Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-07
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en
Pages
16
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Sensors, Volume 24, issue 13
Abstract
This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies.Description
Publisher Copyright: © 2024 by the authors.
Keywords
machine learning in SHM, prediction interval, QD-LUBE, uncertainty quantification in SHM
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Citation
Shabbir, K, Umair, M, Sim, S H, Ali, U & Noureldin, M 2024, ' Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning ', Sensors, vol. 24, no. 13, 4218 . https://doi.org/10.3390/s24134218