A Survey on Pavement Sectioning in Network Level and an Intelligent Homogeneous Method by Hybrid PSO and GA

被引:2
|
作者
Nik, Ashkan Allahyari [1 ]
Nejad, Fereidoon Moghadas [2 ]
Zakeri, Hamzeh [2 ]
机构
[1] Young Researchers & Elites Club, Sci & Res Branch, Tehran, Iran
[2] AUT, Dept Civil & Environm Engn, 424,Hafez Ave, Tehran, Iran
关键词
ASPHALT PAVEMENT; MULTIOBJECTIVE OPTIMIZATION; JOINT OPTIMIZATION; MAINTENANCE; CRACKING; SYSTEM; REHABILITATION; SEGMENTATION; PERFORMANCE; OBJECTIVES;
D O I
10.1007/s11831-019-09360-w
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Pavement homogenous sectioning is a vital step in pavement management system (PMS) analysis. Sections can be created using either fixed, dynamic or static sectioning principles. Each of these has various implications with regard to the data collection and decision making strategy of management. Pavement management with true homogeneous section selection has great importance for cost minimization over a specified time period when modifying the pavement deterioration based on correct decisions in the PMS. This issue was proposed for cost reduction, minimization of sectioning errors, and accuracy improvement of pavement network analysis. Thus, the focus of this research is to investigate efficient hybrid methods applied for reducing complexity involved in this problem. Results show that various combinations of hybrid particle swarm optimization (PSO) and genetic algorithm (GA) were used for analysis of a given pavement network that play a better role as section makers than single GA or PSO in terms of network sectioning error, computation time (CPU time), and number of sections as well as convergence diagrams for network, project, and section management levels. Results indicated that hybrid approaches provide a highly suitable solution in a short time for each pavement branch in massive networks with big data and minimize the costs involved in the sectioning process.
引用
收藏
页码:977 / 997
页数:21
相关论文
共 50 条
  • [41] Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms
    Ankush Choudhary
    Manoj Kumar
    Munish Kumar Gupta
    Deepak Kumar Unune
    Mozammel Mia
    Neural Computing and Applications, 2020, 32 : 5761 - 5774
  • [42] Intelligent Motion Controller Design for Four-Wheeled Omnidirectional Mobile Robots Using Hybrid GA-PSO Algorithm
    Huang, Hsu-Chih
    2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2011, : 2267 - 2272
  • [43] Inferring gene regulatory networks using a hybrid GA-PSO approach with numerical constraints and network decomposition
    Lee, Wei-Po
    Hsiao, Yu-Ting
    INFORMATION SCIENCES, 2012, 188 : 80 - 99
  • [44] Hybrid modeling of piezoresistive pavement using finite element method and artificial neural network
    Wang, Tianling
    Shi, Jianwei
    Wang, Haopeng
    Oeser, Markus
    Liu, Pengfei
    MATERIALS AND STRUCTURES, 2025, 58 (02)
  • [45] Gray and fuzzy clustering method-based on network level pavement performance assessment
    Zhang L.
    Ling J.
    Zhu Y.
    Tongji Daxue Xuebao/Journal of Tongji University, 2010, 38 (02): : 252 - 256
  • [46] Correction to: Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms
    Ankush Choudhary
    Manoj Kumar
    Munish Kumar Gupta
    Deepak Rajendra Unune
    Mozammel Mia
    Neural Computing and Applications, 2020, 32 : 11961 - 11961
  • [47] Process Parameters Optimization for Multiple Quality Characteristics in Plastic Injection Molding using Taguchi Method, BPNN, GA, and Hybrid PSO-GA
    Chen, Wen-Chin
    Kurniawan, Denni
    INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2014, 15 (08) : 1583 - 1593
  • [48] Process parameters optimization for multiple quality characteristics in plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSO-GA
    Wen-Chin Chen
    Denni Kurniawan
    International Journal of Precision Engineering and Manufacturing, 2014, 15 : 1583 - 1593
  • [49] Hybrid application of an inductive learning method and a neural network for intelligent information retrieval
    Cortez, Edwin M.
    Park, Sang C.
    Kim, Seonghee
    Information Processing and Management, 1995, 31 (06): : 789 - 813
  • [50] The hybrid application of an inductive learning method and a neural network for intelligent information retrieval
    Cortez, EM
    Park, SC
    Kim, S
    INFORMATION PROCESSING & MANAGEMENT, 1995, 31 (06) : 789 - 813