A hybrid ant lion optimizer with improved Nelder–Mead algorithm for structural damage detection by improving weighted trace lasso regularization

Abstract

Structural damage detection is the kernel technique in deploying structural health monitoring. The structural damage–detection technique using heuristic algorithms has been developed at an astounding pace over the past years. However, some existing structural damage–detection methods are prone to easily fall into the local optimum and to be unstable when they are applied to complex structures. In order to make full use of advantages of heuristic algorithms and overcome abovementioned shortcomings, a hybrid algorithm, which combines the ant lion optimizer with an improved Nelder–Mead algorithm, is proposed to solve the constrained optimization problem of complex structural damage detection. First, an objective function is established for damage identification using structural modal parameters, that is, frequencies and mode shapes. The solution to the objective function is accurately attained by a newly improved weighted trace lasso which can improve the computing performance and stability of procedure and reduce randomness of weighted coefficients. After assessing the computing performance of the proposed hybrid algorithm using three classical mathematical benchmark functions, two structural damage–detection numerical simulations and a laboratory verification are then conducted to fully assess the structural damage–detection capability of the proposed method. Meanwhile, the equivalent element stiffness-reduction model is introduced to estimate the real damage severities of cracks which are created in laboratory structures and to compare with the structural damage–detection results by the proposed method. The illustrated results show that the proposed hybrid algorithm can locate damage and quantify damage severity more accurately and stably with a good robustness to noise.

Ant Lion Optimization: Variants, Hybrids, and Applications

“…Also, Li et al [119] proposed a novel version of ALO by replacing roulette wheel with new technique which classify antlions to 2 groups then, some novel equations are used to breakthrough steps to enhance diverse exploration and thorough exploitation in each group. In [120] Yu proposed an improved version of antlion by hybridizing ALO with Nelder-Mead algorithm and apply it to detect structural damage by improving weighted trace lasso regularization. Kilic et al [121] introduced IALO algorithm by using the absolute value of fitness value before applying roulette wheel selection.…”

“…They applied their algorithm in seismically excited nonlinear buildings. Also, In [120] Yu proposed ALO-INM algorithm which is a hybridized algorithm between ALO and improved Nelder-Mead algorithm.…”

Deep learning based thermal crack detection on structural concrete exposed to elevated temperature

“…During the last decades, there are number of computer vision based methods developed to overcome the disadvantages of traditional investigations (Andrushia et al, 2019; Andrushia and Anand, 2020; Dai et al, 2019). These techniques use image-processing techniques to detect structural damages such as spalling, cracks and exposed aggregate (Beheshti Aval et al, 2020; Cha et al, 2016, 2017; Chen and Yu, 2020).…”

A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization

“…In terms of hybridization, several examples are available in the literature which adopts NM algorithm. Some of them are firefly algorithm for optimal reactive power dispatch [13], particle swarm optimization for modeling Li-ion batteries for electric vehicles [14], Harris hawks optimization for solving design and manufacturing problems [15], ant lion optimizer algorithm for structural damage detection [16], moth flame optimization for parameter identification of photovoltaic modules [17] and artificial electric field algorithm for optimization problems [18].…”