An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. (15th November 2019)

• Record Type: Journal Article
• Authors: Tran-Ngoc, H.; Khatir, S.; De Roeck, G.; Bui-Tien, T.; Abdel Wahab, M.
• Is Part Of: Engineering structures. Volume 199(2019)
• Publication Date: 2019-11-15
• Subjects: Artificial neural network (ANN) -- Local minima -- Evolutionary algorithm (EA) -- Cuckoo search (CS) -- Particle swarm optimization (PSO) -- Genetic algorithm (GA) -- Training parameters -- Damage detection -- Model updating; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2019.109637
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Damage detection in structures by applying using artificial neural network (ANN) and cuckoo search (CS) algorithm. Structural damage localization and quantification in bridges and beam-like structures. Improve ANN training parameters using CS. ANN combined with CS (ANN-CS) is accurate and cost effective. Abstract: This paper presents a new approach for damage detection in structures by applying a flexible combination based on an artificial neural network (ANN) and cuckoo search (CS) algorithm. ANN has become one of the most powerful tools employing computational intelligence techniques to tackle complex problems in numerous fields. However, due to the application of backpropagation algorithms based on gradient descent, a major drawback of ANN is the common problem of local minima that acts as a great hindrance to the search for the best solution. To overcome this disadvantage, we propose to combine ANN with evolutionary algorithms based on global search techniques. This paper employs CS to improve ANN training parameters (weight and bias) by minimizing the difference between real and desired outputs and then using these parameters to generate the network. Two numerical models, comprising a steel beam calibrated using experimental measurements and a large-scale truss bridge, are used to assess the robustness of the proposed approach. The results demonstrate that ANN combined with CS (ANN-CS) is accurate and requires a lower computational time than ANN, and evolutionary algorithm (EA) alone in terms of structural damage localization and quantification.