• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.6
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• pp.283-292
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• 2021

In this study, a structural health monitoring system for cable-stayed bridges is developed. In the system, condition assessment of the structure is performed based on measured records from seismic accelerometers. Response indices are defined to monitor structural safety and serviceability and derived from the measured acceleration data. The derivation process of the indices is structured to follow the transformation from the raw data to the outcome. The process includes noise filtering, baseline correction, numerical integration, and calculation of relative differences. The system is packed as a condition assessment program, which consists of four major processes of the structural health evaluation: (i) format conversion of the raw data, (ii) noise filtering, (iii) generation of response indices, and (iv) condition evaluation. An example set of limit states is presented to evaluate the structural condition of the test-bed and cable-stayed bridge.

• Smart Structures and Systems
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• v.12 no.2
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• pp.209-233
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• 2013

With an annual growth rate of about 30%, wind energy systems, such as wind turbines, represent one of the fastest growing renewable energy technologies. Continuous structural health monitoring of wind turbines can help improving structural reliability and facilitating optimal decisions with respect to maintenance and operation at minimum associated life-cycle costs. This paper presents an integrated monitoring system that is designed to support structural assessment and life-cycle management of wind turbines. The monitoring system systematically integrates a wide variety of hardware and software modules, including sensors and computer systems for automated data acquisition, data analysis and data archival, a multiagent-based system for self-diagnosis of sensor malfunctions, a model updating and damage detection framework for structural assessment, and a management module for monitoring the structural condition and the operational efficiency of the wind turbine. The monitoring system has been installed on a 500 kW wind turbine located in Germany. Since its initial deployment in 2009, the system automatically collects and processes structural, environmental, and operational wind turbine data. The results demonstrate the potential of the proposed approach not only to ensure continuous safety of the structures, but also to enable cost-efficient maintenance and operation of wind turbines.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.201-205
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• 2005

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by piezoelectric Impedance-based technique associated with longitudinal wave propagaation. The natural frequency of the object has a tendency of frequency shifting according to hole size corresponded to real structure crack and crack size. In order to estimate the damage condition numerically, we suggest the evaluation method of Impedance peak frequency shift hF in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.889-893
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• 2004

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by piezoelectric impedance-based technique associated with longitudinal wave propagation method. The bolt fastening condition is adjusted by torque wrench. In order to estimate the damage condition numerically, three damage indices, impedance peak frequency shift ${\Delta}F$ is proposed in this paper. Furthermore, an assessment method is described for estimation of the damage by using these three damage indices.

• Smart Structures and Systems
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• v.30 no.6
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• pp.557-569
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• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.135-142
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• 2004

Two-step identification approach for effective bridge health monitoring is proposed to alleviate the issues associated with many unknown parameters faced in the real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried out whereas the alarming for damages is to be continuously monitored. In the first step for screening potential damaged members, damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are used and then the conventional neural network technique is utilized for damage assessment on the screened members in the second step. The proposed methods are verified through a field test on the northern-most span of old Hannam Grand Bridge.

• Journal of Wind Energy
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• v.9 no.4
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.133-138
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• 2001

Formal safety assessment (FSA) is defined as a structured and systematic methodology aimed at enhancing maritime safety including protection of life, health, and the marine environment and property, by using risk and cost/benefit assessment or, more specifically, it is a process of identifying hazard, evaluating risk and deciding on course of action to control those risks. Emphasis is placed on the structural aspect of the failure of cargo hold. This study is expected to illustrate how a new methodology, i.e. the Formal Safety Assessment, can be applied to specific ship safety problems.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.199-211
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• 2015

The safety and performance of bridges could be monitored and evaluated by Structural Health Monitoring (SHM) systems. These systems try to identify and locate the damages in a structure and estimate their severities. Current SHM systems are applied to a single bridge, and they have not been used to monitor the structural condition of a network of bridges. This paper propose a new method which will be used in Synthetic Rating Procedures (SRP) developed by the authors of this paper and utilizes SHM systems for monitoring and evaluating the condition of a network of bridges. Synthetic rating procedures are used to assess the condition of a network of bridges and identify their ratings. As an additional part of the SRP, the method proposed in this paper can continuously monitor the behaviour of a network of bridges and therefore it can assist to prevent the sudden collapses of bridges or the disruptions to their serviceability. The method could be an important part of a bridge management system (BMS) for managers and engineers who work on condition assessment of a network of bridges.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.860-863
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• 2005

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by PZT impedance-based technique associated with longitudinal wave propagation. The bolt fastening condition is adjusted by torque wrench In order to estimate the damage condition numerically, we suggest the evaluation method of impedance peak frequency shift $\Delta$F in this paper.