Use of Heading Direction for Recreating the Horizontal Alignment of an Existing Road

This article proposes a new method for fitting the horizontal alignment of a road to a set of (x, y) points. Those points can be obtained from digital imagery or GPS‐data collection. Unlike current methods that represent road alignment through its curvature, the proposed method describes the horizontal alignment as a sequence of headings. An analytic–heuristic approach is introduced. The proposed method produces unique solutions even for complex horizontal alignments. Some examples and a case study are presented. This solution may not be accurate enough for road redesign, but it allows researchers and departments of transportation to obtain accurate geometric features.     

A 3D Model for Optimizing Infrastructure Costs in Road Design

In this article, the optimal design of a road joining two terminals is investigated. A geometric model is proposed including horizontal transition curves and vertical curves, obtaining parameterizations for the central axis of the road as well as for its entire surface. These parameterizations allow to express and compute, with great simplicity, the major infrastructure costs, including land acquisition, clearance, pavement, maintenance, and earthwork, where multiple layers of materials with different costs can be handled. The road design problem is formulated as a smooth constrained optimization problem and a two‐stage algorithm is suggested for its numerical resolution. Finally, numerical results are presented in an academic test and in a case study that propose designing a bypass in a Spanish national road (N‐640) to avoid crossing Monterroso's town center.     

A Method for Automatically Recreating the Horizontal Alignment Geometry of Existing Railways

Periodic recreation of existing railway horizontal alignment geometry is needed for smoothing the deviations arising from train operations. It is important for calibrating track and rebuilding existing railways to ensure safety and comfort. Track calibration repairs the existing distorted track centerline to match the smoothed recreated alignment, which may differ considerably from the originally designed track centerline. Identifying the boundaries of all the geometric elements including tangents, circular curves, and transition curves is the key problem. Existing methods recreate the horizontal alignment semi‐automatically and can only generate a locally optimized solution while considering a few constraints. Based on the principle that the attributions of all the measured points to geometric elements should be consistent with the ranges of recreated geometric elements (i.e., for points‐alignment consistency), a method called swing iterations is proposed to reclassify point placements and identify all the tangents, circular curves, and transition curves simultaneously. In a swing iteration, the boundary of a geometric element segment repeatedly changes from left to right, then from the right to left, and finally stabilizes. Before the swing iterations, preliminary tangents and curves are identified based on the heading gradient (i.e., the rate of change of heading), and are set as initial values for the swing iterations. A genetic algorithm is developed to further refine the entire recreated alignment after the swing iterations. In the above processes, multiple constraints are handled. Applications demonstrate that this method can identify all horizontal geometric elements automatically and generate an optimized recreated alignment geometry for an existing railway while satisfying all the applicable constraints.     

Semiautomatic Extraction of Road Horizontal Alignment from a Mobile LiDAR System

This article proposes a method to semiautomatically extract the road axis through a mobile LiDAR system, a recent popular technology for transportation‐related applications, road estimation and even to enhance driver safety. In particular, the approach developed has two components: (1) the feature extraction from LiDAR data to model the road axis, and (2) the estimation of the horizontal alignment that meets the requirements and practice for a transportation authority. Given the massive and complex character of the data captured by the system, a hierarchical (coarse‐to‐fine) and robust strategy based on segmentation, parameterization and filtering, which determine the road centerline together with the geometric elements that compose its horizontal alignment, such as straight lines, circular arcs, and clothoids, has been developed and implemented. Test results using a simulated and a real data are discussed and validated. The experimental results obtained with real cases guarantying relative accuracies under 2%, being a useful approach to produce accurate estimations of the horizontal geometric features of the road alignment.     

基于改进型蚁群算法的结构优化设计

结构优化设计中通常含有离散设计变量,本文探讨了如何将改进型蚁群算法应用于含离散设计变量的结构优化问题。以十杆桁架结构离散设计变量优化问题为例进行仿真,仿真结果表明蚁群算法能比较有效地解决含离散设计变量的优化问题。     

Optimal tuning of fuzzy parameters for structural motion control using multiverse optimizer

In recent years, there is an increasing interest in optimization of structural control algorithms. Fuzzy logic controller is one of the most common and versatile control algorithms that is generally formulated based on the human knowledge and expert. Human knowledge and experience do not yield optimal control responses for a given structure, and tuning of the fuzzy parameters is necessary. This paper focuses on the optimization of a fuzzy controller applied to a seismically excited nonlinear building. In the majority of cases, this problem is formulated based on the linear behavior of the structure; however, in this paper, objective functions and the evaluation criteria are considered with respect to the nonlinear responses of the structures. Multiverse optimizer is a novel nature‐inspired optimization algorithm that is based on the three concepts of cosmology as white hole, black hole, and wormhole. This algorithm has fast convergence rate and can be utilized in continuous and discrete optimization problems. In this paper, the multiverse optimizer is considered as the optimization algorithm for optimization of the fuzzy controller. The performance of the selected algorithm is compared with eight different optimization algorithms. The results prove that the selected algorithm is able to provide very competitive results.     

