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Bridge Management Systems (BMSs) have been developed since the early 1990s, as a decision support system (DSS) for effective Maintenance, Repair and Rehabilitation (MR&R) activities in a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations through BMSs. However, available historical condition ratings are very limited in all bridge agencies. This constitutes the major barrier for achieving reliable future structural performances. To alleviate this problem, a Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. Its reliability has been verified using existing condition ratings obtained from the Maryland Department of Transportation, USA. This is achieved through establishing the correlation between known condition ratings and related non-bridge factors such as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to determine 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 chapter, the prediction accuracy based on four and nine BPM-generated historical condition ratings as input data are also compared, using traditional bridge deterioration modeling techniques, i.e., deterministic and stochastic methods. The condition ratings are available. This implies that the BPM can be utilized 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. ©2011 Nova Science Publishers, Inc. All rights reserved.

Bridge Management Systems (BMSs) have been developed to assist in the management of a large bridge network. Historical condition ratings obtained from bridge inspections are major resources for predicting future deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for predicting reliable future structural performance. To alleviate this problem, A Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings which is crucial for bridge deterioration models to be able to predict more accurate solutions. Nevertheless, there are still considerable limitations in the existing bridge deterioration models. In view of this, feasibility study ofTime Delay Neural Network (TDNN) using BPM-generated historical condition ratings is conducted as an alternative to existing bridge deterioration models. It is anticipated that the TDNN using BPM-generated data can lead to further improvement of the current BMS outcome. © 2011 Taylor & Francis Group, London.

Bridge inspection is a pathway to bridge condition rating assessment, and is an essential element of any bridge management system (BMS). The success of a BMS is highly dependent on the quality of bridge inspection outcomes and accurate estimation of future bridge condition ratings. However, existing visual bridge inspection methods suffer several limitations due to human subjective judgment. In order to minimise such limitations, a feasibility study has been performed to enhance the current visual inspection method using optical image processing techniques. However, the accuracy of the inspection outcomes still requires further improvement. This paper proposes an automatic bridge inspection approach employing wavelet-based image features along with support vector machines (SVM) for automatic detection of cracks in bridge images. A two-stage approach is followed, in the first stage, a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics); in the second stage, wavelet features are extracted from the image using a sliding window texture analysis-based technique. Consequently, an average accuracy of 92% (effect of training image types on accuracy) is obtained even when undertaking experiments with noisy and complex bridge images.