• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.73-82
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• 2008

In this paper, a new regularization method by parameter subset selection method is proposed based on the residual force vector for damage localization. Although subset selection using the fundamental modal characteristics as a residual function has been successful in detecting a single damage location, this method seems to have limited capabilities in the detection of multiple damage locations and typically requires cumbersome weighting values. The method is presented herein and considers cases in which damage detection must be achieved using incomplete measurements of the structural responses. Model expansion is incorporated to deal with this challenge. The unique advantage of employing the new regularization method is that it can reliably identify multiple damage locations. Through an illustrative example, the proposed damage detection method is demonstrated to be a reliable tool for identifying multiple damage locations for a planar truss structure.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.599-610
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• 2021

Early detection of small concrete crack or reinforcement corrosion is necessary for Structural Health Monitoring (SHM). Global vibration based methods are advantageous over local methods because of simple equipment installation and cost efficiency. Among vibration based techniques, FRF based methods are preferred over modal based methods. In this study, a new coupled method using frequency response function (FRF) and proper orthogonal modes (POM) is proposed by using the dynamic characteristic of a damaged beam. For the numerical simulation, wave finite element (WFE), coupled with traditional finite element (FE) method is used for effectively incorporating the damage related information and faster computation. As reported in literature, hybrid combination of wave function based wave finite element method and shape function based finite element method can addresses the mid frequency modelling difficulty as it utilises the advantages of both the methods. It also reduces the dynamic matrix dimension. The algorithms are implemented on a three-dimensional reinforced concrete beam. Damage is modelled and studied for two scenarios, i.e., crack in concrete and rebar corrosion. Single and multiple damage locations with different damage length are also considered. The proposed methodology is found to be very sensitive to both single- and multiple- damage while being computationally efficient at the same time. It is observed that the detection of damage due to corrosion is more challenging than that of concrete crack. The similarity index obtained from the damage parameters shows that it can be a very effective indicator for appropriately indicating initiation of damage in concrete structure in the form of spread corrosion or invisible crack.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.14-22
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• 2017

The frequency and scale of natural disasters due to the abnormal climate phenomena caused by global warming have being increasing all over the world. Various natural disasters, such as typhoons, earthquakes, floods, heavy rain, drought, sweltering heat, wind waves, tsunamis and so on, can cause damage to human life. Especially, the damage caused by natural disasters such as the Earthquake of Japan, hurricane Katrina in the United States, typhoon Maemi and so on, have been enormous. At this stage, it is difficult to estimate the scale of damage due to (future) natural disasters and cope with them. However, if we could predict the scale of damage at the disaster response level, the damage could be reduced by responding to them promptly. In the present study, therefore, among the many types of natural disaster, we developed a function to estimate the damage due to wind waves caused by sea winds and waves. We collected the damage records from the Disaster Report ('91~'14) published by the Ministry of Public Safety and Security about wind waves and typhoons in the western coastal zone and, in order to reflect the inflation rate, we converted the amount of damage each year into the equivalent amount in 2014. Finally, the meteorological data, such as the wave height, wind speed, tide level, wave direction, wave period and so on, were collected from the KMA (Korea Meteorological Administration) and KHOA (Korea Hydrographic and Oceanographic Agency)'s web sites, for the periods when wind wave and typhoon damage occurred. After that, the function used to estimate the wind wave damage was developed by reflecting the regional characteristics for the 9 areas of the western coastal zone.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.394-399
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• 2005

In this paper, a structural damage identification method (SDIM) is developed to identify the line crack-like directional damages generated within a cylindrical shell. First, the equations of motion fur a damaged cylindrical shell are derived. Based on a theory of continuum damage mechanics, a small material volume containing a directional damage is represented by the effective orthotropic elastic stiffness, which is dependent of the size and the orientation of the damage with respect to the global coordinates. The present SDIM is then derived from the frequency response function (FRF) directly solved from the dynamic equations of the damaged cylindrical shell. In contrast with most existing SDIMs which require the modal parameters measured in both intact and damaged states, the present SDIM requires only the FRF-data measured in damaged state. By virtue of utilizing FRF-data, one may choose as many sets of excitation frequency and FRF measurement point as needed to acquire a sufficient number of equations fer damage identification analysis. The numerically simulated damage identification tests are conducted to study the feasibility of the present SDIM.

• Earthquakes and Structures
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• v.17 no.1
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• pp.63-73
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• 2019

This paper studies damage detection as an optimization problem. A new objective function based on changes in natural frequencies, and Natural Frequency Vector Assurance Criterion (NFVAC) was developed. Due to their easy and fast acquisition, natural frequencies were utilized to detect structural damages. Moreover, they are sensitive to stiffness reduction. The method presented here consists of two stages. Firstly, Finite Element Model (FEM) is updated. Secondly, damage severities and locations are determined. To minimize the proposed objective function, a new bio-inspired optimization algorithm called salp swarm was employed. Efficiency of the method presented here is validated by three experimental examples. The first example relates to three-story shear frame with two single damage cases in the first story. The second relates to a five-story shear frame with single and multiple damage cases in the first and third stories. The last one relates to a large-scale eight-story shear frame with minor damage case in the first and third stories. Moreover, the performance of Salp Swarm Algorithm (SSA) was compared with Particle Swarm Optimization (PSO). The results show that better accuracy is obtained using SSA than using PSO. The obtained results clearly indicate that the proposed method can be used to determine accurately and efficiently both damage location and severity in multi-story shear frames.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.357-365
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• 2003

The major problems with the conventional neural network, especially Back Propagation Neural Network, arise from the necessity of many training data for neural network learning and ambiguity in the relation of neural network structure to the convergence of solution. In this paper, the PNN is used as a pattern classifier to detect the damage of structure to avoid those drawbacks of the conventional neural network. In the PNN-based pattern classification problems, the probability density function for patterns is usually assumed by Gaussian distribution. But, in this paper, several probability density functions are investigated in order to select the most approriate one for structural damage assessment.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.649-659
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• 2019

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.263-271
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• 1997

This paper presents a preventive maintenance model for determining the preventive replacement period of a system in which a failure rate is affected by the cumulative damage of fault and inspection. Especially, the failure rate function is considered to be a function of the cumulative damage of the fault and inspection time. Types of replacement considered are preventive replacement and failure replacement. Failure rate and expected cost function between replacement are derived. An optimal policy is obtained that minimizes the average cost per unit time for preventive replacement, failure replacement, inspection and repair.

• Structural Engineering and Mechanics
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• v.84 no.4
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.