{"title":"A smoothness control method for kilometer‐span railway bridges with analysis of track characteristics","authors":"Yuxiao Zhang, Jin Shi, Shehui Tan, Yingjie Wang","doi":"10.1111/mice.13215","DOIUrl":null,"url":null,"abstract":"Significant dynamic deformations during the operation of kilometer‐span high‐speed railway bridges adversely affect track maintenance. This paper proposes a three‐stage smoothness control method based on a comprehensive analysis of track alignment characteristics to address this issue. In the method, historical measured data are grouped into multicategories, and reference alignments for each category are reconstructed. Then, the reference alignment category to which the track to be adjusted belongs is accurately matched. Finally, a novel smoothness optimization algorithm is designed to use the 60 m chord as the optimization unit, and the 10 m and 30 m combined chords within the unit constrain the midchord offset and vector distance difference. The proposed method was applied to formulate the maintenance scheme for the Shanghai–Suzhou–Nantong Yangtze River Bridge. The result indicates that the track smoothness improved by more than 79.7%, and the high‐speed train operational performance improved by over 64.3%, effectively enhancing the maintenance quality.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-04-30","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.13215","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
Significant dynamic deformations during the operation of kilometer‐span high‐speed railway bridges adversely affect track maintenance. This paper proposes a three‐stage smoothness control method based on a comprehensive analysis of track alignment characteristics to address this issue. In the method, historical measured data are grouped into multicategories, and reference alignments for each category are reconstructed. Then, the reference alignment category to which the track to be adjusted belongs is accurately matched. Finally, a novel smoothness optimization algorithm is designed to use the 60 m chord as the optimization unit, and the 10 m and 30 m combined chords within the unit constrain the midchord offset and vector distance difference. The proposed method was applied to formulate the maintenance scheme for the Shanghai–Suzhou–Nantong Yangtze River Bridge. The result indicates that the track smoothness improved by more than 79.7%, and the high‐speed train operational performance improved by over 64.3%, effectively enhancing the maintenance quality.
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.