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Decision tree–based seismic damage prediction for reinforcement concrete frame buildings considering structural micro-characteristics

Decision tree–based seismic damage prediction for reinforcement concrete frame buildings... A decision tree–based seismic vulnerability method for reinforcement concrete frames is proposed. Structures with stories equal to 3, 6, 9, and 12 were considered herein and 45,360 (4 × 11,340) reinforcement concrete frame damage samples with different micro-characteristic values were simulated using the capacity spectrum method. Afterward, with the adoption of CART algorithm, a decision tree was derived to visualize the relationship between the structural characteristics and damage states according to training samples. Damage prediction can then be made for unseen structures according to their characteristic values directly using the configured decision trees. Ten training and testing sets were established randomly from the sample library and their seismic vulnerabilities under three earthquake intensity levels were assessed to verify the proposed method. The results show that the decision tree predictor is efficient for seismic vulnerability assessment of reinforcement concrete frames, and the predictor shows high prediction accuracy and stability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Structural Engineering SAGE

Decision tree–based seismic damage prediction for reinforcement concrete frame buildings considering structural micro-characteristics

Advances in Structural Engineering , Volume 22 (9): 13 – Jul 1, 2019

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References (24)

Publisher
SAGE
Copyright
© The Author(s) 2019
ISSN
1369-4332
eISSN
2048-4011
DOI
10.1177/1369433219832508
Publisher site
See Article on Publisher Site

Abstract

A decision tree–based seismic vulnerability method for reinforcement concrete frames is proposed. Structures with stories equal to 3, 6, 9, and 12 were considered herein and 45,360 (4 × 11,340) reinforcement concrete frame damage samples with different micro-characteristic values were simulated using the capacity spectrum method. Afterward, with the adoption of CART algorithm, a decision tree was derived to visualize the relationship between the structural characteristics and damage states according to training samples. Damage prediction can then be made for unseen structures according to their characteristic values directly using the configured decision trees. Ten training and testing sets were established randomly from the sample library and their seismic vulnerabilities under three earthquake intensity levels were assessed to verify the proposed method. The results show that the decision tree predictor is efficient for seismic vulnerability assessment of reinforcement concrete frames, and the predictor shows high prediction accuracy and stability.

Journal

Advances in Structural EngineeringSAGE

Published: Jul 1, 2019

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