• Architectural research
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• v.7 no.2
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• pp.81-89
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• 2005

Over the years, existing bridges have had various degrees of maintenance to extend the service life. As the existing bridges continue to deteriorate, however, each Department of Transportation (DOT) of the United States of America faces increasing demands on the limited funds available for bridge maintenance. Therefore, it is very important for State Department of Transportations to establish Maintenance, Repair, and Rehabilitation (MR&R) strategies for bridge structures such that funds get allocated for appropriate maintenance over the service life. This paper identifies the state-of-art and the state-of-practice of MR&R actions and the use of MR&R strategies in concrete bridge decks. In addition, a questionnaire survey was conducted to identify the type and timing for MR&R actions as well as existing MR&R strategies taken in concrete bridge deck by each DOT. This paper also presents the results of the survey.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.248-258
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• 2020

This study proposed a breakdown structure for maintenance and management technologies under the concept of comprehensive asset management at the life cycle level of infrastructure based on benchmarking with other developed countries. For this purpose, a comparative case study was performed to review and analyze the existing definitions and hierarchies for infrastructure maintenance, repair, and rehabilitation (MR&R) systems under major industrialized countries and South Korea. In accordance with the ratio of maintenance costs to GDP, the U.S., U.K, and Japan were selected to review their systems. The classifications and definitions of MR&R technologies under the laws were analyzed. The result showed that most developed countries differentiate maintenance and repair from improvement and constitute a system centered on preventive maintenance activities. On the other hand, Korea's system for facility management is not definitely classified and still focused on reactive structures, which need to be improved. In this study, as proposed, a breakdown structure established the concept of Maintenance and Management, Maintenance & Repair, and Performance Improvement. Consequently, this study could be used as the basis for the implementation of preventive MR&R activities and reasonable resource allocations from an asset management point of view.

• 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 the Korean Society for Railway
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• v.13 no.1
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• pp.71-77
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• 2010

It is increasing annually that the cost for bridge Maintenance Repair & Rehabilitation (MR&R) in developed countries. Based on Intelligent Technology, Bridge Management System (BMS) is developed for optimization of Life Cycle Cost (LCC) and reliability to predict long-term bridge deteriorations. However, such data are very limited amongst all the known bridge agencies, making it difficult to reliably predict future structural performances. To alleviate this problem, an Artificial Neural Network (ANN) based Backward Prediction Model (BPM) for generating missing historical condition ratings has been developed. Its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since the non-bridge factors used in the BPM can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively based on the minimized discrepancy rate between the BPM prediction result and existing data (deck; 6.68%, superstructure; 6.61%, substructure; 7.52%). This research is on the generation of usable historical data using Artificial Intelligence techniques to reliably predict future bridge deterioration. The outcomes (Long-term Bridge deterioration Prediction) will help bridge authorities to effectively plan maintenance strategies for obtaining the maximum benefit with limited funds.