{"title":"A domain adaptation methodology for enhancing the classification of structural condition states in continuously monitored historical domes","authors":"V. Cavanni, R. Ceravolo, G. Miraglia","doi":"10.1111/mice.13313","DOIUrl":null,"url":null,"abstract":"The unavailability of labeled data has always been the main limitation of data‐driven solutions for monitoring the health state of full‐scale structures. In this area, domain adaptation (DA) solutions have occasionally been proposed in recent years, which allow the sharing of data sets between distinct but similar systems. This paper presents a novel computational methodology to evaluate the condition state of historical buildings subjected to continuous monitoring. The DA method, specifically transfer component analysis, is used to maintain correlations between two data domains with low relevance, thereby improving the accuracy of classification models. Additionally, it is shown that the kernelized Bayesian transfer learning can enhance classification accuracy beyond what is achievable with a support vector machine. The paper is completed with a real‐world application to the classification of data sets from two Italian Baroque churches, both characterized by imposing oval masonry domes, but equipped with very different monitoring systems.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13313","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The unavailability of labeled data has always been the main limitation of data‐driven solutions for monitoring the health state of full‐scale structures. In this area, domain adaptation (DA) solutions have occasionally been proposed in recent years, which allow the sharing of data sets between distinct but similar systems. This paper presents a novel computational methodology to evaluate the condition state of historical buildings subjected to continuous monitoring. The DA method, specifically transfer component analysis, is used to maintain correlations between two data domains with low relevance, thereby improving the accuracy of classification models. Additionally, it is shown that the kernelized Bayesian transfer learning can enhance classification accuracy beyond what is achievable with a support vector machine. The paper is completed with a real‐world application to the classification of data sets from two Italian Baroque churches, both characterized by imposing oval masonry domes, but equipped with very different monitoring systems.
无法获得标注数据一直是数据驱动型解决方案在监测大型结构健康状况方面的主要限制因素。在这一领域,近年来偶尔会有人提出领域适应(DA)解决方案,允许不同但相似的系统共享数据集。本文介绍了一种新颖的计算方法,用于评估接受连续监测的历史建筑的状态。利用 DA 方法,特别是转移分量分析,可以保持两个相关性较低的数据域之间的相关性,从而提高分类模型的准确性。此外,研究还表明,核化贝叶斯迁移学习可以提高分类准确性,超过支持向量机的分类准确性。论文最后介绍了两个意大利巴洛克式教堂数据集分类的实际应用,这两个教堂都是气势恢宏的椭圆形砖石穹顶,但却配备了截然不同的监控系统。
期刊介绍:
Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms.
Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.