Exploring the Deterioration Factors of RC Bridge Decks: A Rough Set Approach

: Information about the factors that lead to the deterioration of bridges is essential for bridge maintenance. Pinpointing what these factors are will certainly enhance the effectiveness of bridge management. However, a review of the literature reveals that such factors are mainly determined based on experts’ opinions rather than a systematic approach. In this study the factors leading to deterioration of RC bridge decks are grouped into six common types. Twenty-nine candidate factors are selected from an extensive review of past work as well as from the inventory of the Taiwan Bridge Management System. A data mining technique, the Rough Set Theory (RST), is employed to find the factors that have the most significant impact on deterioration. It is found that weather-related factors are rather significant for almost all types of deterioration. Finally, the factors mined by RST are compared to those obtained by Mann-Whitney U (MWU). The results of comparison appear fairly consistent, which validates the proposed approach.

[1]  Hojjat Adeli,et al.  A Concurrent Adaptive Conjugate Gradient Learning Algorithm On Mimd Shared-Memory Machines , 1993, Int. J. High Perform. Comput. Appl..

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

[3]  Chun-Yuan Wang,et al.  A Correlation Study of the Existing Bridges for Failure Analysis-Case Study of Taichung County , 2009 .

[4]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[5]  Ron Kohavi,et al.  Wrappers for performance enhancement and oblivious decision graphs , 1995 .

[6]  Ming Gu,et al.  Wind Tunnel and CFD Study on Identification of Flutter Derivatives of a Long‐Span Self‐Anchored Suspension Bridge , 2007, Comput. Aided Civ. Infrastructure Eng..

[7]  Manish Sarkar,et al.  Fuzzy-rough nearest neighbor algorithms in classification , 2007, Fuzzy Sets Syst..

[8]  Luca Caracoglia,et al.  Nonlinear Computer Model for the Simulation of Lock‐in Vibration on Long‐Span Bridges , 2009, Comput. Aided Civ. Infrastructure Eng..

[9]  Qiang Shen,et al.  Fuzzy-Rough Sets Assisted Attribute Selection , 2007, IEEE Transactions on Fuzzy Systems.

[10]  Z J Pasek.  Exploration of Rough Sets Theory Use for Manufacturing Process Monitoring , 2006 .

[11]  Hitoshi Furuta,et al.  Rough‐Set‐Based Knowledge Acquisition from Cases for Integrity Assessment of Bridge Structures , 1998 .

[12]  William T. Scherer,et al.  Markovian Models for Bridge Maintenance Management , 1994 .

[13]  C. Andrade,et al.  Corrosion of Reinforcing Bars in Carbonated Concrete , 1980 .

[14]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[15]  Zhiye Zhao,et al.  A fuzzy system for concrete bridge damage diagnosis , 2002 .

[16]  Richi Nayak,et al.  A knowledge retrieval model using ontology mining and user profiling , 2008, Integr. Comput. Aided Eng..

[17]  Toshinori Munakata,et al.  Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3 , 2002, Eur. J. Oper. Res..

[18]  Carey Rappaport,et al.  Model Based Evaluation of Bridge Decks Using Ground Penetrating Radar , 2007, Comput. Aided Civ. Infrastructure Eng..

[19]  Paulo J. S. Cruz,et al.  Performance of Vibration‐Based Damage Detection Methods in Bridges , 2009, Comput. Aided Civ. Infrastructure Eng..

[20]  Joel P. Conte,et al.  Modal Identification Study of Vincent Thomas Bridge Using Simulated Wind‐Induced Ambient Vibration Data , 2008, Comput. Aided Civ. Infrastructure Eng..

[21]  Akh Kwan,et al.  Effects of traffic vibration on curing concrete stitch: Part II-cracking, debonding and strength reduction , 2007 .

[22]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[23]  John T. DeWolf,et al.  Development of Computer-Based System for the Temperature Monitoring of a Post-Tensioned Segmental Concrete Box-Girder Bridge , 2007, Comput. Aided Civ. Infrastructure Eng..

[24]  H. F. Zhou,et al.  Modal Flexibility Analysis of Cable‐Stayed Ting Kau Bridge for Damage Identification , 2008, Comput. Aided Civ. Infrastructure Eng..

[25]  Qunxiong Zhu,et al.  Rough Set-Based Fuzzy Rule Acquisition and Its Application for Fault Diagnosis in Petrochemical Process , 2009 .

[26]  Yeong Min Kim,et al.  Rough set algorithm for crack category determination of reinforced concrete structures , 2009, Adv. Eng. Softw..

[27]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[28]  Jerzy W. Grzymala-Busse,et al.  THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION , 1995, Comput. Intell..

[29]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[30]  Hojjat Adeli,et al.  A parallel genetic/neural network learning algorithm for MIMD shared memory machines , 1994, IEEE Trans. Neural Networks.

[31]  Runian Geng,et al.  Efficient mining of interesting weighted patterns from directed graph traversals , 2009, Integr. Comput. Aided Eng..

[32]  Jia-Ruey Chang,et al.  Using Rough Set Theory to Induce Pavement Maintenance and Rehabilitation Strategy , 2007, RSKT.

[33]  Hojjat Adeli,et al.  Parallel backpropagation learning algorithms on CRAY Y-MP8/864 supercomputer , 1993, Neurocomputing.

[34]  J. Czerniak,et al.  Application of rough sets in the presumptive diagnosis of urinary system diseases , 2003 .

[35]  Anna Maria Radzikowska,et al.  A comparative study of fuzzy rough sets , 2002, Fuzzy Sets Syst..

[36]  Maria Q. Feng,et al.  Long-Term Monitoring and Identification of Bridge Structural Parameters , 2009 .

[37]  Ning Zhong,et al.  Using Rough Sets with Heuristics for Feature Selection , 1999, RSFDGrC.

[38]  Shiliang Zhang,et al.  Correlation-Based Feature Selection and Regression , 2010, PCM.

[39]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[40]  Richard J. Vaccaro,et al.  A State‐Space Approach for Deriving Bridge Displacement from Acceleration , 2008, Comput. Aided Civ. Infrastructure Eng..

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

[42]  V K Raina.  Concrete Bridges: Inspection, Repair, Strengthening, Testing and Load Capacity Evaluation , 1996 .

[43]  Anthony K. H. Tung,et al.  Efficient Mining of , 2003 .

[44]  Hojjat Adeli,et al.  An adaptive conjugate gradient learning algorithm for efficient training of neural networks , 1994 .

[45]  Masanobu Shinozuka,et al.  Nonlinear Static Procedure for Seismic Vulnerability Assessment of Bridges , 2007, Comput. Aided Civ. Infrastructure Eng..

[46]  Nii O. Attoh-Okine.  Rough set application to data mining principles in pavement management database , 1997 .