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.     

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.     

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.     

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.     

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

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.     

Preliminary Highway Design with Genetic Algorithms and Geographic Information Systems

A method that integrates geographic information systems (GIS) with genetic algorithms (GAs) for optimizing horizontal highway alignments between two given end points is presented in this article. The proposed approach can be used to optimize alignments in highly irregular geographic spaces. The resulting alignments are smooth and satisfy minimum-radius constraints, as required by highway design standards. The objective function in the proposed model considers land-acquisition cost, environmental impacts such as wetlands and flood plains, length-dependent costs (which are proportional to the alignment length), and user costs. A numerical example based on a real map is employed to demonstrate application of the proposed model to the preliminary design of horizontal alignments.     

A modified motion planning algorithm for horizontal highway alignment development

A horizontal alignment can be represented by three key factors: number of horizontal points of intersection (HPIs), their locations, and corresponding horizontal curve radii. Deciding all the three factors simultaneously requires extensive effort, which is not practically feasible in the manual alignment development process. Most available computer‐aided methods prioritize some or all the three factors in the automated alignment development processes. However, approximation in HPI location or pre‐selection of HPI number and curve radius are the few limitations of these methods. This study presents a modified motion‐planning based algorithm for developing new horizontal alignments with optimized costs and impacts. It simultaneously uses a low‐discrepancy sampling technique to develop increasingly dense potential HPIs, rapidly exploring random trees to find a suitable number of intermediate HPIs at appropriate locations and sequential quadratic algorithm to select optimally fitted curve radii. The proposed algorithm is integrated with the GIS database for realistic location‐dependent cost and environmental impact assessment. Two real‐world study areas were selected to compare the results with the one reported in the literature and to evaluate backtracking capability. Results indicated the proficiency of the proposed algorithm in developing new alignments. The sensitivity analyses revealed the effect of design speed and right‐of‐way width on the alignment generation. The proposed algorithm can automate the new horizontal highway alignment development process and support highway engineers in planning and development.     

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.     

Energy-efficient virtual sensor-based deep reinforcement learning control of indoor CO2 in a kindergarten

High concentrations of indoor CO₂ pose severe health risks to building occupants. Often, mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor CO₂ concentrations. However, such equipment consumes large amounts of energy, substantially increasing building energy consumption. In the end, the issue becomes an optimization problem that revolves around maintaining CO₂ levels below a certain threshold while utilizing the minimum amount of energy possible. To that end, we propose an intelligent approach that consists of a supervised learning-based virtual sensor that interacts with a deep reinforcement learning (DRL)-based control to efficiently control indoor CO₂ while utilizing the minimum amount of energy possible. The data used to train and test the DRL agent is based on a 3-month field experiment conducted at a kindergarten equipped with a heat recovery ventilator. The results show that, unlike the manual control initially employed at the kindergarten, the DRL agent could always maintain the CO₂ concentrations below sufficient levels. Furthermore, a 58% reduction in the energy consumption of the ventilator under the DRL control compared to the manual control was estimated. The demonstrated approach illustrates the potential leveraging of Internet of Things and machine learning algorithms to create comfortable and healthy indoor environments with minimal energy requirements.     

Optimizing Design of Highway Horizontal Alignments: New Substantive Safety Approach

Abstract:   Highway agencies are continually facing safety problems on highways, especially on horizontal alignments. Traditionally, the geometric design implicitly considers safety through satisfying minimum design requirements for different geometric elements. This article presents a new substantive-safety approach for the design of horizontal alignments based not only on minimum design guidelines, but also on actual collision experience. The curve radii, spiral lengths, lane width, shoulder width, and tangent lengths are determined to optimize the mean collision frequency along the highway. The model allows the parameters of the horizontal alignment to vary within specified ranges. The model also considers any specified physical obstructions in selecting the optimal alignment. Collision experience is addressed using existing collision prediction models for horizontal alignments and cross sections. The model is applicable to two-lane rural highways for which collision prediction models exist. Application of the model is presented using numerical examples. The proposed substantive-safety approach takes horizontal alignment design one step further beyond the minimum-guideline concept, and therefore should be of interest to highway designers.     

