• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.669-674
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• 2007

To develop a promising hybrid structural health monitoring (SHM) system, a combined use of structural vibration and electro-mechanical (EM) impedance is proposed. The hybrid SHM system is designed to use vibration characteristics as global index and EM impedance as local index. The proposed health monitoring scheme is implemented into prestressed concrete (PSC) girder bridges for which a series of damage scenarios are designed to simulate various prestress-loss situations at which the target bridges car experience during their service life. The measured experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the occurrence of damage and furthermore to indicate its location.

• 한국해양공학회지
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• 제21권6호
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• pp.26-33
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• 2007

Finite element simulation of elastic wave propagation in a concrete plate was carried out to investigate its modeling and damage detection procedures. For the numerical stability three criteria were introduced and tested. With a proper element size and time increment, two different kinds of damage scenarios (crack and deterioration) were applied to verify the feasibility of the finite element simulation. It is shown that the severities of those damages are sensitive to the received displacement signals.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Smart Structures and Systems
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• 제22권3호
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• pp.277-290
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• 2018

In this paper, wavelet finite element model (WFEM) updating technique is employed to detect sub-element damage in thin plate structures progressively. The procedure of WFEM-based detection method, which can detect sub-element damage gradually, is established. This method involves the optimization of an objective function that combines frequencies and modal assurance criteria (MAC). During the damage detection process, the scales of wavelet elements in the concerned regions are adaptively enhanced or reduced to remain compatible with the gradually identified damage scenarios, while the modal properties from the tests remains the same, i.e., no measurement point replacement or addition are needed. Numerical and experimental examples were conducted to examine the effectiveness of the proposed method. A scanning Doppler laser vibrometer system was employed to measure the plate mode shapes in the experimental study. The results indicate that the proposed method can detect structural damage with satisfactory accuracy by using minimal degrees-of-freedoms (DOFs) in the model and minimal updating parameters in optimization.

• Structural Engineering and Mechanics
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• 제65권1호
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• pp.43-51
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• 2018

Structural modal parameters i.e. natural frequencies, damping ratios and mode shapes are dynamic features obtained either by measuring the vibration responses of a structure or by means of finite elements models. Over the past two decades, modal parameters have been used to detect damage in structures by observing its variations over time. However, such variations can also be caused by environmental factors such as humidity, wind and, more importantly, temperature. In so doing, the use of modal parameters as damage indicators can be seriously compromised if these effects are not properly tackled. Many researchers around the world have found numerous methods to mitigate the influence of such environmental factors from modal parameters and many advanced damage indicators have been developed and proposed to improve the reliability of structural health monitoring. In this paper, several vibration tests are performed on a simply supported steel beam subjected to different damage scenarios and temperature conditions, aiming to describe the variation in modal parameters due to temperature changes. Moreover, four statistical methodologies are proposed to identify damage. Results show a slightly linear decrease in the modal parameters due to temperature increase, although it is not possible to establish an empirical equation to describe this tendency.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.711-730
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• 2010

This study presents the design of autonomous smart sensor nodes for damage monitoring of tendons and girders in prestressed concrete (PSC) bridges. To achieve the objective, the following approaches are implemented. Firstly, acceleration-based and impedance-based smart sensor nodes are designed for global and local structural health monitoring (SHM). Secondly, global and local SHM methods which are suitable for damage monitoring of tendons and girders in PSC bridges are selected to alarm damage occurrence, to locate damage and to estimate severity of damage. Thirdly, an autonomous SHM scheme is designed for PSC bridges by implementing the selected SHM methods. Operation logics of the SHM methods are programmed based on the concept of the decentralized sensor network. Finally, the performance of the proposed system is experimentally evaluated for a lab-scaled PSC girder model for which a set of damage scenarios are experimentally monitored by the developed smart sensor nodes.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• Structural Engineering and Mechanics
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• 제87권3호
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• 한국가스학회지
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• 제10권4호
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• pp.52-62
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• 2006

본 연구에서는 배관누출에 의한 가스 폭발사고에서 누출 시나리오 선정방법과 사고결과 분석방법을 제시하고자 하였다. 이를 위해 온도, 압력, 누출물질 등 다양한 누출조건에서 누출속도, 장치피해영역 및 상해영역을 산출하고, 비교 분석하였다. 그 결과, 산출방법에 따라 사고 결과값이 다소 차이가 있었으나 파열인 경우에 최대값을 가지며, 이로부터 최악의 사고피해를 예측할 수 있었다. 그리고 누출공의 크기는 임의로 선정하기보다는 고장률을 고려한 가중평균법으로 사고피해를 예측하는 것이 바람직하다고 판단되었다.