Robust Optimization Method for Mountain Railway Alignments Considering Preference Uncertainty for Costs and Seismic Risks

Pu, Hao; Schonfeld, Paul; Hu, Jianping; Liu, Jiangtao

doi:10.1061/ajrua6.0001207

Cited by 10 publications

(5 citation statements)

References 54 publications

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“…(2021) devised a deep learning approach for reducing costs in mountain railway AO. Song, Pu, Schonfeld, Hu, and Liu (2022) and Song, Pu, Schonfeld, and Hu (2022) constructed robust optimization models to quantify several uncertain factors involved in 3‐D AOs.…”

Section: Introductionmentioning

confidence: 99%

“…The model was solved by a PSO algorithm with specifically equipped constraint‐handling operators and bi‐objective solvers. Then, in Song, Pu, Schonfeld, Hu, and Liu (2022), a robust bi‐objective optimization method was further developed to solve the model.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the previous method may underestimate seismic losses. Besides, the seismic repair cost and time were estimated in (2021) and Song, Pu, Schonfeld, Hu, and Liu (2022) with some simplified deterministic functions considering limited uncertainties. Those estimates can be improved by combining probabilistic seismic hazard, fragility, and loss analyses.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing net present values of risk avoidance for mountain railway alignments with seismic performance evaluation

Song,

Pu,

Yang

et al. 2023

Computer aided Civil Eng

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Railway alignment optimization in earthquake‐prone mountainous (EPM) regions should quantify and trade off construction investments and seismic risks. Unfortunately, slight attention has been previously devoted to this trade‐off. To this end, based on the FEMA‐P58 methodology, a net present value (NPV) model of risk avoidance is presented and solved. In the model, alignment alternatives are first segmented into structural groups with different probabilistic seismic fragility curves, which are then used to generate structural repair cost and repair time curves. Afterward, a probabilistic seismic hazard curve is introduced to estimate the expected annual repair cost and time for computing railway direct and indirect seismic losses. Hence, the railway total annual loss caused by seismic activity can be obtained. Next, a benefit–cost analysis is performed to combine construction cost and seismic loss as the risk‐cost NPV. To optimize this objective function, a particle swarm algorithm is used as the basic approach. For implementing the probabilistic seismic performance analysis, a Monte Carlo simulation (MCS) is employed as the risk assessment module. Furthermore, due to the computationally intensive nature of MCS, a CPU‐based parallelization is embedded into the algorithm to expedite the search. Finally, the proposed model and method are applied to a representative real‐world railway case in an EPM region. Their effectiveness is discussed and verified in five experiments, including algorithm convergence analysis, alignment solution comparison, seismic risk interpretation, computational efficiency test, and a specific sensitivity analysis.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing net present values of risk avoidance for mountain railway alignments with seismic performance evaluation

Song,

Pu,

Yang

et al. 2023

Computer aided Civil Eng

Self Cite

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“…With the major aims of locating linear infrastructures (Davey et al, 2017;Kim et al, 2013), configuring geometric characteristics (W. Vázquez-Méndez et al, 2021), and determining structural components (Kang & Schonfeld, 2020;, it generally determines the railway's construction cost (Lee et al, 2009), operational safety (Easa & Mehmood, 2008), environmental impact (Maji & Jha, 2009), and geologic risk (Pu, Xie, et al, 2021). However, due to the large-scale and highly constrained study area (Hong Zhang et al, 2021), multiple mutually conflicting and difficult-to-quantify objectives (Hirpa et al, 2016), numerous social and environmental influencing factors (Song, Pu, Schonfeld, Zhang, Li, Peng, et al, 2021) as well as many potential uncertainties (Song, Pu, Schonfeld, Hu, et al, 2022), railway alignment design is known as a complex task that, to a great extent, still depends on conventional manual work and expert experiences in the real world. Unfortunately, even with considerable invested efforts and very lengthy design cycles, human designers may overlook many promising alignments among the theoretically infinite number of possible solutions in the landscape (Jha et al, 2007;Jong et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the large‐scale and highly constrained study area (Hong Zhang et al., 2021), multiple mutually conflicting and difficult‐to‐quantify objectives (Hirpa et al., 2016), numerous social and environmental influencing factors (Song, Pu, Schonfeld, Zhang, Li, Peng, et al., 2021) as well as many potential uncertainties (Song, Pu, Schonfeld, Hu, et al., 2022), railway alignment design is known as a complex task that, to a great extent, still depends on conventional manual work and expert experiences in the real world. Unfortunately, even with considerable invested efforts and very lengthy design cycles, human designers may overlook many promising alignments among the theoretically infinite number of possible solutions in the landscape (Jha et al., 2007; Jong et al., 2000).…”

Section: Introductionmentioning

confidence: 99%

Mountain railway alignment optimization integrating layouts of large‐scale auxiliary construction projects

Schonfeld

Liang

et al. 2022

Computer aided Civil Eng

Self Cite

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Mountain railway alignment design is an important but complex civil engineering problem. To overcome the drastically undulating terrain, long tunnels and high bridges are major structures used along a mountain railway, which poses great challenges for railway design and construction. Unfortunately, despite being studied for many years, the crucial construction factors of complex structures have received slight attention in alignment optimization. In this paper, for the first time, the layout of large‐scale auxiliary construction projects (LACPs), including tunnel shafts and access roads, is incorporated into the alignment design process in order to consider construction practicability and economy. Primarily, an alignment–LACPs concurrent optimization model is built. After defining the comprehensive design variables, the alignment–LACPs total construction cost is formulated as the objective function. Besides, the separate constraints for designing the alignment and LACPs are considered. Also, a construction duration computation is proposed for constraining the alignment–LACPs integration. To solve the model, a four‐step hybrid solution method is developed. Specifically, the alignment is first generated with a particle swarm optimization (PSO). Afterward, a new divide and conquer approach is devised to search for shaft alternatives along the alignment. Then, a customized Dijkstra algorithm is developed to search for complex access roads. Finally, a novel polynomial mechanism for time‐varying acceleration coefficients (TVAC) is designed for PSO to evolve the alignment–LACPs solutions. The above model and methods have been applied to two complex actual mountain railway examples. Their effectiveness is demonstrated through detailed analysis of resulting railway solutions and control experiments with contemporary TVAC‐based methods.

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Modeling and application of a customized knowledge graph for railway alignment optimization

Pu,

Hu,

Song

et al. 2024

Expert Systems with Applications

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Robust Optimization Method for Mountain Railway Alignments Considering Preference Uncertainty for Costs and Seismic Risks

Cited by 10 publications

References 54 publications

Optimizing net present values of risk avoidance for mountain railway alignments with seismic performance evaluation

Optimizing net present values of risk avoidance for mountain railway alignments with seismic performance evaluation

Mountain railway alignment optimization integrating layouts of large‐scale auxiliary construction projects

Modeling and application of a customized knowledge graph for railway alignment optimization

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