考虑施工误差的弯坡跨线桥转体施工线形调整

针对既有施工误差影响下弯坡跨线桥转体施工的平顺拼接问题,提出了一种基于最小二乘法原理的线形调整方法,即首先实测两侧梁体中心点和球铰不动点的三维坐标,利用最小二乘法原理拟合得到平、纵曲线的理想线形;再以理想线形为目标,利用最小二乘法原理确定实际转体角度、转体到位标识及转体微调值等参数。通过在滨德高速公路上跨京沪铁路立交桥转体施工中的实际应用,结果表明,该方法计算精确,操作简便,能考虑既有误差的影响,实现线形的最优调整。     

A sequential exploration algorithm for the design optimization of horizontal road alignment

Most computer-aided optimization procedures for horizontal alignment optimization of roads require the use of information such as horizontal points of intersection (PIs) to determine an alignment. In these methods, to obtain parameters such as the radius of the curve corresponding to a specific PI, the previous and next PIs must be known. In this paper, a sequential exploration algorithm (SEA) is proposed, and the algorithm continuously explores the entire optimization space through certain steps. Only the parameters of the previous node are required to determine the current node's parameters during the exploration process, avoiding the tight coupling between PIs in traditional optimization algorithms. Furthermore, the proposed SEA does not require assumptions about the positions and numbers of the PIs, and it can design near-optimal road alignments that match geometric restrictions and automatically take transition curves into account. Another feature of the proposed algorithm is that it directly optimizes the geometric element parameters based on the actual milepost, and it is a fully collaborative optimization approach that does not require secondary optimization nesting during the optimization process. Analyses comparing the optimization effects of different algorithms are performed on a numerical case, that is, a problem of avoiding obstacles, and two actual cases from the literature, that is, a new road design problem and an existing road reconstruction problem. It is discovered that the proposed SEA results in an approximately 3% to 10% improvement in optimization effects when compared to two current cutting-edge optimization algorithms. This work offers a new perspective on road alignment optimization by merging discrete and continuous optimizations, with a discrete component handling optimization accuracy and a continuous component handling real optimization.     

浅析叙大铁路站场工程设计特点

叙大铁路为典型的山区铁路,本文结合其货运支线的功能定位,从车站设计与运营、工程的关系入手,结合站场工程实例,分析叙大铁路站场工程设计特点,探讨山区铁路站场设计对线路走向、工程措施、运营安全及工程投资的影响。     

周边地块工程施工对铁路高架桥及站房的安全影响分析

为保证邻近铁路规划影响区范围内的深基坑施工过程中,铁路高架桥及站房的安全,通过建立三维分析模型,对原基坑支护方案的变形控制效果进行评估,并提出了2种优化调整方案。针对优化后的基坑支护方案,根据施工进度设置计算工况,找出最不利工况进行变形分析,确保了铁路桥梁结构及站房的稳定,总结的经验可为类似基坑项目的安全设计提供借鉴。     

高速铁路CFG桩施工工艺及指标研究

结合高速铁路CFG桩设计与施工经验,通过一系列CFG桩施工工艺跟踪试验研究,叙述了高速铁路CFG桩施工工艺和质量控制,得出了各项指标控制范围,从而完善高速铁路CFG桩施工工艺。     

基坑降水设计浅谈

通过对某长、大、深基坑降水设计研究和多个类似工点的观摩学习和总结，在长、大、深基坑降水设计中，通过合理的降水设计，达到了良好的、预期的降水效果，指出此设计方法简捷、易操作、施工速度快、安全性高、可行性强。     

沪宁城际铁路新孟河特大桥深基坑施工方案

以沪宁城际铁路新盂河特大桥为案例,分析了该桥的特殊地理位置与施工难度,介绍了本特大桥深基坑的施工方案、施工工艺与相关施工措施,指出了施工注意事项,为类似工程提供了相关经验。     

Intersection Modeling for Highway Alignment Optimization

Abstract:   When optimizing a highway alignment, it is desirable to consider new and modified intersections along it. This article develops methods for locally optimizing intersections within highway alignment optimization processes. Design and operational characteristics for intersections are reviewed from the literature. The formulation considers the major costs that are sensitive to intersection characteristics. Genetic algorithms are used for optimal search. The proposed methods are implemented on an artificial study area and on a real one through the use of geographic information systems. The results show how the methods work for local optimization of intersections as well as for optimizing entire alignments. These methods can be used for improving search flexibility, thus allowing more effective intersections. They also provide a basis for extending the alignment optimization from single highways to networks .     

深水钻孔灌注桩施工中几个环节的质量控制

结合渝怀铁路25、26标段桥梁深水钻孔灌注桩基础的施工实践，从成孔过程，成桩过程，超前预控和成桩无损检测等几个方面简要地阐述了深水钻孔灌注桩施工中质量控制的要点与方法。指出了在施工过程中要严格控制，并不断总结提高，就能提高成桩的优良率。     

京津城际轨道交通工程建设管理实践

铁路客运专线较普通铁路主要具有速度快、建设标准高、投资规模大等特点,笔者通过参加京津城际轨道交通工程的工程咨询,体会到我国铁路客运专线建设与普通铁路不同,工期短、标准高、没有成熟经验是面临的主要问题,认为必须以系统的设计图纸和必要的投资概算保证为基础,同时应有完善的建设标准体系做保障,引进国外专业的咨询机构参与建设.