Meta-heuristic algorithms: an appropriate approach in crack detection

被引:2
|
作者
Ghannadiasl, Amin [1 ]
Ghaemifard, Saeedeh [1 ]
机构
[1] Univ Mohaghegh Ardabili, Dept Civil Engn, Ardebil 5619911367, Iran
关键词
Meta-heuristic algorithms; Inverse problems; Artificial intelligence; Objective functions; Optimization; Damage detection; Crack; STRUCTURAL DAMAGE IDENTIFICATION; DIFFERENTIAL EVOLUTION ALGORITHM; CUCKOO OPTIMIZATION ALGORITHM; ANT COLONY OPTIMIZATION; BEAM-LIKE; GENETIC ALGORITHM; COMBINATORIAL OPTIMIZATION; SEARCH ALGORITHM; TRUSS STRUCTURES; METAHEURISTICS;
D O I
10.1007/s41062-024-01583-6
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A structural fault is a significant factor that represents a safety hazard in the proper functioning of the structure. Thus, the crack should be detected and remade before the diffusion and demolition of a structure. One of the famous techniques to identification damage and fault, are the optimization-based methods. In these methods, by an optimization algorithm within an iterative process an objective subordinates minimized. For damage recognizing methodology, these optimization algorithms have a remarkable efficient so that a broad type of them are utilized in identification problems. In other words, these algorithms can find acceptable solutions to optimize complex problems. Therefore, these heuristic algorithms use in various optimization problems like structures. The recognition of structural degradation has attracted the attention of many researchers in the past decades. Hence, the progress of crack detection using meta-heuristic algorithms and artificial intelligence is investigated in all types of structures, such as beams in this paper. This paper is purposed to help researchers in acquaintance performance of algorithms for structural damage detection and to develop more reliable and applicable methods for civil engineering structures in the future.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] A meta-heuristic approach for improving the accuracy in some classification algorithms
    Huy Nguyen Anh Pham
    Triantaphyllou, Evangelos
    COMPUTERS & OPERATIONS RESEARCH, 2011, 38 (01) : 174 - 189
  • [2] Affine invariance of meta-heuristic algorithms
    Jian, ZhongQuan
    Zhu, GuangYu
    INFORMATION SCIENCES, 2021, 576 : 37 - 53
  • [3] Reviews of the meta-heuristic algorithms for TSP
    Gao, Hai-Chang
    Feng, Bo-Qin
    Zhu, Li
    Kongzhi yu Juece/Control and Decision, 2006, 21 (03): : 241 - 247
  • [4] A unified approach to parameter selection in meta-heuristic algorithms for layout optimization
    Kaveh, A.
    Farhoudi, N.
    JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, 2011, 67 (10) : 1453 - 1462
  • [5] K-means and meta-heuristic algorithms for intrusion detection systems
    Maazalahi, Mahdieh
    Hosseini, Soodeh
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2024, 27 (08): : 10377 - 10419
  • [6] An ensemble approach to meta-heuristic algorithms: Comparative analysis and its applications
    Singh, Priyanka
    Kottath, Rahul
    COMPUTERS & INDUSTRIAL ENGINEERING, 2021, 162
  • [7] Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach
    Abdolreza Asadi Ghanbari
    Hossein Alaei
    Applied Intelligence, 2021, 51 : 646 - 657
  • [8] Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach
    Ghanbari, Abdolreza Asadi
    Alaei, Hossein
    APPLIED INTELLIGENCE, 2021, 51 (02) : 646 - 657
  • [9] Image Segmentation Using Meta-heuristic Algorithms
    Saxena, Varun
    Goel, Deeksha
    Rawat, Tarun Kumar
    2018 INTERNATIONAL CONFERENCE ON COMPUTING, POWER AND COMMUNICATION TECHNOLOGIES (GUCON), 2018, : 661 - 666
  • [10] Significance Relations for the Benchmarking of Meta-Heuristic Algorithms
    Koeppen, Mario
    Ohnishi, Kei
    2013 13TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA), 2013, : 253 - 258