3,417 research outputs found

    Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

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    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

    An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings

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    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.This work was financed by FEDER funds through the Competitiveness Factors Operational Programme COMPETE and by national funds through FCT - Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007633info:eu-repo/semantics/publishedVersio

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

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    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

    Emerging trends in optimal structural health monitoring system design: From sensor placement to system evaluation

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    This paper presents a review of advances in the field of Sensor Placement Optimisation (SPO) strategies for Structural Health Monitoring (SHM). This task has received a great deal of attention in the research literature, from initial foundations in the control engineering literature to adoption in a modal or system identification context in the structural dynamics community. Recent years have seen an increasing focus on methods that are specific to damage identification, with the maximisation of correct classification outcomes being prioritised. The objectives of this article are to present the SPO for SHM problem, to provide an overview of the current state of the art in this area, and to identify promising emergent trends within the literature. The key conclusions drawn are that there remains a great deal of scope for research in a number of key areas, including the development of methods that promote robustness to modelling uncertainty, benign effects within measured data, and failures within the sensor network. There also remains a paucity of studies that demonstrate practical, experimental evaluation of developed SHM system designs. Finally, it is argued that the pursuit of novel or highly efficient optimisation methods may be considered to be of secondary importance in an SPO context, given that the optimisation effort is expended at the design stage

    Metaheuristic algorithms for damage identification in real sized structures

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    107 σ.Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.)Ο σκοπός αυτής της εργασίας είναι η εφαρμογή μεταευρετικών αλγορίθμων για την αναγνώριση ζημιών σε ρεαλιστικές, όσον αφορά το μέγεθος , την απόκριση των μελών και τον τρόπο προσέγγισης ιδιοτιμών, ( μια περίπτωση ενός διώροφου μεταλλικού κτιρίου εξετάζεται προσεγγίζοντας τις ιδιοσυχνότητες με τη μέθοδο των υποφορέων) κατασκευές πολιτικού μηχανικού καθώς και να επανεξετάσει τις βασικές θεωρίες και υποθέσεις. Οι δύο τεχνικές για την αναγνώριση ζημιών προτείνονται. Το πρόβλημα της αναγνώρισης ζημιών αποτελεί αντίστροφο πρόβλημα , όπου μπορεί κανείς να αναμένει πολλαπλές λύσεις. Προτείνεται , ένας αλγόριθμος διακριτών τιμών ώστε να ελέγχεται ο μέγιστος αριθμός βλαμμένων στοιχείων για την αναζήτηση . Όταν το μέγεθος ή / και ο αριθμός των ζημιών αυξάνει οι υπάρχουσες μέθοδοι ( κυρίως ευαισθησίας μεθόδων που απορρέουν από την πρώτη θεωρία διαταραχών) παράγουν περισσότερες ζημιές , από αυτές που υπετέθησαν . Μια τεχνική χρησιμοποιώντας τον χώρο του πυρήνα του πίνακα ευαισθησίας (ο οποίος θεωρείται συνάρτηση των συντελεστών ζημιάς) προτείνεται έτσι ώστε να μπορεί κανείς να παρακολουθεί τις πολλαπλές λύσεις βρίσκοντας σενάρια με λιγότερα βλαμμένα στοιχεία .The scope of this thesis is to apply metaheuristic algorithms for damage identification in realistic regarding size, member response and eigenvalue approximation (a case of a two-storey steel frame building is examined approximating the eigenvalues via substructuring) civil engineer structures as well as reviewing some of the basic theories and assumptions made. Two techniques for damage identification are proposed. The problem of damage identification is an inverse problem where one may expect multiple solutions. A discrete value algorithm is proposed in order to control the maximum number of damaged elements for the search. When size and/or number of damages increases the existing methods (mainly sensitivity methods derived from first order perturbation theory) produce more damages then the ones alleged. A technique using the null space of the sensitivity matrix (which is considered a function of the damage factors) is proposed so one can track the multiple solutions finding cases with fewer damaged elements.Σταύρος Ε. Χατζηελευθερίο

    A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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    Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

    Clustering Elements of Truss Structures for Damage Identification by CBO

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    The number of structural elements plays a significant role in detecting damage location and severity; such methods have sometimes failed to provide correct solutions due to the entrapment of damage detection algorithms in the local optimum. To resolve this problem, this study proposed the simultaneous use of mathematical and statistical methods to narrow down the search space. To this end, a two-step damage detection method was proposed. In the first step, the structural elements were initially divided into different clusters using the k-means method. Subsequently, the possibly damaged elements of each cluster were identified. In the second step, the elements selected in the first step were placed in a new set, and a process was applied to identify their respective damage location and severity. Thus, the proposed method reduced the search space as well as the possibility of entrapment in the local optimum. Other advantages of the proposed method include the use of fewer dynamic properties. Accordingly, by narrowing down the search space and the dimensions of the system for governing equations, the proposed method could significantly increase the chance of obtaining favorable results in structures with many elements and those with few vibration modes. A meta-heuristic method, called the colliding bodies optimization (CBO), was used in the proposed damage detection optimization algorithm. The optimization problem was based on the modal strain energy equations. According to the results, the proposed method was able to detect the location and severity of damage, even at its slightest percentage

    Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

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    © 2016 Elsevier B.V. Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator

    The doctoral research abstract. Vol:9 2016 / Institute of Graduate Studies, UiTM

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    FOREWORD: Seventy three doctoral graduands will be receiving their scroll today signifying their achievements in completing their PhD journey. The novelty of their research is shared with you through The Doctoral Abstracts on this auspicious occasion, UiTM 84th Convocation. We are indeed proud that another 73 scholarly contributions to the world of knowledge and innovation have taken place through their doctoral research ranging from Science and Technology, Business and Administration, and Social Science and Humanities. As we rejoice and celebrate your achievement, we would like to acknowledge dearly departed Dr Halimi Zakaria’s scholarly contribution entitled “Impact of Antecedent Factors on Collaborative Technologies Usage among Academic Researchers in Malaysian Research Universities”. He has left behind his discovery to be used by other researchers in their quest of pursuing research in the same area, a discovery that his family can be proud of. Graduands, earning your PhD is not the end of discovering new ideas, invention or innovation but rather the start of discovering something new. Enjoy every moment of its discovery and embrace that life is full of mystery and treasure that is waiting for you to unfold. As you unfold life’s mystery, remember you have a friend to count on, and that friend is UiTM. Congratulations for completing this academic journey. Keep UiTM close to your heart and be our ambassador wherever you go. / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR
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