Open AccessMulti-objective cost optimization for geological risk oriented rail transit route selection
Author(s) -
Yan Gao,
Wu Zhiyi,
Yu Junyuan,
Quan Yuan,
Wenlong Li
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/570/2/022019
Subject(s) - particle swarm optimization , selection (genetic algorithm) , urban rail transit , duration (music) , heuristic , transport engineering , computer science , ant colony optimization algorithms , operations research , risk analysis (engineering) , engineering , business , algorithm , art , literature , artificial intelligence
China’s rail transit has entered a period of rapid development, but related safety and risk management fail to match with high-speed construction. The complex and uncertain underground space leads to frequent risk accidents of urban rail transit. As the primary task of rail transit development, the reasonable route selection can not only avoid a lot of geological risks, but also greatly save the duration and cost. In order to guide the rail transit route selection work scientifically and reasonably, the cost problems related to geological risk factors by constructing the objective function of risk assessment, section construction cost and duration are analyzed in this study. Combining with heuristic algorithms such as ant algorithm and particle swarm optimization algorithm, an evaluation method of cost optimization of rail transit route selection for the geological risk are put forward. Taking the geological data of a certain section of a proposed rail transit route in Guangzhou as an example, based on this proposed method, the optimal route selection scheme with relatively low risk, short construction duration and low cost is found, which provides theoretical basis and guidance for the route selection and construction of rail transit, and also verifies the rationality of the application of the cost optimization method for the geological risk to multi-objective engineering management.