Mountain railway alignment optimization considering geological impacts: A cost‐hazard bi‐objective model

Abstract

Railways are greatly threatened by geological hazards whose disastrous effects include severe economic losses as well as serious casualties. It is vital to properly account for such geological hazardous impacts during a railway alignment optimization process. However, geological factors are complex, especially in mountainous regions. Besides, economic factors are also crucial in railway alignment design. Therefore, railway alignment optimization can be termed as a cost-hazard bi-objective decision-making process. So far, least-cost railway alignment optimization has been studied quite thoroughly while the complicated geological hazard factors have received relatively little attention. In this study, a bi-objective alignment optimization model considering cost and geological hazard is developed. A novel geological railway alignment optimization model is proposed, which includes spatial geological constraints and geological hazard evaluations, after geological railway alignment design criteria are presented and analyzed for three kinds of typical geological hazards: debris flows, landslides, and rockfalls. The geological hazard evaluation includes geological susceptibility and vulnerability assessments. Then, this model is integrated with a previous least-cost alignment optimization model to construct a cost-hazard bi-objective model. The alignment searching processes are also improved to solve the proposed model by integrating geological-constraints-handling and bi-objective alignment optimization approaches. Finally, the effectiveness of the proposed method is verified by applying it to a complicated real-world case. The results show that the proposed method can produce less expensive and safer solutions than the best alignment designed by experienced human designers while satisfying all required design standards. Moreover, the method's applicability for solving actual problems is further demonstrated through the sensitivity analysis. 1 INTRODUCTION Geological hazards greatly threaten the construction and operation of railways. Their disastrous effects include not

Railway Alignment Optimization in Mountainous Regions Considering Spatial Geological Hazards: A Sustainable Safety Perspective

“…The framework of this railway alignment optimization model is shown below ( Table 1). Detailed formulations of this model can be found in our previous studies [12,13,15,23] and are not duplicated here. In this study, we mainly focus on improving the geological hazard evaluation part of this model, i.e., the geological hazard objective function.…”

“…However, in their work, geological hazard factors were not considered. Recently, Song et al (2020) [23] developed a cost-hazard bi-objective model for mountain railway alignment optimization considering three kinds of typical geohazard regions, i.e., debris flows, landslides and rockfalls. [23] mainly focused on the hazard evaluation of existing geological hazard regions.…”

“…Recently, Song et al (2020) [23] developed a cost-hazard bi-objective model for mountain railway alignment optimization considering three kinds of typical geohazard regions, i.e., debris flows, landslides and rockfalls. [23] mainly focused on the hazard evaluation of existing geological hazard regions. In fact, in complicated mountainous regions, the influencing ranges of a specific geological hazard are not constant but dynamically change, i.e., the existing geological hazard regions are very likely to further expand.…”

“…Lastly, the above model is incorporated into a previous cost-hazard alignment optimization model [23]). A real-world case of the Sichuan-Tibet railway is used to test the effectiveness of the present method.…”

Bi‐objective mountain railway alignment optimization incorporating seismic risk assessment

“…However, alignment design is known as a quite difficult and time‐consuming process due to the large‐scale study area (T. Song, Pu, Schonfeld, Li et al., 2020), complicated geographical conditions (Bosurgi, Pellegrino, & Sollazzo, 2013; T. Song, Pu, Schonfeld, Zhang et al., 2020), multiple conflicting design objectives (Yang, Kang, Schonfeld, & Jha, 2014), and many constraints (Easa & Mehmood, 2008; W. Li et al., 2019). Moreover, there exist theoretically unlimited possible alignment solutions in the study area (Hirpa, Hare, Lucet, Pushak, & Tesfamariam, 2016).…”