ANN-based Structural Performance Model for Reliable Bridge Asset Management

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 of Time 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.

[1]  Mehdi Khashei,et al.  Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) , 2009, Neurocomputing.

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[3]  Yew-Chaye Loo,et al.  Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM) , 2008 .

[4]  Wei-Zhen Lu,et al.  Using Time-Delay Neural Network Combined with Genetic Algorithms to Predict Runoff Level of Linshan Watershed, Sichuan, China , 2007 .

[5]  Seymour Geisser,et al.  8. Predictive Inference: An Introduction , 1995 .

[6]  Ming Rao,et al.  An on-line wastewater quality predication system based on a time-delay neural network , 1998 .

[7]  Andrew Kusiak,et al.  Selection and validation of predictive regression and neural network models based on designed experiments , 2006 .

[8]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[9]  Pawan Lingras,et al.  Short-term traffic prediction on different types of roads with genetically designed regression and time delay neural network models , 2005 .

[10]  E Czepiel.  BRIDGE MANAGEMENT SYSTEMS LITERATURE REVIEW AND SEARCH , 1995 .

[11]  E. Ziegel.  Forecasting and Time Series: An Applied Approach , 2000 .

[12]  George Morcous,et al.  Modeling Bridge Deterioration Using Case-Based Reasoning , 2002 .

[13]  Nowrouz Kohzadi,et al.  A comparison of artificial neural network and time series models for forecasting commodity prices , 1996, Neurocomputing.