A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area

Damage Diagnosis of Structures Using Modal Data and Static Response

This paper is aimed at presenting three methods to detect and estimate damage using modal data and static response of a damaged structure. The proposed methods use modal data with and without noise or static displacement to formulate objective functions. Damage location and severity in structural elements are determined using optimization of the objective functions by the simulated annealing algorithm. These methods have been applied to three examples, namely a three-story plane frame, cantilever plate and benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring. Also, the effect of the discrepancy in mass and stiffness between the finite element model and the actual tested system has been investigated. The obtained results indicate that the proposed methods can be viewed as a powerful and reliable method for structural damage detection and estimation

Damage quantification method using artificial neural network and static response with limited sensors

In this paper an effective method for structural damage identification via incomplete static response and artificial neural network is proposed. The presented method is based on the condensed stiffness matrices to formulate incomplete static responses as input parameters to the Feed-forward back propagation neural network. The performance of the proposed method for damage detection and estimation has been investigated using three examples, namely, two-span continuous beam, plane steel bridge and two story frame with and without noise in the static displacements containing several damages. Also, the effect of the discrepancy in stiffness between the finite element model and the actual tested system has been investigated. The obtained results indicate that the proposed method perform quite well in spite of the incomplete data and modeling errors

Process control for WAAM using computer vision

This study is mainly about the vision system and control algorithm programming for wire arc additive manufacturing (WAAM). Arc additive manufacturing technology is formed by the principle of heat source cladding produced by welders using molten inert gas shielded welding (MIG), tungsten inert gas shielded welding (TIG) and layered plasma welding power supply (PA). It has high deposition efficiency, short manufacturing cycle, low cost, and easy maintenance. Although WAAM has very good uses in various fields, the inability to control the adding process in real time has led to defects in the weld and reduced quality. Therefore, it is necessary to develop the real-time feedback through computer vision and algorithms for WAAM to ensure that the thickness and the width of each layer during the addition process are the same