• Corrosion Science and Technology
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• v.3 no.4
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• pp.166-171
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• 2004

Recent developments in Bridge Management Systems (BMS) and in Life-Cycle Cost (LCC) of bridges, have raised the need for evaluation procedure of future condition (Deterioration) of a bridge. Predicting future deterioration is not an easy task due to limited past data to extrapolate from and also due to difficulty in measuring actual deterioration such as section loss of steel on an actual steel bridge. Also, increase in live load and reduction of resistance are random variables, thus a probabilistic approach should be adopted for determining the future deterioration. Due to difficulties in evaluation of future deterioration on steel bridges, accepting uncertainties within a reasonable error, a deterministic procedure using bridge condition rating can be a useful tool for projection of future condition of bridges to identify repair and maintenance needs. The object of this paper is to determine applicability of evaluating deterioration of steel bridge components based on Bridge condition ratings. Bridge condition ratings of bridge components show wide variation for bridges of same age and does not directly correlate well with the age of the bridge and/or deterioration of the bridge. High uncertainty can be reduced by breaking down the rating and by sensitivity analysis. From refined condition rating data, generalized deterioration profile of structures based on age can be derived. Examples are shown for sample bridges in USA. Approximately, 3,000 short to medium span steel bridges were listed in the inventory database. Results show wide variation of rating factors but by subdividing the Bridge condition ratings for various categories general deterioration profiles of steel bridges can be determined.

• 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.

• 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.

• Computers and Concrete
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• v.2 no.6
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• pp.439-454
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• 2005

When deterioration of concrete is observed in a structure, it is highly desirable to determine the cause of such deterioration. Only by understanding the cause can an appropriate repair strategy be implemented to address both the cause and the symptom. In colder climates, bridge deck deterioration is often caused by chlorides from de-icing salts, which penetrate the concrete and depassivate the embedded reinforcement, causing corrosion. Bridge decks can also suffer from other deterioration mechanisms, such as alkali-silica reaction, freeze-thaw, and shrinkage. There is a need for a comprehensive and integrative system to help with the inspection and evaluation of concrete bridge deck deterioration before decisions are made on the best way to repair it. The purpose of this research was to develop a model to help with the diagnosis of concrete bridge deck deterioration that integrates the symptoms observed during an inspection, various deterioration mechanisms, and the probability of their occurrence given the available data. The model displays the diagnosis result as the probability that one of four deterioration mechanisms, namely shrinkage, corrosion of reinforcement, freeze-thaw and alkali-silica reaction, is at fault. Sensitivity analysis was performed to determine which probabilities in the model require refinement. Two case studies are included in this investigation.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.386-397
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• 2007

One major development in bridge life cycle cost analysis (LCCA) in recent years is to develop deterioration model for bridge components so that the times of repair/replacement throughout a component's life span can be properly determined. Taiwan also developed her own bridge LCCA model in 2003, integrating with the bridge inspection database in the local bridge management system (T-BMS). Under the framework of the local LCCA model, this study employs the reliability method in developing a deterioration model of bridge components. A component deteriorates through time in its reliability, which represents the probability of a component's condition index exceeds a user specified threshold. Model assumptions and rationale are described in the paper. The steps for applying the developed model are explained in detail. Results and findings are reported.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.597-602
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• 2011

A robust asset management methodology is essential for effective decision-making of maintenance, repair and rehabilitation of a bridge network. It can be achieved by a computer-based bridge management system (BMS). Successful BMS development requires a reliable bridge deterioration model, which is the most crucial component in a BMS, and an optimal management philosophy. The maintenance optimization methodology proposed in this paper is developed for a small bridge network with limited structural condition rating records. . The methodology is organized in three major components: (1) bridge health index (BHI); (2) maintenance and budget optimization; and (3) reliable Artificial Intelligence (AI) based bridge deterioration model. The outcomes of the paper will help to identify BMS implementation problems and to provide appropriate solutions for managing small bridge networks.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.357-369
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• 2014

In the existing bridge management systems, assessment of the structural behavior is based on the results of visual inspections in which corresponding condition states are assigned to individual elements. In this process, limited attention is given to the correlation between bridge elements from structural perspective. Also, the uncertainty of parameters which affect the structural capacity is ignored. A system reliability-based assessment model is potentially an appropriate replacement for the existing procedures. The aim of this research is to evaluate the system reliability of existing conventional Steel-Reinforced bridge decks over time. The developed method utilizes the reliability theory and evaluates the structural safety for such bridges based on their failure mechanisms. System reliability analysis has been applied to simply-supported concrete bridge superstructures designed according to the Canadian Highway Bridge Design Code (CHBDC-S6) and the deterioration pattern is achieved based on the reliability estimates. Finally, the bridge condition index of an old existing bridge in Montreal has been estimated using the developed deterioration pattern. The results obtained from the developed reliability-based deterioration model and from the evaluation done by bridge engineers have been found to be in accordance.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.810-819
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• 2007

Usually the status of a bridge is determined by its structural capability and material strength. Consequently a lot of researchers have studied the failure, the fatigue, and the deterioration of the structure in terms of the structural function of a bridge. However, the overall performance of a bridge may be affected simply by the damage of one of its components. Therefore this study utilized a systematic classification and statistical analysis based on the existing bridge inspection data collected in Taiwan to reach the following goals: (1) assess the performance distribution and deterioration rate for bearing and expansion joint of bridge; (2) find out the right time to do the preventive and essential maintenance for the component of bridge with an empirical method, and to decide what time and which component of a bridge will receive preventive maintenance or regular maintenance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.68-75
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• 2015

This study established key factors causing deck slab deterioration based on GPR database of 747 highway bridges, and predicted deck slab damage rates with respect to bridge service life. To minimize the influence of bridge service life on deck slab deterioration, the deck slab damage rate database was corrected based on a linear regression model of bridge service life vs. deck slab damage rate. The corrected deck slab damage rates were analyzed to determine correlation considering the number of snowy days, the amount of snowfalls, the number of freeze-thaw days, average winter temperature, altitude, the amount of deicing chemicals and equivalent traffic volume, and then both the number of freeze-thaw days and the amount of deicing chemicals were determined to be key factors causing deck slab deterioration. The complex deterioration considering both key factors was represented deck slab damage rate charts, and the average deck slab life was derived. The results of this study will be used as a guideline for highway bridge maintenance to identify the progress of deck slab deterioration for a given bridge and predict the time required deck slab rehabilitation.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.1291-1296
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• 2000

A basis for the direct use of data from nondestructive evaluation methods in bridge management systems is presented. Bridge management systems use integer-valued condition ratings to recognize conditions of bridge elements, to model progression of deterioration, and to determine repair needs. Data from nondestructive evaluation methods can inform management systems on the extent of damage, on the initiation of deterioration processes, and on the exposure of bridge elements to aggressive agents. In addition, data obtained through nondestructive evaluation methods allow the formation of models of specific deterioration process. The use of these data in bridge management systems requires redefinition of condition ratings together with the creation of procedures for automated interpretation of data. By these action, nondestructive evaluation methods are directly used to assign condition ratings, and condition ratings are made into terse form of NDE data that are compatible with present day bridge management systems. This paper reports work in progress to strategic use of nondestructive evaluation methods in bridge management system.