Development and Validation of Deterioration Models for Concrete Bridge Decks: Phase 1: Artificial Intelligence Models and Bridge Management System

This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge inventory (NBI) database were used to develop the ANN models. Two types of artificial neural networks, multi-layer perceptrons and ensembles of neural networks (ENNs), were developed and their performance was evaluated by comparing them against recorded field inspections and using statistical methods. The MLP and ENN models had an average predictive capability across all ratings of 83% and 85%, respectively, when allowed a variance equal to bridge inspectors. A method to extract the influence of parameters from the ANN models was implemented and the results are consistent with the expectations from engineering judgment. An approach for generalizing the neural networks for a population of bridges was developed and compared with Markov chain methods. Thus, the developed ANN models allow modeling of bridge deck deterioration at the project (i.e., a specific existing or new bridge) and system/network levels. Further, the generalized ANN degradation curves provided a more detailed degradation profile than what can be generated using Markov models. A bridge management system (BMS) that optimizes the allocation of repair and maintenance funds for a network of bridges is proposed. The BMS uses a genetic algorithm and the trained ENN models to predict bridge deck degradation. Employing the proposed BMS leads to the selection of optimal bridge repair strategies to protect valuable infrastructure assets while satisfying budgetary constraints. A program for deck degradation modeling based on trained ENN models was developed as part of this project.

[1]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[2]  Dan M. Frangopol,et al.  Computational Platform for Predicting Lifetime System Reliability Profiles for Different Structure Types in a Network , 2004 .

[3]  A. Samer Ezeldin,et al.  Materials for civil and highway engineers , 1981 .

[4]  A. Sharkey.  Linear and Order Statistics Combiners for Pattern Classification , 1999 .

[5]  Benjamin A. Graybeal,et al.  RELIABILITY OF VISUAL BRIDGE INSPECTION , 2001 .

[6]  Pedro M. Domingos.  Using Partitioning to Speed Up Specific-to-General Rule Induction , 1996 .

[7]  Yu-Bin Yang,et al.  Lung cancer cell identification based on artificial neural network ensembles , 2002, Artif. Intell. Medicine.

[8]  Zhe Li,et al.  Using Soft Computing to Analyze Inspection Results for Bridge Evaluation and Management , 2010 .

[9]  Tarek Hegazy,et al.  Bridge Deck Management System with Integrated Life-Cycle Cost Optimization , 2004 .

[10]  Lior Rokach,et al.  Ensemble Methods for Classifiers , 2005, The Data Mining and Knowledge Discovery Handbook.

[11]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[12]  Julio A. Ramirez,et al.  Methods of Corrosion Protection and Durability of Concrete Bridge Decks Reinforced with Epoxy-Coated Bars - Phase I , 1999 .

[13]  Bilal M. Ayyub,et al.  Probability, Statistics, and Reliability for Engineers and Scientists , 2003 .

[14]  A. Bachelor.  GLOSSARY OF TERMS GLOSSARY OF TERMS , 2010 .

[15]  Sung-Keun Kim,et al.  GA-based algorithm for selecting optimal repair and rehabilitation methods for reinforced concrete (RC) bridge decks , 2007 .

[16]  Jamshid Mohammadi,et al.  Analysis of Bridge Condition Rating Data Using Neural Networks , 1997 .

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

[18]  Lawrence L. Kupper,et al.  Probability, statistics, and decision for civil engineers , 1970 .

[19]  Dan M. Frangopol,et al.  Multiobjective Maintenance Planning Optimization for Deteriorating Bridges Considering Condition, Safety, and Life-Cycle Cost , 2005 .

[20]  Yi Jiang,et al.  The development of performance prediction and optimization models for bridge management systems , 1990 .

[21]  Dan M. Frangopol,et al.  RELIABILITY-BASED LIFE-CYCLE MANAGEMENT OF HIGHWAY BRIDGES , 2001 .

[22]  Richard J. Kessler,et al.  Corrosion Performance of Epoxy-Coated Rebar in Florida Keys Bridges , 2001 .

[23]  Michael J. Pazzani,et al.  Error reduction through learning multiple descriptions , 2004, Machine Learning.

[24]  J V Tu,et al.  Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.

[25]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[26]  Changqin Liu,et al.  Maintenance Strategy Optimization of Bridge Decks Using Genetic Algorithm , 1997 .

[27]  Bernhard Elsener.  Corrosion of Steel in Concrete , 2013 .

[28]  Rigoberto Burgueno,et al.  Identification of Causes and Solution Strategies for Deck Cracking in Jointless Bridges , 2012 .

[29]  Michael P. Enright,et al.  Probabilistic analysis of resistance degradation of reinforced concrete bridge beams under corrosion , 1998 .

