An algorithm for random generation of admissible horizontal alignments for optimum layout design

The vertical alignment optimization is about developing a minimum cost curvilinear vertical profile of constrained grade sections and appropriate non‐overlapping vertical curves passing through fixed control points with elevation constraints. Variations in ground profile and discreteness in unit cutting and filling costs make it a non‐convex, noisy, constrained optimization problem with many local minima. Further, the gradient related constraints and vertical curvature are non‐linear. This paper presents an innovative exploring and exploiting ant colony optimization (E&E‐ACO) algorithm with an appropriate point sampling, vertical curve fitting strategies, and an intuitive feasible region identification approach for solving the vertical alignment optimization problem. The E&E‐ACO algorithm extensively explores the feasible search space to generate a set of potential solutions and effectively exploit the space around the potential solutions for developing the optimized vertical alignment. The efficacy of the proposed method is demonstrated using two case studies. In one case study, the optimized solution by the proposed method had a marginally better objective function value and about three times lesser computational time than the solution by the mesh adaptive direct search method. The optimized alignment satisfied the elevation constraints of fixed control points and imitated the manually designed real‐world vertical alignment. The linearly varying exploration and exploitation parameters had better convergence rate than the other tested variations. Further, the proposed method at the end of 1000 iterations yielded about six times better result than the traditional ACO algorithm.

Section: Discussionmentioning

confidence: 99%

Exploring and exploiting ant colony optimization algorithm for vertical highway alignment development

Sushma

Roy

Maji

2022

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…Alignment design is a crucial civil engineering problem that fundamentally influences the life‐cycle conditions of a railway project (Song, Pu, Schonfeld, Li, et al., 2020). With the major aims of locating linear infrastructures (Davey et al., 2017; Kim et al., 2013), configuring geometric characteristics (W. Li et al., 2019; Vázquez‐Méndez et al., 2021), and determining structural components (Kang & Schonfeld, 2020; Pu, Zhang, et al., 2019), 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).…”

Section: Introductionmentioning

confidence: 99%

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

Schonfeld

Liang

et al. 2022

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.

“…Many efficient models have been proposed for vertical alignment optimization incorporating various interesting techniques: various heuristic approaches (e.g., Akay, 2003; Goktepe et al., 2009; Jong & Schonfeld, 2003; Lee & Cheng, 2001; Li et al., 2017; Song et al., 2021; Vázquez‐Méndez et al., 2021), deep learning techniques (e.g., Gao et al., 2022), dynamic programming (e.g., Fwa, 1989; Goh et al., 1988; Goktepe et al., 2005; Li et al., 2013), linear or mixed integer linear programming (MILP; e.g., Easa, 1988; Hare et al., 2011, 2015; Koch & Lucet, 2010; Moreb, 1996, 2009; Moreb & Aljohani, 2004), and other methods (e.g., Ozkan et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Modeling side slopes in vertical alignment resource road construction using convex optimization

Momo

Hare

Lucet

2022

A new convex quadratically‐constrained quadratic programming (QCQP) model is proposed for modeling side‐slopes volumes in the minimization of earthwork operations to compute the vertical alignment of a resource road while satisfying design and safety constraints. The new QCQP model is convex but nonlinear; it is compared to a state‐of‐the‐art mixed integer linear programming (MILP) model. The QCQP model can be viewed as the limit of this MILP model. Numerical results show that for roads with less than 100 stations, the QCQP model has similar computation time to the MILP model. However, the QCQP model significantly outperforms the MILP model for other roads, in some cases finding a global optimum in minutes while the MILP model fails to find any solution in hours. The technique is directly applicable for resource roads and has potential for other types of road.