• Steel and Composite Structures
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• 제14권4호
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• pp.367-377
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• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• 한국공간구조학회논문집
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• 제8권6호
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• pp.59-66
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• 2008

대부분의 대공간구조물은 극장, 스타디움, 체육관 등 공공성을 가지게 되어 내진안전성에 있어서 중요성이 많이 인식되고 있다. 그러나 구조형식 및 형상에 관하여 다양성을 가지고 있는 대공간구조물이 동적하중인 지진하중을 받을 때 나타나는 구조물의 거동은 파악하기 힘들다. 본 논문에서는 대공간구조의 주 구조요소인 아치구조물에 대하여 고유진동모드를 검토하였고 모의지진파를 입력하여 지진거동특성을 분석한 결과로서 아치구조물은 설계가속도스펙트럼의 크기보다 장주기 성분에 더 많은 영향을 받는다는 것을 파악하였다.

• Steel and Composite Structures
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• 제24권5호
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.

• 한국재료학회지
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• 제23권2호
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• pp.149-153
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• 2013

Delamination crack detection is very important for improving the structural reliability of laminated composite structures. This requires real-time delamination detection technologies. For composite laminates that are reinforced with carbon fiber, an electrical potential method uses carbon fiber for reinforcements and sensors at the same time. The use of carbon fiber for sensors does not need to consider the strength reduction of smart structures induced by imbedding sensors into the structures. With carbon fiber reinforced (CF/) epoxy matrix composites, it had been proved that the delamination crack was detected experimentally. In the present study, therefore, similar experiments were conducted to prove the applicability of the method for delamination crack detection of CF/polyetherethereketone matrix composite laminates. Mode I and mode II delamination tests with artificial cracks were conducted, and three point bending tests without artificial cracks were conducted. This study experimentally proves the applicability of the method for detection of delamination cracks. CF/polyetherethereketone material has strong electric resistance anisotropy. For CF/polyetherethereketone matrix composites, a carbon fiber network is constructed, and the network is broken by propagation of delamination cracks. This causes a change in the electric resistance of CF/polyetherethereketone matrix composites. Using three point bending specimens, delamination cracks generated without artificial initial cracks is proved to be detectable using the electric potential method: This method successfully detected delamination cracks.

• Structural Engineering and Mechanics
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• 제29권2호
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• pp.223-235
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• 2008

This paper focuses on the application of artificial neural networks (ANN) for optimal design of tensegrity grid as light-weight roof structures. A tensegrity grid, 2 m ${\times}$ 2 m in size, is fabricated by integrating four single tensegrity modules based on half-cuboctahedron configuration, using galvanised iron (GI) pipes as struts and high tensile stranded cables as tensile elements. The structure is subjected to destructive load test during which continuous monitoring of the prestress levels, key deflections and strains in the struts and the cables is carried out. The monitored structure is analyzed using finite element method (FEM) and the numerical model verified and updated with the experimental observations. The paper then explores the possibility of applying ANN based on multilayered feed forward back propagation algorithm for designing the tensegrity grid structure. The network is trained using the data generated from a finite element model of the structure validated through the physical test. After training, the network output is compared with the target and reasonable agreement is found between the two. The results demonstrate the feasibility of applying the ANNs for design of the tensegrity structures.

• Smart Structures and Systems
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• 제15권1호
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• pp.227-244
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• 2015

This study develops a two stage procedure to identify the structural damage based on the optimized artificial neural networks. Initially, the modal strain energy index (MSEI) is established to extract the damaged elements and to reduce the computational time. Then the genetic algorithm (GA) and artificial neural networks (ANNs) are combined to detect the damage severity. The input of the network is modal strain energy index and the output is the flexural stiffness of the beam elements. The principal component analysis (PCA) is utilized to reduce the input variants of the neural network. By using the genetic algorithm to optimize the parameters, the ANNs can significantly improve the accuracy and convergence of the damage identification. The influence of noise on damage identification results is also studied. The simulation and experiment on beam structures shows that the adaptive parameter selection neural network can identify the damage location and severity of beam structures with high accuracy.

• Steel and Composite Structures
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• 제47권6호
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Advances in concrete construction
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• 제9권3호
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• pp.257-265
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• 2020

Many failures of concrete structures are related to steel corrosion. For this reason, it is important to recognize how the carbonation can affect the durability of reinforced concrete structures. The repeatability of the carbonation depth measure in a specimen of concrete sprayed with a phenolphthalein solution is consistently low whereby it is necessary to have an impartial method to measure the carbonation depth. This study presents two automatic algorithms to detect the non-carbonated zone in concrete specimens. The first algorithm is based solely on digital processing image (DPI), mainly morphological and threshold techniques. The second algorithm is based on artificial intelligence, more specifically on an array of Kohonen networks, but also using some DPI techniques to refine the results. Moreover, another algorithm was developed with the purpose of measure the carbonation depth from the image obtained previously.

• 한국구조물진단유지관리공학회 논문집
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• 제6권4호
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• pp.189-199
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• 2002

This study is intended to propose a systematic procedure for the development of the conditional assessment based on the safety of structures and the cost effective performance criteria for designing and upgrading of bridge structures. As a result, a set of cost function models for a life cycle cost analysis of bridge structures is proposed and thus the expected total life cycle costs (ETLCC) including initial (design, testing and construction) costs and direct/indirect damage costs considering repair and replacement costs, human losses and property damage costs, road user costs, and indirect regional economic losses costs. Also, the optimum safety indices are presented based on the expected total cost minimization function using only three parameters of the failure cost to the initial cost (${\tau}$), the extent of increased initial cost by improvement of safety (${\nu}$) and the order of an initial cost function (n). Through the enough numerical invetigations, we can positively conclude that the proposed optimum design procedure for bridge structures based on the ETLCC will lead to more rational, economical and safer design.

• Smart Structures and Systems
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• 제13권1호
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• pp.81-98
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• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.