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Machine Learning for Enhanced Seismic Damage Assessment of Buildings Kourehpaz, Pouria; Molina Hutt, Carlos
Abstract
Adopting policies for seismic risk mitigation and enhancing community resilience is conditioned on accurate seismic risk assessments. This article proposes a machine learning based framework to estimate a building’s post-earthquake damage state using ground motion intensity measures and structural properties as model inputs. The machine learning algorithms evaluated, namely, Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting are trained using a dataset of nonlinear response history analysis results from 36 detailed modern reinforced concrete shear wall building structural models, ranging from four to 24- stories, subjected to ~500 empirical and simulated ground motion records with a range of shaking intensities. The results suggest that the Gradient Boosting technique is the most efficient algorithm by achieving a prediction success (F1-score) of 87%. The proposed framework is also retrained to identify collapse instances, by leveraging synthetic data samples, considering multiple collapse thresholds. By means of synthetic data, on average, the Gradient Boosting technique increased the percentage of observed collapse cases that are correctly classified from 76% to 93%.
Item Metadata
Title |
Machine Learning for Enhanced Seismic Damage Assessment of Buildings
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Creator | |
Contributor | |
Date Issued |
2022-06
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Description |
Adopting policies for seismic risk mitigation and enhancing community resilience is conditioned on accurate seismic risk assessments.
This article proposes a machine learning based framework to estimate a building’s post-earthquake damage state using ground motion
intensity measures and structural properties as model inputs. The machine learning algorithms evaluated, namely, Logistic Regression,
K-Nearest Neighbors, Decision Tree, Random Forest, AdaBoost, and Gradient Boosting are trained using a dataset of nonlinear response
history analysis results from 36 detailed modern reinforced concrete shear wall building structural models, ranging from four to 24-
stories, subjected to ~500 empirical and simulated ground motion records with a range of shaking intensities. The results suggest that
the Gradient Boosting technique is the most efficient algorithm by achieving a prediction success (F1-score) of 87%. The proposed
framework is also retrained to identify collapse instances, by leveraging synthetic data samples, considering multiple collapse thresholds.
By means of synthetic data, on average, the Gradient Boosting technique increased the percentage of observed collapse cases that are
correctly classified from 76% to 93%.
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Genre | |
Type | |
Language |
eng
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Date Available |
2023-09-15
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0435910
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URI | |
Affiliation | |
Citation |
Kourehpaz P, Molina Hutt C. Machine Learning for Enhanced Seismic Damage Assessment of Buildings. Proceedings of the 12th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Salt Lake City, UT. 2022.
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Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty; Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International