CIVIL STRUCTURES ARE USUALLY PRONE TO DAMAGE DURING THEIR SERVICE LIFE AND IT LEADS THEM TO LOSS THEIR SERVICEABILITY AND SAFETY. THUS, DAM-AGE ASSESSMENT CAN GUARANTEE THE INTEGRITY OF STRUCTURES. AS A RESULT, A STRUCTURAL DAMAGE DETECTION APPROACH INCLUDING TWO MAIN COMPONENTS, A SET OF ACCELEROMETERS TO RECORD THE RESPONSE DATA AND A DATA MINING (DM) PROCEDURE, IS WIDELY USED TO EXTRACT THE INFORMATION ON THE STRUCTURAL HEALTH CONDITION. IN THE LAST DECADES, DM HAS PROVIDED NUMEROUS SOLUTIONS TO STRUCTURAL HEALTH MONITORING (SHM) PROBLEMS AS AN ALL-INCLUSIVE TECHNIQUE DUE TO ITS POWERFUL COMPUTATIONAL ABILITY. THIS PAPER PRESENTS THE FIRST ATTEMPT TO ILLUSTRATE THE DATA MINING TECHNIQUES (DMTS) APPLICATIONS IN SHM THROUGH AN INTENSIVE REVIEW OF THOSE ARTICLES DEALING WITH THE USE OF DMTS AIMED FOR CLASSIFICATION-, PREDICTION- AND OPTIMIZATION-BASED DATA MINING METHODS. ACCORDING TO THIS CATEGORIZATION, APPLICATIONS OF DMTS WITH RESPECT TO SHM RESEARCH AREA ARE CLASSIFIED AND IT IS CONCLUDED THAT, APPLICATIONS OF DMTS IN THE SHM DOMAIN HAVE INCREASINGLY BEEN IMPLEMENTED, IN THE LAST DECADE AND THE MOST POPULAR TECHNIQUES IN THE AREA WERE ARTIFICIAL NEURAL NETWORK (ANN), PRINCIPAL COMPONENT ANALYSIS (PCA) AND GENETIC ALGORITHM (GA), RESPECTIVELY.

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Recent developments in damage identification of structures using data mining

漏洩磁場測定による溶接管のオンライン検査(Non-Destructive Testing,9-1,1976)(文献紹介)

Cyclical Parthenogenesis Algorithm for guided modal strain energy based structural damage detection

A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.

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Energy-Efficient Train Operation Using Nature-Inspired Algorithms

In this paper, a novel and effective damage diagnosis algorithm is proposed to localize and quantify structural damage using incomplete modal data, considering the existence of some limitations in the number of attached sensors on structures. The damage detection problem is formulated as an optimization problem by computing static displacements in the reduced model of a structure subjected to a unique static load. The static responses are computed through the flexibility matrix of the damaged structure obtained based on the incomplete modal data of the structure. In the algorithm, an iterated improved reduction system method is applied to prepare an accurate reduced model of a structure. The optimization problem is solved via a new evolutionary optimization algorithm called the cuckoo optimization algorithm. The efficiency and robustness of the presented method are demonstrated through three numerical examples. Moreover, the efficiency of the method is verified by an experimental study of a five-story shear building structure on a shaking table considering only two sensors. The obtained damage identification results for the numerical and experimental studies show the suitable and stable performance of the proposed damage identification method for structures with limited sensors.

A flexibility-based method via the iterated improved reduction system and the cuckoo?optimization algorithm for damage quantification with limited sensors

The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases. A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB- BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm. This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.

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Pourhoseini Nejad

Papers

A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm