• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.359-366
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• 2020

In this study, a simulation-based damage estimation method for helidecks is proposed using an artificial neural network. The structural members that share a connecting node in the helideck are regarded as a damage group, and a total of 37,400 damage scenarios are numerically generated by applying randomly assigned damage to up to three damage groups. Modal analysis is then performed for all the damage scenarios, which are selectively used as either training or validation or verification sets based on the purpose of use. An artificial neural network with three hidden layers is constructed using a PyTorch program to recognize the patterns of the modal responses of the helideck model under both damaged and undamaged states, and the network is successively trained to minimize the loss function. Finally, the estimated damage rate from the proposed artificial neural network is compared to the actual assigned damage rate using 400 verification scenarios to show that the neural network is able to estimate the location and amount of structural damage precisely.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.6
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• pp.678-685
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• 2012

The objective of this study is to monitor the health status of harbor caissons which have potential foundation damage. To obtain the objective, the following approaches are performed. Firstly, a structural damage monitoring(SDM) method is designed for interlocked multiple-caisson structures. The SDM method utilizes the change in modal strain energy to monitor the foundation damage in a target caisson unit. Secondly, a finite element model of a caisson system which consists of three caisson units is established to verify the feasibility of the proposed method. In the finite element simulation, the caisson units are constrained each other by shear-key connections. The health status of the caisson system against various levels of foundation damage is monitored by measuring relative modal displacements between the adjacent caissons.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.7 s.112
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• pp.720-728
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• 2006

A robust damage assessment technique is presented such that the location and severity of damage in structures can be identified using measured modal data. In order to identify the damage efficiently, the concept of design of experiment using orthogonal array is used for screening the main effects of each parameter which corresponds to possible damage location in FE model. Then, Taguchi method, which has been widely used for robust design in industry, is applied to parameter updating in analytical FE model. The numerical simulations of a truss structure show that damages in structure can be located from updated parameters.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.1
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• pp.91-99
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• 2012

The main objective of this study is to examine the feasibility of using lead zirconate titanate (PZT)'s direct piezoelectric response as vibrational feature for damage monitoring in beam structures. For the purpose, modal strain energy (MSE)-based damage monitoring in beam structures using dynamic strain response based on the direct piezoelectric effect of PZT sensor is proposed in this paper. The following approaches are used to achieve the objective. First, the theoretical background of PZT's direct piezoelectric effect for dynamic strain response is presented. Next, the damage monitoring method that utilizes the change in MSE to locate of damage in beam structures is outlined. For validation, forced vibration tests are carried out on lab-scale cantilever beam. For several damage scenarios, dynamic responses are measured by three different sensor types (accelerometer, PZT sensor and electrical strain gage) and damage monitoring tasks are performed thereafter. The performance of PZT's direct piezoelectric response for MSE-based damage monitoring is evaluated by comparing the damage localization results from the three sensor types.

• Smart Structures and Systems
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• v.25 no.1
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• pp.1-22
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• 2020

The modal strain energy method is one of the efficient methods for detecting damage in the structures. Due to existing some limitations in real-world structures, sensors can only be located on a limited number of degrees of freedom (DOFs) of a structure. Therefore, the mode shape values in all DOFs of structures cannot be measured. In this paper, a modified modal strain energy based index (MMSEBI) is introduced to locate damaged elements of structures when a limited number of sensors are used. The proposed MMSEBI is based on the reconstruction of mode shapes using Improved Reduction System (IRS) method. Therefore, in the first step by employing IRS method, mode shapes in slave degrees of freedom are estimated by those of master degrees of freedom. In the second step, the proposed MMSEBI is used to located damage elements. In order to evaluate the efficiency of the proposed method, two numerical examples are considered under different damage patterns considering the measurement noise. Moreover, the universal threshold based on statistical hypothesis testing principles is applied to damage index values. The results show the effectiveness of the proposed MMSEBI for the structural damage localization when comparing with the available damage index named MESBI. The results demonstrate that the presented method can be used as a practical strategy for structural damage identification, especially when a limited number of sensors are installed on the structure. Finally, the combination of MMSEBI and IRS method can provide a reliable tool to identify the location of damage accurately.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.10a
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• pp.549-555
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• 2003

The objective of かis study is to investigate the feasibility of piezoelectric transducers as a damage detection system for civil infrastructures. There have been considerable amount of efforts by the modal analysis community to localize damage and evaluate its severity without looking at a reliable way to excite the structure. The detection of damages by modal analysis and similar vibration techniques depends upon the knowledge and estimation of various modal parameters. In addition to the associated difficulties, such low-frequency dynamic response based techniques fail to detect incipient damages. Smart piezoelectric ceramic (PZT) transducers which act as both actuators and sensors in a self-analyzing manner are emerging to be effective in non-parametric health monitoring of structural systems. In this paper, we present the results of an experimental study for the detection of damages using smart PZT transducers on the steel plate. The method of extracting the impedance characteristics of the PZT transducer, which is electro-mechanically coupled to the host structure, is adopted for damage detection. Two damages are simulated and assessed by the bonded PZT transducers for characterization. The experimental results verified the efficacy of the proposed approach and provided a demonstration of good robustness at the realistic steel structures, emphasizing the great potential for developing an automated in situ structural health monitoring system for application to large civil infrastructures without the need to blow the modal parameters.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Smart Structures and Systems
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• v.20 no.2
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• pp.247-261
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• 2017

In the field of dynamic measurement and structural damage identification, it is generally known that modal frequencies may be measured with higher accuracy than mode shapes. However, the number of natural frequencies within a measurable range is limited. Accessing additional forms of modal frequencies is thus desirable. The present study is concerned about the extraction of artificial boundary condition (ABC) frequencies from modal testing. The ABC frequencies correspond to the natural frequencies of the structure with a perturbed boundary condition, but they can be extracted from processing the frequency response functions (FRF) measured in a specific configuration from the structure in its existing state without the need of actually altering the physical support condition. This paper presents a comprehensive experimental investigation into the measurability of the ABC frequencies from physical experiments. It covers the testing procedure through modal testing, the data processing and data analysis requirements, and the FRF matrix operations leading to the extraction of the ABC frequencies. Specific sources of measurement errors and their effects on the accuracy of the extracted ABC frequencies are scrutinised. The extracted ABC frequencies are subsequently applied in the damage identification in beams by means of finite element model updating. Results demonstrate that it is possible to extract the first few ABC frequencies from the modal testing for a variety of artificial boundary conditions incorporating one or two virtual pin supports, and the inclusion of ABC frequencies enables the identification of structural damages without the need to involve the mode shape information.

• Steel and Composite Structures
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• v.14 no.4
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11a
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• pp.494-497
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• 2001

Identification of damage of structures has recently received considerable attention in the light of maintenance and safety assessment. In this respect, the vibration characteristics of buildings have been applied steadily to obtain a damage index of the whole building, but it cannot be established as a practical method until now. A practical method for the estimation of structural damage using the first natural frequency and mode shape of building is proposed in this paper. The effectiveness of the proposed method is verified by numerical and experimental tests. From the results, it is observed that severity and location of damage can be estimated with a relatively small error by using modal properties of building.