Performance of Ant Lion Optimization and Artificial Bee Colony Algorithm for Structural Health Monitoring of ASCE Benchmark Structure (original) (raw)
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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.
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.
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.
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.
international journal of advanced design and manufacturing technology, 2021
The Non-destructive vibration based structural damage detection techniques have been developed in the recent decades. They are usually converted into a mathematical optimization problem that should be solved using optimization algorithms. In this paper, a new hybrid algorithm, using a particle swarm - genetic optimization, is proposed that is called Swarm Life Cycle Algorithm (SLCA). Additionally, Modified Total Modal Assurance Criterion (MTMAC) that is modal based and involved natural frequencies and mode shapes, is used as an objective function. A cantilever beam is modelled and simulated using finite element method as a numerical case study with several different damage scenarios. To compare the effectiveness of the proposed algorithm with GA and PSO, they are applied to detect the locations and severities of damages of numerical cases separately. To assess the robustness of them, the effects of environmental noise, coordinate and mode incompleteness on the accuracy of damage det...
Structural damage detection using MAC-Fast Multi Swarm Optimization technique (MAC-FMSO)
MATEC Web of Conferences
Damage detection of structures is important to ensure the service of civil engineering infrastructures. Many techniques have been developed for damage detection of structures in the literature. In this paper a new technique, a so called MAC-FMSO technique, is developed by using a combination of Modal Assurance Criterion (MAC) and Fast Multi Swarm Optimization (FMSO) techniques. By using this method, not only the damage location can be assured, but also the loss of strength and loss of stiffness of structures might be quantified. The first stage is to obtain the damaged member's location by using second level of damage detection techniques, such as mode shape curvature method, damage locating vector, or strain energy method. After obtaining the damaged member, the MAC-FMSO technique is used to obtain the loss of stiffness from the candidate of damaged members. The results show that the MAC-FMSO algorithm has the ability to predict the loss of stiffness in the damaged members. In this case, the MAC-FMSO technique can be considered as an efficient and effective tool at the third level of damage detections. This is because this technique successfully measures the loss of strength or loss of stiffness of the damage member or part of structures. At the end of the paper, the MAC-FMSO technique is applied to predict the location of damage and loss of stiffness of three different structures. The first application is a simply supported beam with three different damage scenarios; the second application is a 13-bar plane truss structure with two different scenarios, and the third application is a shear building structure with three different scenarios. The results of simulation show that the MAC-FMSO technique can accurately predict the loss of stiffness of the damaged member as well as the location of the damage.
Structural Health Monitoring - Measurement Methods and Practical Applications
2017
This chapter presents an improved multi-particle swarm co-evolution optimization algorithm (IMPSCO) to detect structural damage. Firstly, IMPSCO is integrated with Newmark's algorithm for damage detection and system identification, which just need few sensors. In addition, for reducing the searching parameters, a two-stage damage detection method based on modal strain energy and IMPSCO is presented. In order to validate the proposed method, a seven-story steel frame experiment in laboratory conditions is performed and a comparison is made between the proposed approach and genetic algorithm (GA). The results show that: (1) the proposed methods can not only effectively locate damage location but also accurately quantify the damage severity. Besides, it has excellent noise-tolerance and adaptability; (2) for integrating IMPSCO and Newmark's algorithm, it implements only by using any kinds of structural time-series responses and the excitation force; (3) compared with genetic algorithm, IMPSCO is more efficient and robust for damage detection with a better noise-tolerance.
Journal of Vibroengineering, 2016
Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm.
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...
Procedia Engineering
Structural Health Monitoring (SHM) of historical building is an emerging field of research aimed at the development of strategies for on-line assessment of structural condition and identification of damage in the earliest stage. Built heritage is weak against operational and environmental condition and preservation must guarantee minimum repair and non-intrusiveness. SHM provides a cost-effective management and maintenance allowing prevention and prioritization of the interventions. Recently, in computer science, mimicking nature to address complex problems is becoming more frequent. Nature-inspired approaches turn out to be extremely efficient in facing optimization, commonly used to analyze engineering processes in SHM, providing interesting advantages when compared with classic methods. This paper begins with an introduction to Natural Computing. Then, focusing on its applications to SHM, possible improvements in built heritage conservation are shown and discussed suggesting a general framework for safety assessment and damage identification of existing structures.