• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.7
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• pp.36-45
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• 2002

Most of rural bridges have passed 30 years of age since they were built, which have to support unexpected overload caused by changed design load and excessive amount of transportation. For these rural bridges, repairs and replacements are needed. Even though there have been attempt to estimate the safety of existing bridges deteriorated with major defects, those approaches must rely on the observable damage and subsequent decisions are made subjectively. To avoid the high cost of rehabilitation, the bridge rating must correctly represent the present load-carrying capacity. Rating engineers use a methods such as Allowable Stress Design (ASD), Load Factor Design (LFD), and Load Resistance Factor Design (LRFD) to evaluate the bridge load carrying capacity. In this paper, the load rating methods are introduced, and it is illustrated how to use the load test data from literature survey. Load test is conducted to the bridge that was built 30 years ago in rural area. From load test results, new maintenance method is suggested instead of the bridge replacement.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.167-177
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• 2024

Given increasing concerns regarding environmental issues, environmentally responsible behavior during the construction of cross-sea bridge projects becomes critical. However, a systematic investigation of the topic remains absent. To bridge the knowledge gap, this study first defines environmentally responsible behavior from four perspectives: purposes, means, contents and influencing factors. Then, the study uses a grounded theory approach to code and analyze 101 documentations retrieved from 30 cross-sea bridge projects carried out by Chinese construction companies worldwide. Results show that environmentally responsible behaviors in cross-sea bridge projects are highly influenced by four factors, namely government policies, public opinions, goals of the projects, and construction companies'philosophy of environmental protection. Results also indicate that Chinese construction companies have used managerial, technical, and ecological means to ensure that the goals of environmentally responsible behaviors, namely minimization of damage to marine ecology; prevention of air, noise and visual pollution; and reduction of resource consumptions, are achieved. Lastly, suggestions for promoting and governing environmentally responsible behavior are proposed.

• Journal of the Korea Institute of Building Construction
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• v.20 no.2
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• pp.137-145
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• 2020

The recent bridge construction projects demand thorough and systematic safety and risk management, due to the increase of risk factors following the introduction of new and complex construction methods and technologies. Among many types of damages that can occur in bridge construction projects, the damages to third parties who are not directly related to the existing property of the contractor construction project can also bring about critical loss in the project in order to compensate the damages. Therefore, risks that could be caused by the loss occurred to indemnify the third party damages should be clearly analyzed, although there are not subsequent amount of studies focusing on the issue. Based on the past record of insurance payment from domestic insurance companies for bridge construction projects, this study aimed to analyze the risk factors of bridge construction for loss caused to compensate the third-party damages happened in actual bridge construction projects and to develop a quantified and numerical predictive loss model. In order to develop the model, the loss ratio was selected as the dependent variable; and among many analyzed independent variables, the superstructure, foundation, flood, and ranking of contractors were the four significant risk factor variables that affect the loss ratio. The results produced can be used as an essential guidance for balanced risk assessment, supplementing the existing analysis on material losses in bridge construction projects by taking into account the third-party damage and losses.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.556-567
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• 2021

Purpose: In this study, various methods of deep learning-based automatic damage analysis technology were reviewed based on images taken through Unmanned Aerial Vehicle to more efficiently and reliably inspect the exterior inspection and inspection of railway bridges using Unmanned Aerial Vehicle. Method: A deep learning analysis model was created by defining damage items based on the acquired images and extracting deep learning data. In addition, the model that learned the damage images for cracks, concrete and paint scaling·spalling, leakage, and Reinforcement exposure among damage of railway bridges was applied and tested with the results of automatic damage analysis. Result: As a result of the analysis, a method with an average detection recall of 95% or more was confirmed. This analysis technology enables more objective and accurate damage detection compared to the existing visual inspection results. Conclusion: through the developed technology in this study, it is expected that it will be possible to analysis more accurate results, shorter time and reduce costs by using the automatic damage analysis technology using Unmanned Aerial Vehicle in railway maintenance.

• Smart Structures and Systems
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• v.30 no.6
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• Earthquakes and Structures
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• v.3 no.5
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• pp.649-673
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• 2012

Recent studies have highlighted the importance of accounting for aging and deterioration of bridges when estimating their seismic vulnerability. Effects of structural degradation of multiple bridge components, variations in bridge geometry, and comparison of different environmental exposure conditions have traditionally been ignored in the development of seismic fragility curves for aging concrete highway bridges. This study focuses on the degradation of multiple bridge components of a geometrically varying bridge class, as opposed to a single bridge sample, to arrive at time-dependent seismic bridge fragility curves. The effects of different exposure conditions are also explored to assess the impact of severity of the environment on bridge seismic vulnerability. The proposed methodology is demonstrated on a representative class of aging multi-span reinforced concrete girder bridges typical of the Central and Southeastern United States. The results reveal the importance of considering multiple deterioration mechanisms, including the significance of degrading elastomeric bearings along with the corroding reinforced concrete columns, in fragility modeling of aging bridge classes. Additionally, assessment of the relative severity of exposure to marine atmospheric, marine sea-splash and deicing salts, and shows 5%, 9% and 44% reduction, respectively, in the median value bridge fragility for the complete damage state relative to the as-built pristine structure.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.308-315
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• 2002

Fragility analysis is widely used for the seismic safety evaluation of a structure. In fragility analysis, damage evaluation is a crucial factor. Most of the present fragility analyses use the representative responses such as displacement and absorbed hysteretic energy as a tool of damage evaluation. But damage evaluation method that can represent the local damage of a structure is required in the case of piers of which the local damage can cause the whole failure of bridge system. Therefore this study proposes a damage index, which can represent the distribution and magnitude of local damage by using the Lee and Fenves'plastic-damage model. Using the proposed damage index, fragility curves and damage probability matrix of pier are produced and fragility analysis is performed.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.87-94
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• 1995

In highway bridges robust damage detection exercises are mandatory to secure the safety of the structures from hostile environmental conditions such as fatigue earthquake, wind, and corrosion. This paper presents a damage detection practice in a full-scale highway bridge by utilizing modal response parameters of as-built and damaged states of the structure. first the test structure is described and modal testing procedures are outlined. Next, a damage detection model which yields information on the location of damage directly from changes in mode shapes is outlined. Finally, the damage detection model is implemented to predict the location of damage in the ten structure. From the results, it was found that the damage detection model accurately locates damage in the test structures for which modal parameters of only a single mode are available for pre-damage (as-built) and post-damage stages.