• Smart Structures and Systems
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• v.13 no.6
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.533-538
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• 2005

An outline of an Artificial Neural Network (ANN) model for bridge condition rating and the results of a pilot study are presented in this paper. Most BMS implementation systems involve an extensive range of data collection to operate accurately. It takes many years to effectively implement a BMS using existing methodologies. This is due to unmatched data requirements. Such problems can be overcome by adopting the ANN model presented in this paper. The objective of the proposed model is to predict bridge condition ratings using historical bridge inspection data for effective BMS operation.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.493-500
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• 2000

Rock Mass Rating has been widely applied to the underground tunnel excavation and many other practical problems in rock engineering. However, Rock Mass Rating is hard to make out because it is difficult to estimate each valuation items through all kind of field situations and items of RMR have interdependence. So the experts of tunnel assessment have problems with rating rock mass. In this study, using multivariate analysis based on domestic data(1011EA) of water conveyance tunnel, we presented rock mass rating system which is objective and easy to use. The constituents of RMR are decided to RQD, condition of discontinuities, groundwater conditions, orientation of discontinuities, intact rock strength, spacing of discontinuities in important order. In each step, we proposed the best multiple regression model for RMR system. And using data which have been collected at other site, we examined that presented multiple regression model was useful.

• Smart Structures and Systems
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• v.15 no.3
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• pp.751-767
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• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.6
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• pp.871-881
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• 2012

This study suggested a model that can predict a degradation condition over time of two plastic pipes, PE and PVC, which are currently used in the country. This study was analyzed physical characteristics change of plastic pipes by comparison with initial physical characteristics (on the case of new pipes). Since this is dependent on accidents that already occurred, there are limitations that it only decides a priority on improvement based on relative corrosion status rather than precautionary aspects. The comparison results between physical degradation by the deducted performance rating and a conventional numerical scoring method showed that correlation coefficient was 0.67 for PE pipes and 0.86 for PVC pipes, indicating a high correlation. According to this result, it has been decided that the performance rating suggested herein can be applied naturally to the criterion of an improvement decision, which was based on Scoring System. From results of the research, it is expected that a reliable result can be provided to an improvement decision process related to degradation of plastic pipes by comprehensively comparing and evaluating a condition of pipe materials(direct factors) and an environmental impact(indirect factors).

• Proceedings of the Korea Concrete Institute Conference
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• 1990.10a
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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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.517-526
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• 2004

In the resent years, the early evaluation of concrete quality, construction and maintenance has been considered as all is of major concern due to the increase of loading and the degradation of structures related with time. This paper presents evaluation of structural safety performance using measured data of construction, on the basis of a field measurements for the prevention of unreliable concrete works. Measurements analyzed in this paper are early quality condition and performance assessment, serviceability performance by cracks and deflection, rating performance by loading, durability performance by chloride attack and carbonation. Thus, a quantitative assessment model of resistance capacity was developed here to meet the requirement for deteriorated concrete structures. The model focuses on damage mechanical of concrete structures deteriorated by initial damage factors for concrete quality and environment factors such as chloride and carbonation attacks. These results could provide useful information for concrete structures interested in design, construction and maintenance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.2 no.2
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• pp.41-49
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• 2000

Rock Mass Rating has been widely applied to the underground tunnel excavation and many other practical problems in rock engineering. However, Rock Mass Rating is hard, even by the experts of tunnel assessment owing to lack of investigation system. In this study, using multivariate analysis we presented rock mass rating system that is objective and easy to use. The constituents of RMR are decided to RQD, condition of discontinuities, groundwater conditions, intact rock strength, orientation of discontinuities, spacing of discontinuities in important order. In each step, we proposed the best multiple regression model for RMR system.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.11 s.254
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• pp.1093-1100
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• 2006

Combustion stability rating of jet injector is conducted numerically using air-injection technique in a model chamber, where air is supplied to oxidizer and fuel manifolds of the model five-element injector head. A sample F(fuel)-O(oxidizer)-O-F impinging-jet injector is adopted. In this technique, we can simulate mixing process of streams flowing through oxidizer and fuel orifices under cold-flow condition without chemical reaction. The model chamber was designed based on the methodologies proposed in the previous work regarding geometrical dimensions and operating conditions. From numerical data, unstable regions can be identified and they are compared with those from air-injection acoustic and hot-fire tests. The present stability boundaries are in a good agreement with experimental results. The proposed numerical method can be applied cost-effectively to stability rating of jet injectors when mixing of fuel and oxidizer jets is the dominant process in instability triggering.

• Journal of KIBIM
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• v.6 no.2
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• pp.29-38
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• 2016

Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.