Vertical alignment optimization of mountain railways with terrain-driven greedy algorithm improved by Monte Carlo tree search

Vertical alignment design is an important process for railway construction which fundamentally affects the infrastructure investment cost. Determining an optimized vertical alignment is a challenging task since the objective function is non-linear, non-differentiable, and quite unsmooth. Great efforts have been invested in solving the vertical alignment optimization problem and many methods have been proposed. However, for vertical alignment designs in complex mountainous regions, the terrain conditions impose great difficulties and, hence, many bridges and tunnels are generally required. Thus, reasonably locating bridges and tunnels along the entire alignment (EA) is a major concern that deserves further investigations. To solve this problem, this study develops a terrain-driven greedy algorithm improved by Monte Carlo tree search (T-GRA-MCTS). A terrain-driven method is proposed to determine the number and longitudinal distribution of vertical points of intersection (VPIs). In order to trade off the local section of an alignment versus the EA when optimizing each VPI along the alignment to locate bridges and tunnels reasonably, an MCTS is employed and integrated with a GRA. The basic MCTS is modified for vertical alignment optimization with a novel equation for computing the upper confidence bounds for trees and a customized termination criterion is provided. A real-world railway case is used to demonstrate the effectiveness of the proposed method. The results show that the T-GRA-MCTS performs better than a greedy search method without MCTS or a widely used nature-inspired algorithm (i.e., a particle swarm optimization). Moreover, it can find a less expensive solution than the one designed by experienced human engineers.     

A deep reinforcement learning approach to mountain railway alignment optimization

The design and planning of railway alignments is the dominant task in railway construction. However, it is difficult to achieve self-learning and learning from human experience with manual as well as automated design methods. Also, many existing approaches require predefined numbers of horizontal points of intersection or vertical points of intersection as input. To address these issues, this study employs deep reinforcement learning (DRL) to optimize mountainous railway alignments with the goal of minimizing construction costs. First, in the DRL model, the state of the railway alignment optimization environment is determined, and the action and reward function of the optimization agent are defined along with the corresponding alignment constraints. Second, we integrate a recent DRL algorithm called the deep deterministic policy gradient with optional human experience to obtain the final optimized railway alignment, and the influence of human experience is demonstrated through a sensitivity analysis. Finally, this methodology is applied to a real-world case study in a mountainous region, and the results verify that the DRL approach used here can automatically explore and optimize the railway alignment, decreasing the construction cost by 17.65% and 7.98%, compared with the manual alignment and with the results of a method based on the distance transform, respectively, while satisfying various alignment constraints.     

Mountain railway alignment optimization based on landform recognition and presetting of dominating structures

Mountain railway alignment optimization has always been a challenge for designers and researchers in this field. It is extremely difficult for existing methods that optimize alignments before major structures to generate a better alignment than the best one provided by human designers when the terrain is drastically undulating between the start and endpoints. To fill this gap, a “structures before alignments” design process is proposed in this paper. Primarily, a landform recognition method is devised for recognizing dominating landforms. Then, a bi-level alignment optimization model is proposed, with the upper level dedicated to characterizing dominating structures and the lower level focusing on optimizing the entire alignments. To solve this bi-level model, a three-stage optimization method is designed. At the first stage, a scanning process and screening operators are devised for generating all the possible locations of dominating structures. At the second stage, a hierarchical multi-criteria decision-making procedure is applied for selecting the optimized dominating structure layouts. At the third stage, alignments are optimized based on the determined structure layouts using a bi-objective optimization method, which minimizes construction cost and geo-hazard risk simultaneously. The proposed model and solution method are applied to two real-world cases whose results verify their capabilities in producing alignment alternatives with better combinations of construction cost and geo-hazard risk than manually designed alternatives.     

A Customized Particle Swarm Method to Solve Highway Alignment Optimization Problem

Abstract: Optimizing highway alignment requires a versatile set of cost functions and an efficient search method to achieve the best design. Because of numerous highway design considerations, this issue is classified as a constrained problem. Moreover, because of the infinite number of possible solutions for the problem and the continuous search space, highway alignment optimization is a complex problem. In this study, a customized particle swarm optimization algorithm was used to search for a near‐optimal highway alignment, which is a compound of several tangents, consisting of circular (for horizontal design) and parabolic (for vertical alignment) curves. The selected highway alignment should meet the constraints of highway design while minimizing total cost as the objective function. The model uses geographical information system (GIS) maps as an efficient and fast way to calculate right‐of‐way costs, earthwork costs, and any other spatial information and constraints that should be implemented in the design process. The efficiency of the algorithm was verified through a case study using an artificial map as the study region. Finally, we applied the algorithm to a real‐world example and the results were compared with the alignment found by traditional methods.     

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

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.     

Seismic liquefaction potential assessed by support vector machines approaches

Liquefaction of loose, saturated granular soils during earthquakes poses a major hazard in many regions of the world. Determining the liquefaction potential of soils induced by earthquakes is a major concern and an essential criterion in the design process of civil engineering structures. The present study examines the potential of support vector machines (SVMs) for assessing liquefaction potential based on cone penetration test (CPT) field data. A hybrid model based on a combination of SVMs and particle swarm optimization (PSO) is proposed in this study to improve the forecasting performance. PSO was employed in selecting the appropriate SVM parameters to enhance forecasting accuracy. Nine parameters, such as earthquake magnitude, the water table, the total vertical stress, the effective vertical stress, the depth, the peak acceleration at the ground surface, the cyclic stress ratio, the mean grain size and the measured CPT tip resistance, were used as input parameters. Prediction results demonstrate that the classification accuracy rates of the developed PSO–SVM approach surpass those of a grid search and many other approaches.     

A three‐dimensional distance transform for optimizing constrained mountain railway alignments

Railway alignment optimization is considered one of the most complicated and time‐consuming problems in railway planning and design. It requires searching among the infinite potential alternatives in huge three‐dimensional (3D) search spaces for a near‐optimal alignment, while considering complex constraints and a nonlinear objective function. In mountainous regions, the complex terrain and constructions require additional and more complex constraints than in topographically simpler regions. In this paper, the authors solve this problem with an algorithm based on a 3D distance transform (3D‐DT). Compared with previous two‐dimensional distance transform (2D‐DT) methods developed in this field, the feasible search spaces of 3D‐DT are greatly increased. Consequently, this new method can find more alternatives with higher qualities. In this approach, an erythrocyte‐shaped 3D neighboring mask is developed to narrow local search spaces and speed up the search process. Besides, a stepwise‐backstepping strategy is designed to dynamically determine feasible 3D search spaces and efficiently search the study area. During the 3D‐DT search process, multiple constraints, including geometric, construction, and location constraints, are effectively handled. After the 3D‐DT search, a genetic algorithm is employed to optimize the 3D‐DT paths into final alignments. Finally, this novel approach is applied to an actual case in a complex mountainous region. The comprehensive cost of the best solution generated by 3D‐DT is 16% below a manual solution produced by very experienced human designers. Furthermore, the total number of feasible alternatives found by 3D‐DT is 4.3 times greater than by 2D‐DT. The comprehensive cost of the best 3D‐DT solution is 10% below the best one generated by 2D‐DT.     

重庆轻轨沿线景观设计难点探析--以重庆轻轨二号线较新线为例

面对特殊的地形条件,重庆轻轨的出现为城市景观设计带来了全新的挑战。为了更好地提高环境质量,营造具有山城特色的环境氛围,本文对重庆轻轨沿线景观设计中出现的部分难点进行了分析和研究,希望能对正在进行的工程建设提供参考。     

改进的PSO算法在桁架结构形状优化设计中的应用

利用随机方向法初始化种群提高粒子群算法初始种群的质量．将模糊推理应用于粒子群算法的参数调整克服了人为经验设定参数的不足,种群搜索过程中嵌入Metropolis准则改善粒子群算法的鲁棒性能。将改进的粒子群算法应用于桁架结构形状优化设计中。实验仿真表明．改进后的算法具有较好的搜索性能和较高的计算精度,有望实现应用在复杂的桁架结构优化设计中．其具有重要的理论研究价值和广阔的工程应用前景.     