[30]  Xiaohua Hu,et al.  Using rough sets theory and database operations to construct a good ensemble of classifiers for data mining applications , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[31]  Dan M. Frangopol,et al.  Balancing Connectivity of Deteriorating Bridge Networks and Long-Term Maintenance Cost through Optimization , 2005 .

[32]  Timothy L. Jacobs,et al.  Optimal long term scheduling of bridge deck replacement and rehabilitation , 1992 .

[33]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[34]  Kapilesh Bhargava,et al.  Analytical model for time to cover cracking in RC structures due to rebar corrosion , 2006 .

[35]  John O Sobanjo.  A Neural Network Approach to Modeling Bridge Deterioration , 1997 .

[36]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[37]  Moshe Ben-Akiva,et al.  ESTIMATION OF LATENT PAVEMENT PERFORMANCE FROM DAMAGE MEASUREMENTS . THIRD INTERNATIONAL CONFERENCE ON BEARING CAPACITY OF ROADS AND AIRFIELDS. PROCEEDINGS, NORWEGIAN INSTITUTE OF TECHNOLOGY, TRONDHEIM, NORWAY, JULY 3-5 1990. VOLUMES 1-2 , 1989 .

[38]  George Morcous,et al.  Comparing the use of artificial neural networks and case-based reasoning in modeling bridge deterioration , 2002 .

[39]  Tarek Hegazy,et al.  Integrated Multiple-Element Bridge Management System , 2009 .

[40]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[41]  Robert E. Schapire,et al.  The strength of weak learnability , 1990, Mach. Learn..

[42]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[43]  Pc Pandey,et al.  Multilayer perceptron in damage detection of bridge structures , 1995 .

[44]  Frederik A. Ruinen.  Pontis Bridge Management System Software Implementation , 1993 .

[45]  Tarek Zayed,et al.  Condition Rating Model for Underground Infrastructure Sustainable Water Mains , 2006 .

[46]  R. Weyers.  SERVICE LIFE MODEL FOR CONCRETE STRUCTURES IN CHLORIDE LADEN ENVIRONMENTS , 1998 .

[47]  Ying-Hua Huang,et al.  Artificial Neural Network Model of Bridge Deterioration , 2010 .

[48]  Jan A Snyman,et al.  Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms , 2005 .

[49]  Youping Liu,et al.  Modeling the Time-to Corrosion Cracking of the Cover Concrete in Chloride Contaminated Reinforced Concrete Structures , 1996 .

[50]  Teresa M Adams,et al.  Analysis of Life-Cycle Maintenance Strategies for Concrete Bridge Decks , 2004 .

[51]  N. K. Bose,et al.  Neural Network Fundamentals with Graphs, Algorithms and Applications , 1995 .

[52]  Allen R. Marshall,et al.  PONTIS BRIDGE MANAGEMENT SYSTEM: STATE OF THE PRACTICE IN IMPLEMENTATION AND DEVELOPMENT , 2003 .

[53]  A. Razaqpur,et al.  Modelling steel corrosion in concrete structures , 2005 .

[54]  Dan M. Frangopol,et al.  Optimizing Bridge Network Maintenance Management under Uncertainty with Conflicting Criteria: Life-Cycle Maintenance, Failure, and User Costs , 2006 .

[55]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[56]  Jianchang Mao,et al.  A case study on bagging, boosting and basic ensembles of neural networks for OCR , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[57]  Ayaho Miyamoto,et al.  Bridge Management System and Maintenance Optimization for Existing Bridges , 2000 .

[58]  David West,et al.  Neural network ensemble strategies for financial decision applications , 2005, Comput. Oper. Res..

[59]  F Fanous,et al.  IMPACT OF DECK CRACKING ON DURABILITY , 2000 .

[60]  Geoffrey E. Hinton,et al.  A general framework for parallel distributed processing , 1986 .

[61]  Hitoshi Furuta,et al.  Life-Cycle Cost Analysis for Infrastructure Systems: Life-Cycle Cost vs. Safety Level vs. Service Life , 2003 .

[62]  Samer Madanat,et al.  Computation of Infrastructure Transition Probabilities using Stochastic Duration Models , 2002 .

[63]  Anil K. Agrawal,et al.  Bridge Element Deterioration Rates , 2009 .

[64]  Osama Moselhi,et al.  Classification of Defects in Sewer Pipes Using Neural Networks , 2000 .

[65]  Syed Waqar Haider,et al.  Estimating Optimum Timing for Preventive Maintenance Treatment to Mitigate Pavement Roughness , 2011 .

[66]  Matthew G. Karlaftis,et al.  Probabilistic Infrastructure Deterioration Models with Panel Data , 1997 .