An Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower. (1st September 2021)

• Record Type: Journal Article
• Authors: Minh, Hoang-Le; Khatir, Samir; Abdel Wahab, Magd; Cuong-Le, Thanh
• Is Part Of: Engineering structures. Volume 242(2021)
• Publication Date: 2021-09-01
• Subjects: SAP2000 (OAPI) -- Model updating -- Structural health monitoring, structural damage detection -- Particle swarm optimization; 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.2021.112412
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in structures. Damage assessment of Electric Power Transmission with 44.05 m height. Open Application Programming Interface (OAPI) source code making use of SAP2000 and MATLAB. High accuracy and reliability of the results obtained using EHVPSO. Abstract: In this paper, a novel enhanced Particle Swarm Optimization (PSO) algorithm is introduced for solving damage identification problems. For the first time, the algorithm is applied to a complex structure, namely an Electric Power Transmission with 44.05 m height. The process of structural damage assessment is implemented using SAP2000 commercial software combined with MATLAB. Using the Open Application Programming Interface (OAPI) source code, which is available in SAP2000, a strong MATLAB environment program has been developed in this research. This program can allow the user to adjust the initial model's parameters in SAP2000 to create a continuous two ways data exchange between SAP2000 and MATLAB. Thus, the process of detecting the location and level of damage in the structure is performed by applying a new version of PSO, namely Enhancing Particle Swarm Optimization Algorithm (EHVPSO), using stochastic parameters. The key factor in the EHVPSO algorithm is to introduce two novel equations. The first equation is used to control the convergence rate in each movement of particle i th, and the second equation is used to control the balance between local optimal value and global optimal value. The results demonstrate that the proposed algorithm can detect damage with very high accuracy and reliability.