Mountain Railway Alignment Optimization with Bidirectional Distance Transform and Genetic Algorithm

Various challenging constraints must be satisfied in railway alignment design for topographically complex mountainous regions. The alignment design for such environments is so challenging that existing methodologies have great difficulties in automatically generating viable railway alignment alternatives. We solve this problem with a hybrid method in which a bidirectional distance transform (DT) algorithm automatically selects control points before a genetic algorithm (GA) refines the alignment. This approach solves the problems of (1) determining the appropriate distribution of control points in the GA and (2) producing alignments that deviate significantly from the DT‐optimized paths. Automatic design of backtracking curves and dynamic generation of vertical points of intersection handling multiple constraints are developed to improve the GA performance. This method has been applied to a real case on the Sichuan–Tibet Railway where excessively severe natural gradients must be overcome. It automatically finds an alignment optimized for the given objectives and complex constraints, as well as various promising alternatives.     

Design of water distribution networks using particle swarm optimization

Application of particle swarm optimization (PSO) is demonstrated through design of a water distribution pipeline network. PSO is an evolutionary algorithm that utilizes the swarm intelligence to achieve the goal of optimizing a specified objective function. This algorithm uses the cognition of individuals and social behaviour in the optimization process. For the optimization of water distribution system, a simulation – optimization model, called PSONET is developed and used in which the optimization is by PSO. This formulation is applied to two benchmark optimization design problems. The results are compared with the results obtained by other optimization methods. The results show that the PSO is more efficient than other optimization methods as it requires fewer objective function evaluations.     

Cyber‐physical approach to the optimization of semiactive structural control under multiple earthquake ground motions

This paper presents a cyber‐physical approach to optimize the semiactive control of a base‐isolated structure under a suite of earthquakes. The approach uses numerical search algorithms to guide the exploration of the design space and real‐time hybrid simulation (RTHS) to evaluate candidate designs, creating a framework for real‐time hybrid optimization (RHTO). By supplanting traditional numerical analysis (i.e., finite element methods) with RTHS, structural components that are difficult to model can be represented accurately while still capturing global structural performance. The efficiency of RTHO is improved for multiple design excitations with the creation of a multiinterval particle swarm optimization (MI‐PSO) algorithm. As a proof‐of‐concept, RTHO is applied to improve the seismic performance of a base‐isolated structure with supplemental control. The proposed RTHO framework with MI‐PSO is a versatile technique for multivariate optimization under multiple excitations. It is well suited for the accurate and rapid evaluation of structures with nonlinear experimental substructures, in particular, those that do not undergo permanent damage such as structural control devices. The RTHO framework integrates popular optimization algorithms with advanced experimental methods, creating an exciting new cyber‐physical approach to design.     

下穿铁路箱涵引道路基路面排水设计

以原山大道下穿胶济铁路箱涵引道的路基路面排水的设计方案为例,从道路的纵断面设计、路基路面的结构设计、排水构筑物的设置等多个方面详细论述了下穿型引道的排水设计,为类似工程提供参考。     

青田县城市组团交通枢纽工程道路选线研究

根据工程的水文、地形、地质等条件,考虑道路在整个道路网的交通功能,深入研究山区城市道路的选线,并对东堡山隧道洞口选取方案、金温铁路节点方案等进行分析比选,最终确定最佳线形方案,满足路网交通的要求。     

