• Structural Engineering and Mechanics
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• 제81권6호
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• pp.665-675
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• 2022

The safety problems of giant hydraulic structures such as dams caused by terrorist attacks, earthquakes, and wars often have an important impact on a country's economy and people's livelihood. For the national defense department, timely and effective assessment of damage to or impending damage to dams and other structures is an important issue related to the safety of people's lives and property. In the field of damage assessment and vulnerability analysis, it is usually necessary to give the damage assessment results within a few minutes to determine the physical damage (crack length, crater size, etc.) and functional damage (decreased power generation capacity, dam stability descent, etc.), so that other defense and security departments can take corresponding measures to control potential other hazards. Although traditional numerical calculation methods can accurately calculate the crack length and crater size under certain combat conditions, it usually takes a long time and is not suitable for rapid damage assessment. In order to solve similar problems, this article combines simulation calculation methods with machine learning technology interdisciplinary. First, the common concrete gravity dam shape was selected as the simulation calculation object, and XFEM (Extended Finite Element Method) was used to simulate and calculate 19 cracks with different initial positions. Then, an LSTM (Long-Short Term Memory) machine learning model was established. 15 crack paths were selected as the training set and others were set for test. At last, the LSTM model was trained by the training set, and the prediction results on the crack path were compared with the test set. The results show that this method can be used to predict the crack propagation path rapidly and accurately. In general, this article explores the application of machine learning related technologies in the field of mechanics. It has broad application prospects in the fields of damage assessment and vulnerability analysis.

• Structural Engineering and Mechanics
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• 제41권4호
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• pp.539-558
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• 2012

The diagnosis of bridge serviceability is carried out by a combination of in-situ visual inspection, static and dynamic loading tests and analyses. Structural health monitoring (SHM) using information technology and sensors is increasingly being used for providing a better estimate of structural performance characteristics rather than above traditional methods. Because the mechanical behavior of bridges with various kinds of damage can not be made clear, it is very difficult to estimate both the damage mode and degree of damage of existing bridges. In this paper, the sensitivity of both static and dynamic behaviors of bridges are studied as a measure of damage assessment through experiments on model bridges induced with some specified artificial damages. And, a method of damage assessment of bridges based on those behaviors is discussed in detail. Finally, based on the results, a possible application for structural health monitoring systems for existing bridges is also discussed.

• Smart Structures and Systems
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• 제2권2호
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• pp.127-140
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• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• Advances in concrete construction
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• 제2권4호
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• pp.261-280
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• 2014

In many parts of the world, reinforced concrete (RC) buildings, designed and built in accordance with older codes, have suffered severe damage or even collapse as a result of recent near-fault earthquakes. This is particularly due to the deficiencies of most of the older (and even some of the recent) codes in dealing with near fault events. In this study, a tested three-storey frame designed for gravity loads only was selected to represent those deficient buildings. Nonlinear time history analyses were performed, followed by damage assessment procedures. The results were compared with experimental observation of the same frame showing a good match. Damage and fragility analyses of the frame subjected to 204 pulse-type motions were then performed using a selected damage model and inter-storey drifts. The results showed that the frame located in near-fault regions is extremely vulnerable to ground motions. The results also showed that the damage model better captures the damage distribution in the frame than inter-storey drifts. The first storey was identified as the most fragile and the inner columns of the first storey suffered most damage as indicated by the damage index. The findings would be helpful in the decision making process prior to the strengthening of buildings in near-fault regions.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.243-248
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• 1999

From the lessons after the Nakhodka oil-spill in Jan. 1997, oil slick detection by using remote sensing data and assimilating the data to the simulation program is important for monitoring the oil-drift pattern. For this object, we are going to construct the oil-spill warning system for estimating the oil-drift pattern using remotesensing/numerical simulation Model. Additionally we plan to use this system for restorating oil-spill damage domestically, such as estimating the ecological damage and making the priority fur restorating the oil-spilled shoreline. This report is intended to summarize the role of geo-informatics in the oil spill accident by not only paying attention to the effect of information provision/information management via the map, but also reporting the interim result in part based on the details discussed in the processes of recovery support and environmental impact assessment during the Nakhodka's accident.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2000년도 춘계학술대회 발표논문집
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• pp.213-222
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• 2000

In present study, a stochastic model is developed for the low cycle fatigue life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. In the proposed model, fatigue phenomenon is considered as a Markov process, and damage vector and reliability are defined on every plane. Any low cycle fatigue damage evaluating method can be included in the proposed model. The model enables calculation of statistical reliability and crack initiation direction under variable multiaxial loading, which are generally not available. In present study, a critical plane method proposed by Kandil et al., maximum tensile strain range, and von Mises equivalent strain range are used to calculate fatigue damage. When the critical plane method is chosen, the effect of multiple critical planes is also included in the proposed model. Maximum tensile strain and von Mises strain methods are used for the demonstration of the generality of the proposed model. The material properties and the stochastic model parameters are obtained from uniaxial tests only. The stochastic model made of the parameters obtained from the uniaxial tests is applied to the life prediction and reliability assessment of 316L stainless steel under variable multiaxial loading. The predicted results show good accordance with experimental results.

• Earthquakes and Structures
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• 제26권2호
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• pp.87-96
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• 2024

This paper proposes a logistic multinomial regression approach to model the spatial cross-correlation of damage probabilities among different damage states in an expanded transportation network. Utilizing Bayesian theory and the multinomial logistic model, we analyze the damage states and probabilities of bridges while incorporating damage correlation. This correlation is considered both between bridges in a network and within each bridge's damage states. The correlation model of damage probabilities is applied to the seismic assessment of a portion of Tehran's transportation network, encompassing 26 bridges. Additionally, we introduce extra daily traffic time (EDTT) as an operational parameter of the transportation network and employ the shortest path algorithm to determine the path between two nodes. Our results demonstrate that incorporating the correlation of damage probabilities reduces the travel time of the selected network. The average decrease in travel time for the correlated case compared to the uncorrelated case, using two selected EDTT models, is 53% and 71%, respectively.

• 한국구조물진단유지관리공학회 논문집
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• 제7권4호
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• pp.121-128
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• 2003

토목구조물의 손상평가를 위해 현재까지 인공신경망이 유형분류기로 많이 이용되어왔다. 그러나 본 논문에서는 신경망을 구조재해석기로 사용하여 최적화에 의한 모델업데이팅을 이용한 손상평가를 수행하였다. 최적화에 의한 모델업데이팅을 위해 손상전후의 구조물 모드형상의 절대차의 합을 목적함수로 하였으며, 부재의 강성을 미지의 변수로 취급하였다. 본 손상평가 알고리즘의 적용성을 검토하기 위해 단순보 형태의 판형교 모델에 수치적으로 적용하였다. 적용결과 구조 재해석을 위해 유한요소법을 이용한 결과와 유사한 정도의 손상추정 결과를 얻었다.

• Wind and Structures
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• 제19권3호
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• pp.321-337
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• 2014

This study has developed an Enhanced Remote-Sensing (ERS) scale to improve the accuracy and efficiency of using remote-sensing images of residential building to predict their damage conditions. The new scale, by incorporating multiple damage states observable on remote-sensing imagery, substantially reduces measurement errors and increases the amount of information retained. A ground damage survey was conducted six days after the Joplin EF 5 tornado in 2011. A total of 1,400 one- and two-family residences (FR12) were selected and their damage states were evaluated based on Degree of Damage (DOD) in the Enhanced Fujita (EF) scale. A subsequent remote-sensing survey was performed to rate damages with the ERS scale using high-resolution aerial imagery. Results from Ordinary Least Square regression indicate that ERS-derived damage states could reliably predict the ground level damage with 94% of variance in DOD explained by ERS. The superior performance is mainly because ERS extracts more information. The regression model developed can be used for future rapid assessment of tornado damages. In addition, this study provides strong empirical evidence for the effectiveness of the ERS scale and remote-sensing technology for assessment of damages from tornadoes and other wind events.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1990년도 가을 학술발표회 논문집
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• pp.45-50
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• 1990

This study develops practical models and methods for the assessment of safety and capacity rating of existing P.C. girder bridges based on the reliability methods. One of the main objectives of the study is to propose a practical but realistic limit state model for safety assessment and LRFR rating criteria, which explicitly incorporates the degree of deterioration and damage as well as actual condition of P.C. girder bridges in terms of the damage factor and the response ratio. The damage factor proposed in the paper is defined as the ratio of the current estimated stiffness to the intact base-line stiffness of a member. Based on the observation and the results of applications to existing bridges, it may be concluded that the proposed methods for the assessment and capacity rating models, which explicitly account for the uncertainties and effects of degree of deterioration or damage, provide more realistic and consistent safety-assessment and capacity rating.