Structural health monitoring based on the hybrid ant colony algorithm by using Hooke–Jeeves pattern search (original) (raw)
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Structural Damage Detection Based on Modal Parameters Using Continuous Ant Colony Optimization
Advances in Civil Engineering, 2014
A method is presented to detect and quantify structural damages from changes in modal parameters (such as natural frequencies and mode shapes). An inverse problem is formulated to minimize the objective function, defined in terms of discrepancy between the vibration data identified by modal testing and those computed from analytical model, which then solved to locate and assess the structural damage using continuous ant colony optimization algorithm. The damage is formulated as stiffness reduction factor. The study indicates potentiality of the developed code to solve a wide range of inverse identification problems.
Ant lion optimisation algorithm for structural damage detection using vibration data
Journal of Civil Structural Health Monitoring, 2018
Structural damage assessment is crucial for structural health monitoring to evaluate the safety and residual service life of the structure. To solve the structural damage detection problem, various optimisation techniques have been in use. However, they fail to identify damage and are prone to converge to local optima for improper tuning of algorithm-specific parameters, which are problem specific. In this study, the recently proposed ant lion optimiser, which is a population-based search algorithm, mimicked the hunting behaviour of antlions, was used for assessing structural damage. The objective function for damage detection was based on vibration data, such as natural frequencies and mode shapes. The effectiveness of the proposed technique was evaluated against several benchmark problems with different damage settings. The results indicate that the proposed algorithm required fewer parameters than other metaheuristic algorithms to identify the location and extent of damage.
Applied Mathematics and Computation, 2012
A method is presented to detect and assess structural damages from changes in natural frequencies using ant colony optimization (ACO) algorithm. It is possible to formulate the inverse problem in terms of optimization and then to utilize a solution technique employing ACO to assess the damages of structures using natural frequencies. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic way. The developed code is used to assess damages of truss like structures using first few natural frequencies. The outcomes of the results show that the developed method can detect and estimate the amount of damages with satisfactory precision.
Proceedings of the 12th Structural Engineering Convention, SEC 2022: Themes 1-2
Structural health Monitoring (SHM) has been a fast-moving tread for monitoring the health status of civil engineering structures. The SHM strategy involves application of different techniques involving the use of modal parameters, such as natural frequencies and mode shapes, to detect and localize the damage. Though localization of damage in structure plays an important role, damage quantification, which helps in performing repair works, is also essential. However, very few algorithms have been developed which calculates the amount of damage in structure. In view to this situation, the swarm-based optimization algorithms have been developed which can detect the damage severity in a structure. Some of the algorithms developed so far are Grey Wolf Algorithm (GWO), Artificial Bee Colony (ABC) algorithm, Firefly algorithm, Ant Lion Optimization (ALO) algorithm and others. Out of these algorithms, ALO and ABC have been used in limited number of cases for performing SHM. No real-life stru...
2010
Structural systems in a variety of applications including aerospace vehicles, automobiles and engineering structures such as tall buildings, bridges and offshore platforms, accumulate damage during their service life. The approach used in this investigation is one where the structural properties of the analytical model are varied to minimize the difference between the analytically predicted and empirically measured response. This is an inverse problem where the structural parameters are identified. In this work a reduced number of vibration modes and nodal displacements were used as the measured response. For the damage assessment problem a finite element model of the structural system is available and the model of the damaged structure will be identified. Damage will be represented by a reduction in the elastic stiffness properties of the system. In this investigation, the Differential Evolution (DE) and the Ant Colony Optimization (ACO) were applied to simple truss structures with...
Performance Comparison among Vibration Based Indicators in Damage Identification of Structures
Applied Mechanics and Materials, 2014
A simple and robust methodology is presented to identify damages in a structure using changes in vibration data. A comparison is made among damage indicators such as natural frequencies, mode shape data, curvature damage factors and flexibility matrices to study their efficacy in damage assessment. Continuous ant colony optimization (ACOR) technique is used to solve the inverse problem related to damage identification. The outcome of the simulated results demonstrates that the flexibility matrix as a damage indicator provides better damage identification.
The study presented herein compares the performance of structural damage detection using artificial neural networks (ANNs) and least square support vector machines (LS-SMVs). Structural response signals under ambient vibration are processed according to wavelet energy spectrum for feature extraction. The feature vectors are used as inputs to both classifiers based on ANNs and LS-SVMs. LS-SVM parameters along with the selection of input features are optimised using particle swarm harmony search (PSHS) algorithm with a distance evaluation fitness function. The PSHS that has been introduced in this paper is a new hybrid meta-heuristic algorithm for improving the accuracy and the convergence rate of harmony search (HS) algorithm. The effectiveness of different feature extraction methods and different optimisation algorithms are investigated. This investigation shows that although performance of both classifiers is improved by employing PSHS-based selection, for most cases considered, the classification accuracy of LS-SVM is better than ANN. Furthermore, the results demonstrate the efficiency and the robustness of PSHS.
A predator-prey optimization for structural health monitoring problems
MATEC Web of Conferences
Monitoring a structure using permanent sensors has been one of the most interesting topics, especially with the increase of the number of aging structures. Such a technique requires the implementation of sensors on a structure to predict the condition states of the structural elements. However, due to the costs of sensors, one must judiciously install few sensors at some defined locations in order to maximize the probability of detecting potential damages. In this paper, we propose a methodology based on a genetic algorithm of type predator-prey with a Bayesian updating of the structural parameters, to optimize the number and location of the sensors to be placed. This methodology takes into consideration all uncertainties related to the degradation of the elements, the mechanical model and the accuracy of sensors. Starting with two initial populations representing the damages (prey) and the sensors (predator), the genetic algorithm evolves both populations in order to converge towar...
2011
An efficient optimization procedure is proposed to detect multiple damage in structural systems. Natural frequency changes of a structure are considered as a criterion for damage presence. In order to evaluate the required natural frequencies, a finite element analysis (FEA) is utilized. A modified genetic algorithm (MGA) with two new operators (health and simulator operators) is presented to accurately detect the locations and extent of the eventual damage. An efficient correlation-based index (ECBI) as the objective function for the optimization algorithm is also introduced. The numerical results of two benchmark examples considering the measurement noise demonstrate the computational advantages of the proposed method to precisely determine the sites and the extent of multiple structural damage.
Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring
Journal of Civil Engineering and Construction, 2020
In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one of the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of the procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms.