• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.107-112
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• 2013

Base on insurance vehicle collision and bodily injury claim reports, 23,655 cases of vehicle to vehicle accidents occurred in Korea 2010 are investigated in order to understand vehicle damage severities, repair costs and occupant injury types. The results of our statistical analysis reveal that minor damages with small dent or scratches on vehicle body panels which is assumed to imply during very low speed crashes are major portion of accident severities types. The most vulnerable body regions due to the real-world accident are neck. The 86.3% of total injured driver in minor rear damaged vehicles has reported neck pains and they are followed by whole bodies and head but with much lower occurrence rates.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.297-303
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• 2000

Damage estimation of a bridge structure is presented using ambient vibration data caused by the traffic loadings. The procedure consists of identification of the modal properties and assessment of the damage locations and severities. An experimental study is carried out on the bridge model subjected to vehicle loadings. Vertical accelerations of the bridge deck are measured at a limited number of locations. The modal parameters are identified from the free vibration signals extracted using the random decrement method. Then, the damage assessment is carried out based on the estimated modal parameters using the neural networks technique. The identified damage locations and severities agree reasonably well with the inflicted damages on the structure.

• Structural Engineering and Mechanics
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• v.38 no.6
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• pp.715-732
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• 2011

A novel structural damage detection method with a new damage index has been recently proposed by the authors based on the statistical moments of dynamic responses of shear building structures subject to white noise ground motion. The statistical moment-based damage detection (SMBDD) method is theoretically extended in this paper with general application. The generalized SMBDD method is more versatile and can identify damage locations and damage severities of many types of building structures under various external excitations. In particular, the incomplete measurements can be considered by the proposed method without mode shape expansion or model reduction. Various damage scenarios of two general forms of building structures with incomplete measurements are investigated in consideration of different excitations. The effects of measurement noise are also investigated. The damage locations and damage severities are correctly identified even when a high noise level of 15% and incomplete measurements are considered. The effectiveness and versatility of the generalized SMBDD method are demonstrated.

• Smart Structures and Systems
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• v.2 no.2
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• pp.141-154
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• 2006

Recently, the Hilbert-Huang transform (HHT) has gained considerable attention as a novel technique of signal processing, which shows promise for the system identification and damage detection of structures. This study investigates the effectiveness and accuracy of the HHT method for the system identification and damage detection of structures through a series of experiments. A multi-degree-of-freedom (MDOF) structural model has been constructed with modular members, and the columns of the model can be replaced or removed to simulate damages at different locations with different severities. The measured response data of the structure due to an impulse loading is first decomposed into modal responses using the empirical mode decomposition (EMD) approach with a band-pass filter technique. Then, the Hilbert transform is subsequently applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least-square fit procedure is used to identify the natural frequencies and damping ratios from the instantaneous amplitude and phase angle for each modal response. When the responses at all degrees of freedom are measured, the mode shape and the physical mass, damping and stiffness matrices of the structure can be determined. Based on a comparison of the stiffness of each story unit prior to and after the damage, the damage locations and severities can be identified. Experimental results demonstrate that the HHT method yields quite accurate results for engineering applications, providing a promising tool for structural health monitoring.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.198-205
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• 2000

In this study, an integrated damage identification system (IDIS) using modal information to detect damage in structures is presented. The main dobjective is to develop a system of softwares that facilitates detecting damage locations and estimating damage severities in bridges. Firstly, theoretical background for IDIS is outlined. Secondly, a GUI-based IDIS software scheme are programmed. Finally, the feasibility and applicability of the IDIS software are experimentally demonstrated using small-scaled plate-girder models.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.24-31
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• 2000

An integrated damage identification system (IDIS) using modal information to detect damage in structures is presented. The objective of this study is to develop a system of softwares that facilitates detecting damage locations and estimating damage severities in bridges. Firstly, the theoretical background for IDIS is outlined. Secondly, a GUI-based IDIS software is programmed. Finally, the feasibility and applicability of the IDIS software are experimentally demonstrated using small-scaled plate-girder models.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.292-299
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• 2002

In this paper a new damage identification theory is developed in order to identify the locations, severities, and orientations of local damages, all together at a time, by using the frequency response functions measured from damaged plate. Finally, the effects of damage orientation on the vibration responses of a plate are numerically investigated, and the numerically simulated damage identification tests are conducted to verify the present damage identification theory.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.87-94
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• 2012

A damage estimation method of wind turbine tower using natural frequency and mode shape is presented for effective condition monitoring. Dynamic analysis for a wind turbine was carried out to obtain the response of tower from which modal properties were identified. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. The changes of modal property were calculated using a program for modal parameter estimation. Damage locations and severities could be successfully estimated for 10 damage cases including multi-damage cases using the trained neural network. The damage severities for very small damages generally tends to be slightly under-estimated however, the identified damage locations agreed reasonably well with the accurate locations. Enhancement of the estimation result for very small damage and verification of the proposed method through experiment will be carried out by further study.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.300-307
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• 2002

Damage estimation methods are classified into two groups according to the dependence on the FE model : signal-based and model-based methods. Signal-based damage estimation methods are generally appropriate for detection of damage location, whereas not effective for estimation of damage severities. Model-based damage estimation methods are difficult to apply directly to the structures with a large number of the probable damaged members. It is difficult to obtain the exact model representing the real bridge behavior due to the modeling errors. The modeling errors even may exceed the modal sensitivity on damage. In this study, Model-based damage detection method which can effectively consider the modeling errors is suggested. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the presented method.

• Structural Monitoring and Maintenance
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• v.5 no.4
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• pp.507-519
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• 2018

In this paper, damage assessment in wind-turbine towers using vibration-based artificial neural networks (ANNs) is numerically investigated. At first, a vibration-based ANNs algorithm is designed for damage detection in a wind turbine tower. The ANNs architecture consists of an input, an output, and hidden layers. Modal parameters of the wind turbine tower such as mode shapes and frequencies are utilized as the input and the output layer composes of element stiffness indices. Next, the finite element model of a real wind-turbine tower is established as the test structure. The natural frequencies and mode shapes of the test structure are computed under various damage cases of single and multiple damages to generate training patterns. Finally, the ANNs are trained using the generated training patterns and employed to detect damaged elements and severities in the test structure.