遗传算法与自适应粒子群算法耦合的大坝安全预警评价模型

改变应用最小二乘法求解大坝统计预警模型的传统方式,利用粒子群算法随机搜索的优化能力确定统计模型的回归系数。针对粒子群算法收敛速度较慢等问题,提出一种新的自适应策略,能够依据粒子个体和种群的优化信息,调整学习因子,并将该策略与遗传算法的交叉、变异算子相结合。通过工程算例表明,该方法具备较好的搜索多样解能力,自适应地调整粒子飞行的步长,提高了粒子群算法的收敛速度;基于该方法的大坝预警评价模型与最小二乘法、基本粒子群算法相比,数据挖掘能力强,预警评价结果与大坝的实际运行状态更加吻合,有效地提高了统计模型的预测精度。     

高速铁路混凝土结构耐久性技术创新及发展方向

介绍高速铁路建设初期混凝土结构耐久性面临的若干问题,从结构设计、材料优化、施工管控、附加防腐蚀措施以及养护维修五个方面系统阐述高速铁路桥梁、隧道及无砟轨道典型混凝土结构耐久性保障技术。结合当前高速铁路建设和发展需要,指出高速铁路混凝土结构耐久性技术的发展方向。     

Sustainability in highrise building design and construction

This paper presents a review of the recent literature on sustainability in construction and design with a focus on highrise buildings. The paper is divided into the following main sections: energy consumption, environmental effects and green practices for highrise buildings. A number of concepts in sustainable design are reviewed including passive solar design, renewable energy resources, cogeneration and tri‐generations, embodied energy reduction, net zero energy building, carbon emission reduction, envelope environment quality, green materials, efficient mechanical design and innovative structural systems. Their applications in a dozen signature and iconic structures are described. In order to achieve net zero energy in a new highrise building, first, multiple green solutions need to be evaluated using two categorical solutions: passive solar and envelop environment design and renewable energy resources along with efficient energy generators. Next, a robust optimization algorithm should be used to select the optimum set of solutions. This is worth pursuing in future sustainable design of highrise buildings because they are massive and complex structures with many components. Copyright © 2016 John Wiley & Sons, Ltd.     

A particle swarm ant colony optimization for truss structures with discrete variables

In this paper, a particle swarm optimizer with passive congregation (PSOPC), ant colony optimization (ACO) and harmony search scheme (HS) are combined to reach to an efficient algorithm, called discrete heuristic particle swarm ant colony optimization (DHPSACO). This method is then employed to optimize truss structures with discrete variables. The DHPSACO applies a PSOPC for global optimization and the ant colony approach for local search, similar to its continuous version. The problem-specific constraints are handled using a modified feasible-based mechanism, and the harmony search scheme is employed to deal with variable constraints. Some design examples are tested using the new method and their results are compared to those of PSO, PSOPC and HPSO algorithms to demonstrate the effectiveness of the present method.     

Fluid transients and pipeline optimization using GA and PSO: the diameter connection

This paper describes the optimal selection of pipe diameters in a network considering steady state and transient analysis in water distribution systems. Two evolutionary approaches, namely genetic algorithms (GA) and particle swarm optimization (PSO), are used as optimization methods to obtain pipe diameters. Both optimization programs, inspired by natural evolution and adaptation, show excellent performance for solving moderately complex real-world problems which are highly nonlinear and demanding. The case study shows that the integration of GA or PSO with a transient analysis technique can improve the search for effective and economical hydraulic protection strategies. This study also shows that not only is the selection of pipe diameters crucially sensitive for the surge protection strategies but also that more global systematic approaches should be involved in water distribution system design, preferably at an early stage in the design process.     

Rapid geomorphological reconnaissance survey for road alignment in West Nepal

A rapid geomorphological reconnaissance survey of the 112 km Baitadi-Darchula road currently under construction in the Middle Himalaya of West Nepal is described. The first 39 km of the proposed road was under construction when this 5 day survey commenced, to cover both the initial segment as well as an examination of the stability and practicability of the remainder of the alignment. A combination of landslide and erosion mapping along the proposed alignment and interpretation of air photographs led to the recommendation to abandon the remainder of the alignment and adopt an alternative route, considered to be more stable. The findings were later endorsed by a detailed feasibility study and preliminary design thus supporting the cost-effective use of rapid geomorphological surveys for engineering projects in unstable mountain terrain.