Mountain Railway Alignment Optimization with Bidirectional Distance Transform and Genetic Algorithm

Li, Wei; Pu, Hao; Schonfeld, Paul; Yang, Junsheng; Zhang, Hong; Wang, Lei; Xiong, Jiaxing

doi:10.1111/mice.12280

Cited by 90 publications

(84 citation statements)

References 55 publications

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“…Therefore, an adjusted vertical path can be generated. Finally, using the SHKPs and SVKPs as input data, the authors here employ a GA‐based method to optimize the refined 3D path into a final 3D alignment (detailed descriptions can be found in Li et al., ). The final horizontal alignment is shown in Figure c and the final vertical alignment can be similarily obtained.…”

Section: Search Methodologymentioning

confidence: 99%

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

Song

Schonfeld

et al. 2019

Computer aided Civil Eng

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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.

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Section: Search Methodologymentioning

confidence: 99%

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

Song

Schonfeld

et al. 2019

Computer aided Civil Eng

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“…In using a GA to solve both problems of optimizing new‐built alignments and recreating existing alignments, the initial distribution of control points for alignment needs to be preset before genetic coding. In previous studies, we proposed two methods for determining the initial distribution of control points: (1) presetting the control points at equal intervals along the straight line linking the start and end points (Jong, ; Kang et al., 2013; Yang et al., ), (2) presetting the control points according to a promising path generated by an improved distance transform (Li et al., ). Unfortunately, all these methods proposed for newly built alignments cannot be used to recreate existing alignment, because an initial distribution of control points should be as close as possible to the actual existing track centerline.…”

Section: Optimizing An Entire Alignment With a Gamentioning

confidence: 99%

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

Schonfeld

et al. 2018

Computer aided Civil Eng

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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.

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“…The WT provides a powerful tool for the time‐frequency analysis of time series signals (Boashash, Khan, & Ben‐Jabeur, ; Li, Park, & Adeli, ). A number of researchers have used the WT to identify structural modal parameters (Amini et al, ; Amini & Zabihi Samani, ; Kijewski & Kareem, ; Staszewski, ) and develop integrated structural system identification techniques (Adeli & Jiang, ; Jiang & Adeli, ; Jiang, Mahadevan, & Adeli, ).…”

Section: Intelligent Controlmentioning

confidence: 99%

“…advanced adroit integration of ANN(Quintian & Corchado, 2017;Weissenberger, Meier, Lengler, Einarsson, & Steger, 2017) and GA(Li, Pu, Schonfeld, Yang, Zhang, Wang, and Xiong, 2017;Mencıa, Sierra, Mencıa, & Varela, 2016;Zhao, Guo, Zhou, & Zhang, 2018) with fuzzy logic(D'Urso, Masi, Zuccaro, & De Gregorio, 2018;Peng, Wang, Shi, Pérez-Jiménez, & Riscos-Núñez, 2017) …”

mentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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Mountain Railway Alignment Optimization with Bidirectional Distance Transform and Genetic Algorithm

Cited by 90 publications

References 55 publications

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

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

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

Control methodologies for vibration control of smart civil and mechanical structures

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