详情

原文传递 Realign Existing Railway Curves without Key Parameter Information

题名：	Realign Existing Railway Curves without Key Parameter Information
正文语种：	eng
作者：	Mengxue Yi;Yong Zeng;Zhangyue Qin;Ziyou Xia;Qing He
作者单位：	School of Civil Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China Ministry of Education Key Laboratory of High-Speed Railway Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China;School of Civil Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China Ministry of Education Key Laboratory of High-Speed Railway Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China;School of Civil Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China Ministry of Education Key Laboratory of High-Speed Railway Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China;School of Civil Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China Ministry of Education Key Laboratory of High-Speed Railway Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China;School of Civil Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China Ministry of Education Key Laboratory of High-Speed Railway Engineering Southwest Jiaotong Univ. Chengdu Sichuan 610031 China
关键词：	Railway curve realignment; Curves without key parameters information (CWI); Adaptive simplified particle swarm optimization (ASPSO); Range identification (RI); Automatic update strategy; Adaptive local random search strategy
摘要：	Railway curve realignment is critical for rectifying railway alignment deviations caused by excessive train load and repeated repairs. The existing realignment methods have limitations, such as low efficiency and precision, when considering realigning curves without key parameter information (CWI). To address the CWI issues, this study proposes a range identification and adaptive simplified particle swarm optimization (RI-ASPSO) algorithm combined with the existing principle of realigning railway curves. In this algorithm, the RI is designed to identify the range of curve parameters and is the premise of the ASPSO. Moreover, an automatic update strategy of the velocity threshold and an adaptive local random search strategy are developed in the ASPSO to efficiently and stably search the final near-optimal solution. The method is applied in real-world case studies, and the results show that the RI-ASPSO outperforms the particle swarm optimization (PSO) algorithm and coordinate method with higher accuracy, higher efficiency, and less deviation.
出版年：	2022
期刊名称：	Journal of Transportation Engineering
卷：	148
期：	8
页码：	04022048.1-04022048.11