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

Minshui Huang,Xifan Li,Yongzhi Lei

doi:10.1155/2019/5954104

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.

Highlights

Structural health monitoring (SHM) has received much attention in recent years due to its importance in transportation service and building structure safety, which offers important economic benefits to human society
In order to improve its performance in structural damage identification (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
The application of the aforementioned method is based on the need for providing a large amount of experimental data, but there is a widely recognized obstacle of the data-based approach—feature extraction has been found unstable under inaccurate data conditions [10,11,12,13]

Summary

Introduction

Structural health monitoring (SHM) has received much attention in recent years due to its importance in transportation service and building structure safety, which offers important economic benefits to human society. A key technology named swarm intelligence (SI) optimization algorithms is introduced to overcome the drawbacks of the traditional optimization methods and considered to handle complex structural damage identification problems. En, the mode shape was introduced to enhance the accuracy of damage location [38]; at the same time, the method of selecting a regularization parameter was proposed [39] It seems that the sparse recovery theory has a good effect on damage detection. To overcome the drawback of basic BBPSO, such as local optimal, slow convergence, and lower computing efficiency, a novel optimization algorithm, which is named bare bones particle swarm optimization with double jump (BBPSODJ), is first proposed here; a double jump strategy is incorporated to solve the problem of local optima state in the process of iteration and keep the diversity of particles. The method proposed is adopted to identify the damage of an experimental example of 3-story steel frame structure, whose performances show that BBPSODJ can determine the location of damage and quantify the severity

Sparse Damage Identification Model

Bare Bones Particle Swarm Optimization with Double Jump

Damage Identification Using Numerical Example

Experiment Example

Conclusions