Affiliations: [a] School of Civil Engineering, Central South University, Changsha, Hunan, China | [b] National Engineering Research Center of High-speed Railway Construction Technology, Changsha, Hunan, China | [c] Department of Civil Engineering, University of British Columbia, Vancouver, Canada | [d] Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA | [e] China Railway Eryuan Engineering Group Co. Ltd, Chengdu, Sichuan, China
Correspondence:
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Corresponding authors: Hao Pu and Taoran Song, School of Civil Engineering, Central South University, Changsha, Hunan 410075, China. E-mail: [email protected]@csu.edu.cn.
Abstract: Railway alignment design is a complicated problem affected by intricate environmental factors. Although numerous alignment optimization methods have been proposed, a general limitation among them is the lack of a spatial environmental suitability analysis to guide the subsequent alignment search. Consequently, many unfavorable regions in the study area are still searched, which significantly degrades optimization efficiency. To solve this problem, a geographic information model is proposed for evaluating the environmental suitability of railways. Initially, the study area is abstracted as a spatial voxel set and the 3-D reachable ranges of railways are determined. Then, a geographic information model is devised which considers topographic influencing factors (including those affecting structural cost and stability) as well as geologic influencing factors (including landslides and seismic impacts) for different railway structures. Afterward, a 3-D environmental suitability map can be generated using a multi-criteria decision-making approach to combine the considered factors. The map is further integrated into the alignment optimization process based on a 3-D distance transform algorithm. The proposed model and method are applied to two complex realistic railway cases. The results demonstrate that they can considerably improve the search efficiency and also find better alignments compared to the best alternatives obtained manually by experienced human designers and produced by a previous distance transform algorithm as well as a genetic algorithm.
