Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Soft Computing Pub Date : 2024-10-31 DOI:10.1016/j.asoc.2024.112421
Hao Pu , Ting Hu , Taoran Song , Paul Schonfeld , Wei Li , Lihui Peng
{"title":"Knowledge graph-driven mountain railway alignment optimization integrating karst hazard assessment","authors":"Hao Pu ,&nbsp;Ting Hu ,&nbsp;Taoran Song ,&nbsp;Paul Schonfeld ,&nbsp;Wei Li ,&nbsp;Lihui Peng","doi":"10.1016/j.asoc.2024.112421","DOIUrl":null,"url":null,"abstract":"<div><div>Karst hazard is a considerable threat that should be considered in railway alignment design for mountainous regions with dense water systems. Nevertheless, alignment design principles in karst regions have not been systematically studied. Moreover, a quantitative karst hazard assessment model is currently lacking for automated alignment optimization. To solve the above problems, based on the analyses of karst inducing factors and hazard representation, the railway alignment design principles in karst regions are summarized through an event tree. A highly-coupled knowledge graph (called KaRAD-KG) modeling method is proposed. Then, a bi-objective alignment optimization model considering railway construction cost and karst hazard (mainly including hazard components of synclinal karst, anticlinal karst and karst depression) is constructed. To solve the optimization model, a knowledge-driven distance transform algorithm incorporating a karst hazard assessment method and a multicriteria tournament decision method is customized. Finally, the application in a real-world case indicates that the proposed method can generate an alignment which reduces construction cost by 3.39 % and karst hazard by 18.73 % compared to the best manually-designed alternative, which verifies the effectiveness of this method for assisting actual railway alignment design in a karst-dense mountainous region.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112421"},"PeriodicalIF":7.2000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494624011955","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Karst hazard is a considerable threat that should be considered in railway alignment design for mountainous regions with dense water systems. Nevertheless, alignment design principles in karst regions have not been systematically studied. Moreover, a quantitative karst hazard assessment model is currently lacking for automated alignment optimization. To solve the above problems, based on the analyses of karst inducing factors and hazard representation, the railway alignment design principles in karst regions are summarized through an event tree. A highly-coupled knowledge graph (called KaRAD-KG) modeling method is proposed. Then, a bi-objective alignment optimization model considering railway construction cost and karst hazard (mainly including hazard components of synclinal karst, anticlinal karst and karst depression) is constructed. To solve the optimization model, a knowledge-driven distance transform algorithm incorporating a karst hazard assessment method and a multicriteria tournament decision method is customized. Finally, the application in a real-world case indicates that the proposed method can generate an alignment which reduces construction cost by 3.39 % and karst hazard by 18.73 % compared to the best manually-designed alternative, which verifies the effectiveness of this method for assisting actual railway alignment design in a karst-dense mountainous region.
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
结合岩溶危害评估的知识图谱驱动山区铁路线路优化
岩溶危害是一个相当大的威胁,在水系密集的山区进行铁路线路设计时应加以考虑。然而,岩溶地区的线路设计原则尚未得到系统研究。此外,目前还缺乏用于自动线路优化的岩溶灾害定量评估模型。为了解决上述问题,在分析岩溶诱发因素和危害表征的基础上,通过事件树总结了岩溶地区的铁路选线设计原则。提出了一种高度耦合的知识图谱(称为 KaRAD-KG)建模方法。然后,构建了一个考虑铁路建设成本和岩溶危害(主要包括同向岩溶、反向岩溶和岩溶洼地的危害成分)的双目标选线优化模型。为求解该优化模型,定制了一种知识驱动的距离变换算法,该算法结合了岩溶危害评估方法和多标准锦标赛决策方法。最后,在一个实际案例中的应用表明,与人工设计的最佳替代方案相比,所提出的方法可以生成一条建设成本降低 3.39 %、岩溶危害降低 18.73 % 的线路,从而验证了该方法在岩溶密集山区实际铁路线路设计中的有效性。
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
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
期刊最新文献
Optimized hybrid XGBoost-CatBoost model for enhanced prediction of concrete strength and reliability analysis using Monte Carlo simulations A Z-number-based three-way decision method with classification-based state determination for the evaluation of new energy enterprises Deep supervision network with contrastive learning for zero-shot sketch-based image retrieval Adaptive deep learning models for efficient multivariate anomaly detection in IoT infrastructures A robust rank aggregation method for malicious disturbance based on objective credit
×
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