events using artificial neural network
AU - Tsuchimoto, K.
AU - Spencer, B. F.
N1 - Funding Information:
The work described was substantially supported by a grant from Design Department of Taisei Corporation through a PhD fellowship to the first author; this support is gratefully acknowledged.
Publisher Copyright:
© 2019 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Safety of buildings after a seismic event should be confirmed before resume of occupation. Therefore, the condition of individual buildings need to be evaluated to minimize interference of lives and businesses. However, manual inpection by experts depend on each skill and also time consuming. Structural Health Monitoring provides a means to accelerate the required evaluation. This paper proposed to develop a cost-effective method for rapid damage assessment in buildings after seismic events. First a damage sensitive feature is defined that can distinguish damaged and undamaged structure. An artificial neural network is then explored to describe the complex relationship between the damage sensitive features and the damage index. In this paper, the maximun inter-story drift angle is proposed as a reliable damage index to classify the safety of buildings after seismic events. A five-story steel structure in which the nonlinear floor stiffness is represented by a Bouc-Wen model is developed to validate the proposed strategy. These results demonstrate the potential of the proposed framework for rapid damage assessment of buildings after seismic events.
AB - Safety of buildings after a seismic event should be confirmed before resume of occupation. Therefore, the condition of individual buildings need to be evaluated to minimize interference of lives and businesses. However, manual inpection by experts depend on each skill and also time consuming. Structural Health Monitoring provides a means to accelerate the required evaluation. This paper proposed to develop a cost-effective method for rapid damage assessment in buildings after seismic events. First a damage sensitive feature is defined that can distinguish damaged and undamaged structure. An artificial neural network is then explored to describe the complex relationship between the damage sensitive features and the damage index. In this paper, the maximun inter-story drift angle is proposed as a reliable damage index to classify the safety of buildings after seismic events. A five-story steel structure in which the nonlinear floor stiffness is represented by a Bouc-Wen model is developed to validate the proposed strategy. These results demonstrate the potential of the proposed framework for rapid damage assessment of buildings after seismic events.
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M3 - Conference contribution
AN - SCOPUS:85091442459
T3 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings
SP - 738
EP - 744
BT - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure
A2 - Chen, Genda
A2 - Alampalli, Sreenivas
PB - International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
T2 - 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019
Y2 - 4 August 2019 through 7 August 2019
ER -