• 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.

• 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%).

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.70-75
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• 2007

This paper discusses condition based preventive replacement for deteriorating systems. The system continuouslydeteriorates in time and fails at any deterioration level which is always monitored, It is replaced at failure or atsome deteriorated level preventively before failure. The deterioration process is represented by a Weibulldistribution with a time-linear scale parameter. The cost rate function is formed considering replacement costand opportunity loss cost and deterioration dependent failure distribution, If the system has an increasingdeterioration dependent failure rate, the optimal deterioration level for preventive replacement can be determinedfrom minimizing the cost rate. An illustrative example is given for a Weibull deterioration dependent failuredistribution.

• Computers and Concrete
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• v.6 no.6
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• pp.473-490
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• 2009

This paper presents a new methodology to evaluate the load carrying capacity of deteriorated non-slender concrete bridge pier columns by construction of the full P-M interaction diagrams. The proposed method incorporates the actual material properties of deteriorated columns, and accounts for amount of corrosion and exposed corroded bar length, concrete loss, loss of concrete confinement and strength due to stirrup deterioration, bond failure, and type of stresses in the corroded reinforcement. The developed structural model and the damaged material models are integrated in a spreadsheet for evaluating the load carrying capacity for different deterioration stages and/or corrosion amounts. Available experimental and analytical data for the effects of corrosion on short columns subject to axial loads combined with moments (eccentricity induced) are used to verify the accuracy of proposed model. It was observed that, for the limited available experimental data, the proposed model is conservative and is capable of predicting the load carrying capacity of deteriorated reinforced concrete columns with reasonable accuracy. The proposed analytical method will improve the understanding of effects of deterioration on structural members, and allow engineers to qualitatively assess load carrying capacity of deteriorated reinforced concrete bridge pier columns.

• 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.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1199-1201
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• 2003

This paper presents an averaged switch model for an on-line deterioration analysis of inverters. The model is based on the operational characteristics of power diodes and IGBTS. Switching loss mechanisms are described and divided into the diode reverse-recovery mechanism, the IGBT current-tailing phenomenon, the IGBT turn-on time, and the output capacitances of the IGBTS and diodes. The deterioration analysis parameters are derived from the averaged switch modeling and the parameters can determinate the deterioration of the inverters.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.08a
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• pp.142-145
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• 2002

This paper describes an influence of system voltage harmonics on arrester deterioration diagnostic techniques based on leakage current measurement because the resistive current is composed of two components caused by nonlinear characteristics of arrester and by system voltage harmonics. Resistive leakage currents of arresters, which can be evaluated by the third harmonic component of total leakage currents, increase with its deterioration progress. In this paper, we developed a PSpice model for ZnO arrester to simulate the harmonics' effect described above. In simulation, pure sinusoidal voltage and the $3^{rd}$ harmonic voltage are applied to the model, and the leakage current changes are compared. The simulation results showed that the magnitudes of resistive leakage current depend not only on the phase of system voltage harmonics but also on the magnitude of it.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1096-1101
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• 2014

This study (II) has appled the new assessment model of large-diameter pipe deterioration proposed by the study (I) to the metropolitan multi-regional water supply system (1st phase). In the total 30 pipelines, 24 and 27 pipelines were required for improvement as results from the existing evaluation methods 1995 and 2002, respectively. The assessment results were almost similar in the new developed model and the existing methods. It is founded that the new simple model developed in this study can produce reliable results, consistent with those from the existing methods requiring many factors for a pipe deterioration assessment. It is therefore expected that the new model would be helpful in practical applications of a pipe deterioration assessment since it can save both temporal and economic costs for experiments and analysis, as compared with existing assessment methods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.239-244
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• 2022

In this study, the key parameters affecting the deterioration of each track type were derived based on field inspections and laboratory tests. The existing track deterioration model was limited to the ballasted tracks, and the deterioration evaluation of concrete tracks was insufficient. In this study, the laboratory test was performed to evaluate the performance and condition of track components to derive the deterioration factors reflecting the characteristics of various track structures. In addition, through analysis of track maintenance history data, parameters affecting track deterioration and maintenance were derived. The key parameters for presenting a track deterioration model based on the track performance of ballasted and concrete tracks through field inspection, track maintenance history data analysis, and performance test of track components using on-site specimens were identified as track support stiffness, Ballast gravel, track settlement and Resilience pad were presented.