A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization

Abstract: The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO‐based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient‐based quasi‐Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms.     

GPS–GIS-Based Procedure for Tracking Vehicle Path on Horizontal Alignments

Abstract:   This article presents a global positioning system–geographic information system (GPS–GIS)-based procedure for the deduction of the horizontal alignment of a road based on the path of a control vehicle. Using differential GPS surveying, field data were collected at a 0.1-second interval, under different speed conditions on a 25-km section of a two-lane rural highway in eastern Ontario. The raw GPS data were post-processed to filter out the possible errors and then imported into a GIS environment for analysis and interpretation of the results. An extension for ArcView was written to determine the geometric features of the highway horizontal alignment, including the tangents, spirals, and circular curves. Values were obtained for the radius and length of nine circular curves, length of spirals, and the lateral position of the vehicle path along the straight and curved segments. These values were compared with the same features of the actual highway alignment. The results showed that the developed procedure and ArcView extension could produce the horizontal alignment of a road quickly, accurately, and for a relatively low cost. In addition to the extraction of the horizontal alignment of a road, the procedure can be used to track the actual vehicle path under normal driving conditions and compare it with the horizontal alignment of a road in an investigation concerning driver behavior.     

Road Infrastructure Data Acquisition Using a Vehicle-Based Mobile Mapping System

Abstract:   This study presents the technology of a vehicle-based mobile mapping system to maintain an updated transportation database. The mobile mapping system that integrates the global positioning system (GPS), the inertial navigation system (INS), and digital cameras has been developed to collect data on position and attributes of road infrastructure. The vehicle-based mobile mapping system works by having the GPS and INS record the position and attitude data, and digital cameras take road images. The stereovision system can determine the position of objects that are visible on the image pair in the global coordinate system with GPS and INS data. As field data acquisition is a very expensive task, a mobile mapping system offers a greatly improved solution. In this study, we successfully created a road infrastructure map with mobile mapping technology and proposed automatic algorithms for detecting and identifying road signs from road images. The proposed detection algorithm includes line and color region extraction processes and uses the Hopfield neural networks. The identification algorithm uses seven invariant moments and parameters that present geometric characteristics. With this combined method, we could successfully detect and identify road signs.     

Reliability-based design optimization with discrete design variables and non-smooth performance functions: AB-PSO algorithm

The purpose of reliability-based design optimization (RBDO) is to find a balanced design that is not only economical but also reliable in the presence of uncertainty. Practical applications of RBDO involve discrete design variables, which are selected from commercially available lists, and non-smooth (non-differentiable) performance functions. In these cases, the problem becomes an NP-complete combinatorial optimization problem, which is intractable for discrete optimization methods. Moreover, the non-smooth performance functions would hinder the use of gradient-based optimizers as gradient information is of questionable accuracy. A framework is presented in this paper whereby subset simulation is integrated with a new particle swarm optimization (PSO) algorithm to solve the discrete and non-smooth RBDO problem. Subset simulation overcomes the inefficiency of direct Monte Carlo simulation (MCS) in estimating small failure probabilities, while being robust against the presence of non-smooth performance functions. The proposed PSO algorithm extends standard PSO to include two new features: auto-tuning and boundary-approaching. The former feature allows the proposed algorithm to automatically fine tune its control parameters without tedious trial-and-error procedures. The latter feature substantially increases the computational efficiency by encouraging movement toward the boundary of the safe region. The proposed auto-tuning boundary-approaching PSO algorithm (AB-PSO) is used to find the optimal design of a ten-bar truss, whose component sizes are selected from commercial standards, while reliability constraints are imposed by the current design code. In multiple trials, the AB-PSO algorithm is able to deliver competitive solutions with consistency. The superiority of the AB-PSO algorithm over standard PSO and GA (genetic algorithm) is statistically supported by non-parametric Mann-Whitney U tests with the p-value less than 0.01.     

Prescreening and Repairing in a Genetic Algorithm for Highway Alignment Optimization

Abstract:   The method of handling infeasible solutions in an evolutionary search algorithm [e.g., genetic algorithms (GAs)] is crucial to the effectiveness of the solution search process. This problem arises because solution search steps, techniques, and operators used in GAs (such as reproduction, mutation, and recombination) are normally  " blind " to the constraints, and thus GAs can generate solutions that do not satisfy the requirements of the problems. In GA-based highway alignment optimization (HAO), many infeasible solutions, which violate model constraints, are also possibly generated, and evaluation of such solutions is wasteful. This study focuses on ways to avoid wasting computation time on evaluating infeasible solutions generated from the GA-based HAO, and develops a prescreening and repairing (P&R) method for an efficient search of highway alignments. The key idea of the P&R method is to repair (before the very detailed alignment evaluation) any candidate alignments whose violations of design constraints can be fixed with reasonable modifications. However, infeasible alignments whose violations of constraints are too severe to repair are discarded (prescreened) before any detailed evaluation procedure is applied. The proposed P&R method is simple, but significantly improves computation time and solution quality in the GA-based HAO process. Such improvements are demonstrated with a test example for a real road project. Through the example study, it is shown that the model incorporating the P&R method can find a good solution much faster (by approximately 23%) than the model with the conventional penalty method. In addition, the P&R method allows the model to evaluate about 70% more solutions than that it can evaluate with the penalty method for the same number of generations.     

