Particle swarm optimization with sequential niche technique for dynamic finite element model updating

Peer reviewedPostprin

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

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 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 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

Structural Damage Detection Based on Improved Multi-Particle Swarm Co-Evolution Optimization Algorithm

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

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

A hybrid heuristic optimization algorithm PSOGSA coupled with a hybrid objective function using ECOMAC and frequency in damage detection

Presence of damage leads to variation in modal properties of observed structures. The majority of studies use the changes in natural frequencies for damage detection. The reason is that the frequencies are often easily measurable with high accuracy by using reasonable sensors. However, frequencies are more sensitive to environmental effects, such as temperature, in comparison with mode shapes. Besides, defects in symmetric structures can cause the same changes in frequency. In contrast, mode shapes are more sensitive to local damage because they own local information and are independent of symmetric characteristics. These make mode shapes have dominant advantages in detecting nonlinear and multiple damage. ECOMAC is an index derived from mode shapes. It is a fact that these indices are not always possible to detect faults successfully in structures. Therefore, in this paper, a hybrid optimization algorithm, particle swarm optimization – gravitational search algorithm, namely PSOGSA, is used to improve the accuracy of infect detection using a hybrid objective function combined ECOMAC and frequency based on the inverse problem. Numerical studies of a two-span continuous beam, a simply supported truss, and a free-free beam, are utilized to verify the effectiveness and reliability of the proposal. From the obtained results, the proposed approach shows high potential in damage identification for different structures

Damage identification in steel plate using FRF and inverse analysis

Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA).  The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance.  Both in convergence speed and CPU time

Finite element model updating for composite plate structures using particle swarm optimization algorithm

In the Architecture, Engineering, and Construction (AEC) industry, particularly civil engineering, the Finite Element Method (FEM) is a widely applied method for computational designs. In this regard, computational simulation has increasingly become challenging due to uncertain parameters, significantly affecting structural analysis and evaluation results, especially for composite and complex structures. Therefore, determining the exact computational parameters is crucial since the structures involve many components with different material properties, even removing some additional components affects the calculation results. This study presents a solution to increase the accuracy of the finite element (FE) model using a swarm intelligence-based approach called the particle swarm optimization (PSO) algorithm. The FE model is created based on the structure’s easily observable characteristics, in which uncertainty parameters are assumed empirically and will be updated via PSO using dynamic experimental results. The results show that the finite element model achieves high accuracy, significantly improved after updating (shown by the evaluation parameters presented in the article). In this way, a precise and reliable model can be applied to reliability analysis and structural design optimization tasks. During this research project, the FE model considering the PSO algorithm was integrated into an actual bridge’s structural health monitoring (SHM) system, which was the premise for creating the initial digital twin model for the advanced digital twinning technologyThis work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. The authors also acknowledge ANI (“Agência Nacional de Inovação”) for the financial support given to the R&D Project “GOA Bridge Management System—Bridge Intelligence”, with reference POCI-01-0247-FEDER-069642, cofinanced by the European Regional Development Fund (FEDER) through the Operational Competitiveness and Internationalization Program (POCI).Minh Q. Tran was supported by the doctoral grant reference PRT/BD/154268/2022 financed by the Portuguese Foundation for Science and Technology (FCT), under the MIT Portugal Program (2022 MPP2030-FCT). Minh Q. Tran acknowledges Huan X. Nguyen (Faculty of Science and Technology, Middlesex University, London NW4 4BT, UK) and Thuc V. Ngo (Mien Tay Construction University, Institute of Science and International Cooperation, 85100 Vĩnh Long, Vietnam) for their support as cosupervisors as well as specific suggestions in terms of the “conceptualization” and “methodology” of this paper. Helder S. Sousa acknowledges the funding by FCT through the Scientific Employment Stimulus—4th Editio

Boundary Strategy for Optimization-based Structural Damage Detection Problem using Metaheuristic Algorithms

The present paper proposes a new strategy namely Boundary Strategy (BS) in the process of optimization-based damage detection using metaheuristic algorithms. This strategy gradually neutralizes the effects of structural elements that are healthy in the optimization process. BS causes the optimization method to find the optimum solution better than conventional methods that do not use the proposed BS. This technique improves both aspects of the accuracy and convergence speed of the algorithms in identifying and quantifying the damage. To evaluate the performance of the developed strategy, a new damage-sensitive cost function, which is defined based on vibration data of the structure, is optimized utilizing the Shuffled Shepherd Optimization Algorithm (SSOA). Different examples including truss, beam, and frame are investigated numerically in order to indicate the applicability of the proposed technique. The proposed approach is also applied to other well-known optimization algorithms including TLBO, GWO, and MFO. The obtained results illustrate that the proposed method improves the performance of the utilized algorithms in identifying and quantifying of the damaged elements, even for noise-contaminated data

The doctoral research abstracts. Vol:7 2015 / Institute of Graduate Studies, UiTM

Foreword: The Seventh Issue of The Doctoral Research Abstracts captures the novelty of 65 doctorates receiving their scrolls in UiTM’s 82nd Convocation in the field of Science and Technology, Business and Administration, and Social Science and Humanities. To the recipients I would like to say that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into high impact innovative research for the community and country from knowledge gained during this academic journey. As alumni of UiTM, we will always hold you dear to our hearts. A new ‘handshake’ is about to take place between you and UiTM as joint collaborators in future research undertakings. I envisioned a strong research pact between you as our alumni and UiTM in breaking the frontier of knowledge through research. I wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ / Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR