{"title":"利用两流全局融合分类器模型在未知条件下对埋地管道中的意外事件进行早期检测和定位","authors":"Sun‐Ho Lee, Choon‐Su Park, Dong‐Jin Yoon","doi":"10.1111/mice.13507","DOIUrl":null,"url":null,"abstract":"Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference of arrival approach, which is widely used for source localization, also lacks the capability to identify anomalous events. This study introduces what is termed as the two‐stream global fusion classifier (TSGFC), a novel multitask deep‐learning model designed to early detection and location of unexpected events in buried pipelines, even under previously unseen conditions. TSGFC combines spatial and temporal features from accelerometer data using a global fusion mechanism, and uniquely performs both event classification and source localization through a unified multitask framework. To ensure generalization across diverse environments, we employed a unique data acquisition strategy that was specifically designed to evaluate the model's performance under domain shift by using training data from controlled experiments and test data from real‐world excavation activities conducted on a completely different pipeline. Our results confirm that TSGFC can identify unexpected excavation activity with 95.45% accuracy and minimal false alarms, even when evaluated on data collected from a completely different buried pipeline under real‐world excavation scenarios unseen during training.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"57 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Early detection and location of unexpected events in buried pipelines under unseen conditions using the two‐stream global fusion classifier model\",\"authors\":\"Sun‐Ho Lee, Choon‐Su Park, Dong‐Jin Yoon\",\"doi\":\"10.1111/mice.13507\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference of arrival approach, which is widely used for source localization, also lacks the capability to identify anomalous events. This study introduces what is termed as the two‐stream global fusion classifier (TSGFC), a novel multitask deep‐learning model designed to early detection and location of unexpected events in buried pipelines, even under previously unseen conditions. TSGFC combines spatial and temporal features from accelerometer data using a global fusion mechanism, and uniquely performs both event classification and source localization through a unified multitask framework. To ensure generalization across diverse environments, we employed a unique data acquisition strategy that was specifically designed to evaluate the model's performance under domain shift by using training data from controlled experiments and test data from real‐world excavation activities conducted on a completely different pipeline. Our results confirm that TSGFC can identify unexpected excavation activity with 95.45% accuracy and minimal false alarms, even when evaluated on data collected from a completely different buried pipeline under real‐world excavation scenarios unseen during training.\",\"PeriodicalId\":156,\"journal\":{\"name\":\"Computer-Aided Civil and Infrastructure Engineering\",\"volume\":\"57 1\",\"pages\":\"\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2025-05-29\",\"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.13507\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13507","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}
Early detection and location of unexpected events in buried pipelines under unseen conditions using the two‐stream global fusion classifier model
Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference of arrival approach, which is widely used for source localization, also lacks the capability to identify anomalous events. This study introduces what is termed as the two‐stream global fusion classifier (TSGFC), a novel multitask deep‐learning model designed to early detection and location of unexpected events in buried pipelines, even under previously unseen conditions. TSGFC combines spatial and temporal features from accelerometer data using a global fusion mechanism, and uniquely performs both event classification and source localization through a unified multitask framework. To ensure generalization across diverse environments, we employed a unique data acquisition strategy that was specifically designed to evaluate the model's performance under domain shift by using training data from controlled experiments and test data from real‐world excavation activities conducted on a completely different pipeline. Our results confirm that TSGFC can identify unexpected excavation activity with 95.45% accuracy and minimal false alarms, even when evaluated on data collected from a completely different buried pipeline under real‐world excavation scenarios unseen during training.
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.