Calibration of the descent local search algorithm parameters using orthogonal arrays

Solving optimization problems using heuristic algorithms requires the selection of its parameters. Traditionally, these parameters are selected by a trial and error process that cannot guarantee the quality of the results obtained because not all the potential combinations of parameters are checked. To fill this gap, this paper proposes the application of Taguchi's orthogonal arrays to calibrate the parameters of a heuristic optimization algorithm (the descent local search algorithm). This process is based on the study of the combinations of discrete values of the heuristic tool parameters and it enables optimization of the heuristic tool performance with a reduced computational effort. To check its efficiency, this methodology is applied to a technical challenge never studied before: the optimization of the tensioning process of cable‐stayed bridges. The statistical improvement of the heuristic tool performance is studied by the optimization of the tensioning process of a real cable‐stayed bridge. Results show that the proposed calibration technique provided robust values of the objective function (with lower minimum and mean values, and lower standard deviation) with reduced computational cost.     

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 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.     

Constrained mean‐variance mapping optimization for truss optimization problems

Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.     

A mixed-integer linear programming approach for temporary haul road design in rough-grading projects

Temporary haul road plays a pivotal part in achieving cost-efficiency and successful project delivery in heavy civil and industrial construction. Temporary haul road layout design has been empirically performed by field managers, superintendents or even truck drivers largely based on experience instead of science. Previous research endeavors in earthmoving research devised analytical algorithms to minimize the total earthmoving cost and developed simulation models to optimize earthmoving resources and processes. In the same domain, this research introduces an optimization methodology for temporary haul road layout design in order to facilitate mass earthmoving operations and improve construction performances on a typical rough-grading site. A Mixed-Integer Linear Programming model, integrated with a cutting plane method, is established for the identified problem. In particular, the cutting plane method is introduced to refine the optimization formulation by maintaining field accessibility and haul road continuity, thus ensuring practical feasibility of the analytical solution. Performances of the newly devised algorithms were benchmarked against genetic algorithms in solving ten test cases. Further, feasibility of the proposed methodology was evaluated based on a real-world case study. In conclusion, the proposed methodology is capable of tackling the temporary haul road layout design problem with high computing efficiency and delivering optimal results that are ready for field implementation.     

立交匝道平面线形优化设计及MATLAB的应用

本文在探讨最优化计算理论与方法的基础上,分析立交匝道平面线形设计的特点与方法,拟定线形组合型式,建立平面线形优化设计的数学模型,给出相应的优化计算方法,并应用大型通用的辅助计算软件MATLAB优化工具箱里的优化函数,实现线形参数的优化设计。     

改进的万有引力搜索算法在边坡稳定分析中的应用

基于万有引力搜索算法(GSA)提出了一种改进的万有引力搜索算法(MGSA)。针对GSA在处理优化问题时会出现发散的情况,通过限制粒子的速度同时更改算法中的参数来改善这一问题。算法改进后显著提高了GSA中粒子的探索能力与开发能力,可以获得较强的优化能力。采用MATLAB对8个测试基准函数进行仿真实验,并将该方法引入到边坡稳定分析中。对于边坡稳定性分析,利用MGSA搜索出临界滑动面并结合极限平衡法计算出相应的最小安全系数。结果表明:与GSA法及其他方法相比,MGSA在求解最危险滑动面安全系数时具有更好的优化性能。     

地下水渗流模型参数识别的模拟退火算法

反问题的求解常常需要转化为非线性优化问题，其目标函数定义为观测数据与模型数据之间的残差平方和。地下水模型参数识别最常用的优化方法都是基于梯度搜索，其缺陷在于对模型参数初始估计比较敏感和局部极小问题。与传统的基于梯度搜索的优化方法相比，模拟退火算法具有良好的全局收敛特性。把含水层参数识别反问题转化为组合优化问题，提出模拟退火算法识别二维、非稳态地下水流动模型的渗透系数和储水系数的策略。反问题的不适定性由解的不唯一性和不稳定性来表征，模拟退火算法具有解决这一问题的能力。通过与梯度搜索算法相对比，数值模拟计算结果显示所提出反演方法的有效性和适用性。