Guoping Bu

Development of an Integrated Method for Probabilistic Bridge-Deterioration Modeling

Probabilistic deterioration models such as state-based and time-based models are only capable of predicting future bridge-condition ratings when a sufficient amount of condition data and reasonable data distribution are available. However, such are usually difficult to acquire from limited bridge-inspection records. As a result, these probabilistic models cannot guarantee reliable long-term prediction for each of the bridge elements concerned. To minimize this shortcoming, this paper proposes an advanced integrated method to construct workable transition probabilities for predicting long-term bridge performance. A selection process within this method automatically chooses a suitable prediction procedure for a given situation in terms of available inspection data. The backward prediction model (BPM) is also incorporated to effectively predict the bridge performance when sufficient inspection data are unavailable. Four different situations in regard to the available inspection data are predefined in this study to demonstrate the capabilities of the proposed integrated method. The outcomes show that the method can help develop an effective prediction model for various situations in terms of the quantity and distribution of available condition-rating data.

Automatic Bridge Crack Detection A Texture Analysis-Based Approach

To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. This article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first stage a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics), and later in the second stage, wavelet features are extracted from the image using a sliding window-based technique. We obtained an overall accuracy of 92.11% while conducting experiments even on noisy and complex bridge images.

Prediction of Long-Term Bridge Performance: Integrated Deterioration Approach with Case Studies

AbstractA bridge-deterioration approach is to predict the condition ratings and the deterioration pattern of bridge elements for determining optimal maintenance strategies and estimating future funding requirements. To effectively predict long-term bridge performance, an advanced integrated deterioration approach has been developed that incorporates a time-based model, a state-based model with the Elman neural network (ENN) and a backward prediction model (BPM). The proposed approach involves the categorization of the selected inspection records by bridge components, material types, traffic volume, and the construction era. The primary advantage of such categorization is to group similar components together, thereby identifying the common deterioration patterns. A selection process embedded in the proposed approach offers the ability to automatically select the most appropriate model for predicting future bridge condition ratings. To demonstrate the advantage of the proposed approach in predicting long-te...

Implementation of Elman Neural Networks for Enhancing Reliability of Integrated Bridge Deterioration Model

Probabilistic modelling is one of the most prominent techniques in bridge deterioration forecast. It can be classified into two types, namely, state- and time-based models. Reliability of both modelling techniques in forecasting long-term performance rely heavily on sufficient amount of bridge condition rating data being available together with well-distributed deterioration pattern over the age of bridge. However, it can be problematic when the available condition rating records are insufficient. In order to overcome this problem, an integrated deterioration method incorporating both the state- and time-based models has recently been developed. Despite such development and advancement, certain issues still remain with some cases of given condition data that cannot be used to produce reliable long-term performance curve. Aiming to achieve enhanced prediction performance, an Elman neural networks (ENN) technique is incorporated in the integrated method to replace the third-order polynomial regression function, the latter being the core component for long-term prediction in the state-based model. In the present study, the ENN are able to generate more reliable deterioration patterns than a typical deterministic method. The results demonstrate that the integrated method incorporating ENN are more effective in handling various situations of condition data quantities and distributions for generating long-term performance curves.

Long-Term Performance of Bridge Elements Using Integrated Deterioration Method Incorporating Elman Neural Network

Currently, probabilistic deterioration modeling techniques have been employed in most state-of-the-art Bridge Management Systems (BMSs) to predict future bridge condition ratings. As confirmed by many researchers, the reliability of the probabilistic deterioration models rely heavily on the sufficient amount of condition data together with their well-distributed historical deterioration patterns over time. However, inspection records are usually insufficient in most bridge agencies. As a result, a typical standalone probabilistic model (e.g. state-based or time-based model) is not promising for forecasting a reliable bridge long-term performance. To minimise the shortcomings of lacking condition data, an integrated method using a combination of state- and time-based techniques has recently been developed and has demonstrated an improved performance as compared to the standalone techniques. However, certain shortcomings still remain in the integrated method which necessities further improvement. In this study, the core component of the state-based modeling is replaced by an Elman Neural Networks (ENN). The integrated method incorporated with ENN is more effective in predicting long-term bridge performance as compared to the typical deterioration modeling techniques. As part of comprehensive case studies, this paper presents the deterioration prediction of 52 bridge elements with material types of Steel (S), Timber (T) and Other (O). These elements are selected from 94 bridges (totaling 4,115 inspection records). The enhanced reliability of the proposed integrated method incorporating ENN is confirmed.

Performance Prediction of Concrete Elements in Bridge Substructures using Integrated Deterioration Method

The typical probabilistic deterioration model cannot guarantee a reliable long-term prediction for various situations of available condition data. To minimise this limitation, this paper presents an advanced integrated method using state-/time-based model to build a reliable transition probability for prediction long-term performance of bridge elements. A selection process is developed in this method to automatically select a suitable prediction approach for a given situations of condition data. Furthermore, a Backward Prediction Model (BPM) is employed to effectively prediction the bridge performance when the inspection data are insufficient. In this study, a benchmark example-concrete element in bridge substructures is selected to demonstrate that the BPM in conjunction with time-based model can improve the reliability of long-term prediction.