Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

Abstract

Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

A New Stochastic Model Updating Method Based on Improved Cross-Model Cross-Mode Technique

“…. ) are the deterministic coefficients of the expansions in Equation (15) and Equation (16), respectively. Taking into account that ξ j = ξ, and substituting Equations ( 13)-( 16) into Equations ( 9) and ( 12), respectively, one can have…”

“…At present, the updating of FE model is mainly to adjust the stiffness matrix and mass matrix in the FE model, or to update the physical parameters in these matrices, so that the updated model can be used to reasonably predict the structural real mechanical behaviors. By now, many deterministic model updating methods have been developed, which mainly include optimization based model updating methods [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ], or FE based model updating methods [ 8 , 9 , 10 , 11 , 12 ], Bayesian model updating methods and recently developed model updating methods based on artificial intelligence [ 13 , 14 , 15 ].…”

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization

“…Regularization technology, as an excellent tool, has been widely applied in solving the problem of underdetermined equation in damage identification [ 10 ]. Assuming the change of modal parameters caused by damage is linear, it can be expressed as: where denotes the sensitivity coefficient matrix, which can be calculated according to Equation (37); is the combination of the stiffness reduction vectors of elemental damage and bearing damage; means the change of eigenvalue.…”

“…However, because the dimensions of measured modal information are far less than the degree of freedoms of the structure, the methods of sensitivity analyses have to face the problem of equation underdetermined [ 10 ]. This issue will cause infinite solutions, which can be overcome by adopting the regularization technology.…”

Optimization of Historic Building Survey Technology under Artificial Intelligence Wireless Network Technology Environment

“…In this way, it can be judged whether the structure is damaged. The current health status of the building structure is obtained [6][7][8].…”