{"title":"基于非线性树的回归集合模型用于震损 RC 桥梁的修复成本预测","authors":"","doi":"10.1016/j.soildyn.2024.108947","DOIUrl":null,"url":null,"abstract":"<div><p>Data driven models are useful in various classification and regression problems in bridge engineering. Although machine learning applications in identifying the performance and characteristics of bridges has been increasing, repair cost modeling using real earthquake damage data is only sparsely reported in the literature. This study assesses various machine learning models for repair cost estimation of reinforced concrete (RC) bridges damaged by the 2015 Gorkha earthquake in Nepal. Ensemble learning interpretation is used to hierarchize bridge attributes based on their relative importance in repair cost prediction. The impact of individual features is also assessed using various combinations of bridge features. The results indicate that two categorical features, discrete damage state and foundation type, are the most important in predicting repair cost. Of the various models tested, extra trees (ET) ensemble outperformed any other ensemble as well as base learner methods. The findings indicate that, for the case-study data used here, repair cost is better estimated by a classification model for damage state in series with a regression model than just a regression model.</p></div>","PeriodicalId":49502,"journal":{"name":"Soil Dynamics and Earthquake Engineering","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonlinear tree based regression ensemble modeling for repair cost prediction in earthquake damaged RC bridges\",\"authors\":\"\",\"doi\":\"10.1016/j.soildyn.2024.108947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Data driven models are useful in various classification and regression problems in bridge engineering. Although machine learning applications in identifying the performance and characteristics of bridges has been increasing, repair cost modeling using real earthquake damage data is only sparsely reported in the literature. This study assesses various machine learning models for repair cost estimation of reinforced concrete (RC) bridges damaged by the 2015 Gorkha earthquake in Nepal. Ensemble learning interpretation is used to hierarchize bridge attributes based on their relative importance in repair cost prediction. The impact of individual features is also assessed using various combinations of bridge features. The results indicate that two categorical features, discrete damage state and foundation type, are the most important in predicting repair cost. Of the various models tested, extra trees (ET) ensemble outperformed any other ensemble as well as base learner methods. The findings indicate that, for the case-study data used here, repair cost is better estimated by a classification model for damage state in series with a regression model than just a regression model.</p></div>\",\"PeriodicalId\":49502,\"journal\":{\"name\":\"Soil Dynamics and Earthquake Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soil Dynamics and Earthquake Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0267726124004998\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil Dynamics and Earthquake Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0267726124004998","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
Nonlinear tree based regression ensemble modeling for repair cost prediction in earthquake damaged RC bridges
Data driven models are useful in various classification and regression problems in bridge engineering. Although machine learning applications in identifying the performance and characteristics of bridges has been increasing, repair cost modeling using real earthquake damage data is only sparsely reported in the literature. This study assesses various machine learning models for repair cost estimation of reinforced concrete (RC) bridges damaged by the 2015 Gorkha earthquake in Nepal. Ensemble learning interpretation is used to hierarchize bridge attributes based on their relative importance in repair cost prediction. The impact of individual features is also assessed using various combinations of bridge features. The results indicate that two categorical features, discrete damage state and foundation type, are the most important in predicting repair cost. Of the various models tested, extra trees (ET) ensemble outperformed any other ensemble as well as base learner methods. The findings indicate that, for the case-study data used here, repair cost is better estimated by a classification model for damage state in series with a regression model than just a regression model.
期刊介绍:
The journal aims to encourage and enhance the role of mechanics and other disciplines as they relate to earthquake engineering by providing opportunities for the publication of the work of applied mathematicians, engineers and other applied scientists involved in solving problems closely related to the field of earthquake engineering and geotechnical earthquake engineering.
Emphasis is placed on new concepts and techniques, but case histories will also be published if they enhance the presentation and understanding of new technical concepts.