Effect of data drift on the performance of machine-learning models: Seismic damage prediction for aging bridges

IF 4.3 2区 工程技术 Q1 ENGINEERING, CIVIL Earthquake Engineering & Structural Dynamics Pub Date : 2024-09-06 DOI:10.1002/eqe.4230
Mengdie Chen, Yewon Park, Sujith Mangalathu, Jong-Su Jeon
{"title":"Effect of data drift on the performance of machine-learning models: Seismic damage prediction for aging bridges","authors":"Mengdie Chen,&nbsp;Yewon Park,&nbsp;Sujith Mangalathu,&nbsp;Jong-Su Jeon","doi":"10.1002/eqe.4230","DOIUrl":null,"url":null,"abstract":"<p>Machine-learning models play a crucial role in structural seismic risk assessment and facilitate decision-making by analyzing complex data patterns. However, the dynamic nature of real-world data introduces challenges, particularly data drift, which can significantly affect model performance. This adversely affects machine-learning models intended to aid emergency responders and disaster recovery teams. This study primarily focused on assessing the impact of column corrosion-induced data drift on the performance of machine-learning models for seismic risk assessment of bridges. The machine-learning model performance was evaluated with and without considering the impact of corrosion. The results revealed a significant decrease in prediction accuracy when the data drift effect was not considered. To address this challenge, this study proposes integrating principal component analysis-based anomaly detection to enhance the model performance. The optimized model considering drift demonstrates significant improvements in accuracy across corroded bridges aged 25, 50, and 75 years, with accuracy rates increasing from 90%, 85%, and 81% to 98%, 97%, and 96%, respectively.</p>","PeriodicalId":11390,"journal":{"name":"Earthquake Engineering & Structural Dynamics","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eqe.4230","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earthquake Engineering & Structural Dynamics","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/eqe.4230","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

Abstract

Machine-learning models play a crucial role in structural seismic risk assessment and facilitate decision-making by analyzing complex data patterns. However, the dynamic nature of real-world data introduces challenges, particularly data drift, which can significantly affect model performance. This adversely affects machine-learning models intended to aid emergency responders and disaster recovery teams. This study primarily focused on assessing the impact of column corrosion-induced data drift on the performance of machine-learning models for seismic risk assessment of bridges. The machine-learning model performance was evaluated with and without considering the impact of corrosion. The results revealed a significant decrease in prediction accuracy when the data drift effect was not considered. To address this challenge, this study proposes integrating principal component analysis-based anomaly detection to enhance the model performance. The optimized model considering drift demonstrates significant improvements in accuracy across corroded bridges aged 25, 50, and 75 years, with accuracy rates increasing from 90%, 85%, and 81% to 98%, 97%, and 96%, respectively.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
数据漂移对机器学习模型性能的影响:老化桥梁的地震损坏预测
机器学习模型在结构性地震风险评估中发挥着至关重要的作用,通过分析复杂的数据模式促进决策制定。然而,真实世界数据的动态性质带来了挑战,尤其是数据漂移,会严重影响模型性能。这对旨在帮助应急响应人员和灾难恢复团队的机器学习模型产生了不利影响。本研究主要侧重于评估柱腐蚀引起的数据漂移对用于桥梁地震风险评估的机器学习模型性能的影响。在考虑和不考虑腐蚀影响的情况下,对机器学习模型的性能进行了评估。结果表明,在不考虑数据漂移影响的情况下,预测准确率会明显下降。为应对这一挑战,本研究提出整合基于主成分分析的异常检测,以提高模型性能。考虑了漂移的优化模型在 25 年、50 年和 75 年的腐蚀桥梁上的准确性有了显著提高,准确率分别从 90%、85% 和 81% 提高到 98%、97% 和 96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Earthquake Engineering & Structural Dynamics
Earthquake Engineering & Structural Dynamics 工程技术-工程:地质
CiteScore
7.20
自引率
13.30%
发文量
180
审稿时长
4.8 months
期刊介绍: Earthquake Engineering and Structural Dynamics provides a forum for the publication of papers on several aspects of engineering related to earthquakes. The problems in this field, and their solutions, are international in character and require knowledge of several traditional disciplines; the Journal will reflect this. Papers that may be relevant but do not emphasize earthquake engineering and related structural dynamics are not suitable for the Journal. Relevant topics include the following: ground motions for analysis and design geotechnical earthquake engineering probabilistic and deterministic methods of dynamic analysis experimental behaviour of structures seismic protective systems system identification risk assessment seismic code requirements methods for earthquake-resistant design and retrofit of structures.
期刊最新文献
Issue information Issue information SSI-induced seismic earth pressures on an integral abutment bridge model: Experimental measurements versus numerical simulations and code provisions Estimation of inelastic displacement ratio spectrum for existing RC structures via displacement response spectrum Linear equivalence for motion amplification devices in earthquake engineering
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1