Ant lion optimisation algorithm for structural damage detection using vibration data (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.
SN Applied Sciences
Structural health monitoring is crucial for the timely damage diagnosis of civil infrastructure. This paper explores the damage detection method based on the ant colony algorithm (ACO) by using Hooke-Jeeves (HJ) pattern search for intensification. The HJ is incorporated into the ACO to improve its performance in detecting damages. The damage is simulated by reducing the stiffness of the structural members, via elastic modulus reduction factor. Four civil engineering structures of varying complexity are analysed for low-and high-level damage scenarios to test the efficacy of the proposed approach. An inverse problem is formulated to minimise the objective function based on the frequency response function rather than using the frequency and mode-shape-based approach. The analysis results indicate that the proposed method can locate damages and identify their severity with higher precision than previously used GA, SPSO, and UPSO can.
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...
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.
Identification of Damage in Composite Beam using Ant Lion Optimizer
Second ASCE India Conference on “Challenges of Resilient and Sustainable Infrastructure Development in Emerging Economies” (CRSIDE2020), 2020
In this present study, a metaheuristic-based algorithm named ant lion optimizer (ALO) is performed combined with eigenvalue obtained from numerical simulation of the composite beam for detection of the damage. Fibre reinforced plastic (FRP) composite beams have been modeled using finite element method. Free vibration analysis of the beam is carried out for both undamaged and damaged beam to acquire the modal data such as natural frequencies and mode shapes, which is the baseline data used in the ALO algorithm in MATLAB. Since, damage in any structure causes loss of stiffness, in the present study, the damage is induced in the beam by reducing the stiffness of the beam, which is achieved by reducing the thickness of the beam at specified locations. The ALO algorithm minimizes the differences between the vibrational responses of the baseline model and those acquired from the finite element models of the composite structures. The objective function based on frequencies of the composite beam is formulated. It is observed that the ALO algorithm is satisfactory to detect the location and severity of the damage.
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.
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...
Applied Sciences
Dynamic properties such as natural frequencies and mode shapes are directly affected by damage in structures. In this paper, changes in natural frequencies and mode shapes were used as the input to various objective functions for damage detection. Objective functions related to natural frequencies, mode shapes, modal flexibility and modal strain energy have been used, and their performances have been analyzed in varying noise conditions. Three beams were analyzed: two of which were simulated beams with single and multiple damage scenarios and one was an experimental beam. In order to do this, SAP 2000 (v14, Computers and Structures Inc., Berkeley, CA, United States, 2009) is linked with MATLAB (r2015, The MathWorks, Inc., Natick, MA, United States, 2015). The genetic algorithm (GA), an evolutionary algorithm (EA), was used to update the damaged structure for damage detection. Due to the degradation of the performance of objective functions in varying noisy conditions, a modified objective function based on the concept of regularization has been proposed, which can be effectively used in combination with EA. All three beams were used to validate the proposed procedure. It has been found that the modified objective function gives better results even in noisy and actual experimental conditions.
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.