• Journal of the Korea institute for structural maintenance and inspection
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• v.5 no.1
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• pp.131-140
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• 2001

In general, the deterioration induced by the freezing and thawing cyclic in concrete structures often leads to the reduction in concrete durability by the cracking or surface spalling. If it can prediction of concrete deterioration subjected to cyclic freezing and thawing, we can rationally do the design of mix proportion in view of concrete durability and the maintenance management of concrete structures. Therefore in this study a prediction method of deterioration for concrete structures subjected to the irregular freezing and thawing is proposed from the results of accelerated laboratory freezing and thawing test using the constant temperature condition and the in-situ weathering data. Furthermore, to accurately predict the concrete deterioration, a method of modification for the effect of hydration increasing during rapid freezing and thawing test is investigated.

• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.61-74
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• 2003

This study(II) apply to the A city by using the optimal rehabilitation model based on the deterioration prediction of the water distribution system proposed the study(I). The deterioration prediction model divides factors into 14 factors with digging and experiment and 9 factor without digging and experiment and calculate the deterioration degree. The application results of the deterioration prediction model show that a difference of the deterioration degree according to factor numbers is within 1~2%. Also, the model can predict the deterioration degree of each pipe without digging and experiment. The optimal rehabilitation model is divided into the optimal residual durability of each deterioration factor and budget constraint or not. The application result is as follow: the rehabilitation time and cost increase according to the increasing of the optimal residual durability. When compared the model with budget constraint and model without budget constraint, the former model increase the cost of total contents. In case of budget constraint, the increasing tendency is concluded that the pipe rehabilitation is executed in same budget every year in condition that every rehabilitation cost do not exceed the every year budget within the optimal residual durability.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.2
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• pp.155-165
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• 2016

Pipe Deterioration Prediction (PDP) and Pipe Failure Risk Prediction (PFRP) models were developed in an attempt to predict the deterioration and failure risk in water mains using fuzzy technique and the markov process. These two models were used to determine the priority in repair and replacement, by predicting the deterioration degree, deterioration rate, failure possibility and remaining life in a study sample comprising 32 water mains. From an analysis approach based on conservative risk with a medium policy risk, the remaining life for 30 of the 32 water mains was less than 5 years for 2 mains (7%), 5-10 years for 8 (27%), 10-15 years for 7 (23%), 15-20 years for 5 (17%), 20-25 years for 5 (17%), and 25 years or more for 2 (7%).

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.261-267
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• 2010

Present maintenance of a high speed railway is after the fack maintenance that executes a task when measured value goes over threshold value except some planned maintenance. It is difficult from efficient management of maintenance human resource and equipment commitment because it is difficult to predict quantity of maintenance targets. Corrective maintenance is pushed back on the repair priority of other target to need repair and it is exceeded repair cost potentially. For safety and dependable track management because track deterioration prediction is linked directly with track's life and safety of train service, it is very important that track management be based on preventive maintenance. In this study, we propose statistics model of track quality to use track inspection data and forecast model for track deterioration prediction.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• Structural Engineering and Mechanics
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• v.37 no.6
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• pp.685-691
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• 2011

Developing accurate prediction models for deterioration behavior represents a challenging but essential task in comprehensive Infrastructure Management Systems. The challenge may be a result of the lack of historical data, impact of unforeseen parameters, and/or the past repair/maintenance practices. These realities contribute heavily to the noticeable variability in deterioration behavior even among similar components. This paper introduces a novel approach to predict the deterioration of any infrastructure component. The approach is general as it fits any component, however the prediction is custom for a specific item to consider the inherent impacts of expected and unexpected parameters that affect its unique deterioration behavior.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.10a
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• pp.795-798
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• 1999

Deterioration induced by the freezing and thawing in concrete often leads to the reduction in concrete durability by the cracking or surface spalling. In this paper, the deterioration prediction model for concrete structures subjected to the irregular freeze-thaw was proposed from the results of accelerated laboratory test using the constant temperature condition and acceleration factor from the in-situ weather data.

• Structural Engineering and Mechanics
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• v.91 no.1
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• pp.39-47
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• 2024

Bridge deterioration shows the change of bridge condition during its operation, and predicting bridge deterioration is important for implementing predictive protection and planning future maintenance. However, in practical application, the raw inspection data of bridges are not continuous, which has a greater impact on the accuracy of the prediction results. Therefore, two kinds of bridge deterioration models are established in this paper: one is based on the traditional regression theory, combined with the distribution fitting theory to preprocess the data, which solves the problem of irregular distribution and incomplete quantity of raw data. Secondly, based on the theory of Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the network is trained using the raw inspection data, which can realize the prediction of the future deterioration of bridges through the historical data. And the inspection data of 60 prestressed concrete box girder bridges in Xiamen, China are used as an example for validation and comparative analysis, and the results show that both deterioration models can predict the deterioration of prestressed concrete box girder bridges. The regression model shows that the bridge deteriorates gradually, while the LSTM-RNN model shows that the bridge keeps great condition during the first 5 years and degrades rapidly from 5 years to 15 years. Based on the current inspection database, the LSTM-RNN model performs better than the regression model because it has smaller prediction error. With the continuous improvement of the database, the results of this study can be extended to other bridge types or other degradation factors can be introduced to improve the accuracy and usefulness of the deterioration model.

• Journal of the Korean Society of Safety
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• v.23 no.4
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• pp.59-66
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• 2008

Recently, the deterioration of reinforced concrete structures, primarily due to corrosion of steel reinforcement, has become a major concern. Chloride-induced deterioration is the most important deterioration phenomenon in reinforced concrete structures in harsh environments. For the realistic prediction of chloride penetration into concrete, a mathematical model was developed in which the effects of diffusion, chloride binding and convection due to water movement can be taken into account. The aim of this research was to reach a better understanding on the physical mechanisms underlying the deterioration process of reinforced concrete associated with chloride-induced corrosion and to propose a reliable method for estimating these effects. Chloride concentrations coming from de-icing salts are significantly influenced by the exposure conditions such as salt usage, ambient temperature and repeated wet-dry cycles.