Service life of the reinforced concrete bridge deck in corrosive environments: A soft computing system

In the recent years, the soft computing techniques are increasingly applied in many fields of civil engineering due to their capabilities in computation and knowledge processing. In this paper, a soft computing system is developed to estimate the service life of reinforced concrete bridge deck as one of the most important issues in the civil engineering. The proposed system utilizes four fuzzy interfaces to quantify the exposure condition, required cover thickness, corrosion current density, and pitting corrosion ratio. The computational core of the proposed system employs @a-level optimization in conjunction with the mentioned fuzzy systems to estimate the entire service life. The service life of structurally designed reinforced concrete bridge deck sample is assessed by both proposed system and traditional probabilistic method. The results showed that the proposed system could effectively predict the service life; however, it estimated longer service life in comparison with the probabilistic method.

[1]  K. Ramanjaneyulu,et al.  An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks , 2007, Adv. Eng. Softw..

[2]  Ayaho Miyamoto,et al.  Condition state evaluation of existing reinforced concrete bridges using neuro-fuzzy hybrid system , 2003 .

[3]  K. Sumangala,et al.  Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach , 2006 .

[4]  W. Graf,et al.  Fuzzy structural analysis using α-level optimization , 2000 .

[5]  C. Andrade,et al.  An Initial Effort to Use the Corrosion Rate Measurements for Estimating Rebar Durability , 1990 .

[6]  Palle Thoft-Christensen,et al.  Assessment of the Reliability Profiles for Concrete Bridges , 1998 .

[7]  Peter A. Claisse,et al.  Determination of the concrete chloride diffusion coefficient based on an electrochemical test and an optimization model , 2009 .

[8]  Uwe Reuter,et al.  Application of fuzzy randomness to time-dependent reliability , 2005 .

[9]  Bryan T. Adey,et al.  Condition Evolution in Bridge Management Systems and Corrosion-Induced Deterioration , 2004 .

[10]  Michael P. Enright,et al.  SURVEY AND EVALUATION OF DAMAGED CONCRETE BRIDGES , 2000 .

[11]  Manolis Papadrakakis,et al.  Structural optimization using evolution strategies and neural networks , 1998 .

[12]  Shamsad Ahmad.  Reinforcement corrosion in concrete structures, its monitoring and service life prediction - A review , 2003 .

[13]  Ha-Won Song,et al.  Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling , 2010 .

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

[15]  Richard E. Weyers,et al.  MODELING THE TIME-TO-CORROSION CRACKING IN CHLORIDE CONTAMINATED REINFORCED CONCRETE STRUCTURES , 1998 .

[16]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[17]  A A Ramezanianpour,et al.  APPLICATION OF NETWORK-BASED NEURO-FUZZY SYSTEM FOR PREDICTION OF THE STRENGTHOF HIGH STRENGTH CONCRETE , 2004 .

[18]  K. R. Ramakrishnan,et al.  Neural network analysis for corrosion of steel in concrete , 2005 .

[19]  K. Balaji Rao,et al.  Application of Fuzzy Sets for Remaining Life Assessment of Corrosion Affected Reinforced Concrete Bridge Girders , 2007 .

[20]  Ahmet Serdar Yilmaz,et al.  Prediction of early heat of hydration of plain and blended cements using neuro-fuzzy modelling techniques , 2009, Expert Syst. Appl..

[21]  I. Topcu,et al.  Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic , 2008 .

[22]  B. V. Venkatarama Reddy,et al.  Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN , 2009 .

[23]  Comité euro-international du béton.  Durable Concrete Structures: Design Guide , 1992 .

[24]  David Darwin,et al.  Effect of Cracking on Chloride Content in Concrete Bridge Decks , 2006 .

[25]  Fatih Onur Hocaoglu,et al.  Modeling corrosion currents of reinforced concrete using ANN , 2009 .

[26]  Okan Karahan,et al.  Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network , 2009, Adv. Eng. Softw..

[27]  Bernd Möller,et al.  Safety assessment of structures in view of fuzzy randomness , 2003 .

[28]  Chun-Hsien Chen,et al.  Concrete bridge deterioration diagnosis using fuzzy inference system , 2001 .

[29]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy polynomial neural networks for approximation of the compressive strength of concrete , 2008, Appl. Soft Comput..

[30]  Ayaho Miyamoto,et al.  Fuzzy concrete bridge deck condition rating method for practical bridge management system , 2009, Expert Syst. Appl..

[31]  Seung-Jun Kwon,et al.  Evaluation of Chloride Penetration in High Performance Concrete Using Neural Network Algorithm and Micro Pore Structure , 2009 .

[32]  Ana Colubi,et al.  On the formalization of fuzzy random variables , 2001, Inf. Sci..

[33]  Christine M. Anderson-Cook,et al.  Impact of specification changes on chloride-induced corrosion service life of bridge decks , 2002 .

[34]  Bang Yeon Lee,et al.  Optimum Concrete Mixture Proportion Based on a Database Considering Regional Characteristics , 2009 .

[35]  Dan M. Frangopol,et al.  Lifetime Performance Analysis of Existing Reinforced Concrete Bridges. I: Theory , 2005 .

[36]  M. Beer,et al.  Fuzzy probabilistic structural analysis considering fuzzy random functions , 2003 .

[37]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[38]  C. Q. Li,et al.  Life-Cycle Modeling of Corrosion-Affected Concrete Structures: Propagation , 2003 .

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

[40]  Wolfgang Graf,et al.  Lifetime prediction using accelerated test data and neural networks , 2009 .

[41]  James R. Clifton,et al.  Predicting the Service Life of Concrete , 1993 .

[42]  İlker Bekir Topçu,et al.  Prediction of rubberized concrete properties using artificial neural network and fuzzy logic , 2008 .

[43]  Adil Baykasoglu,et al.  Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches , 2009, Expert Syst. Appl..

[44]  Dan A. Ralescu,et al.  Overview on the development of fuzzy random variables , 2006, Fuzzy Sets Syst..

[45]  Ali Akbar Ramezanianpour,et al.  CHLORIDE-INDUCED CORROSION OF RC STRUCTURES , 2007 .

[46]  N. Lakshmanan,et al.  Fuzzy Logic Based Condition Rating of Existing Reinforced Concrete Bridges , 2006 .

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

[48]  Bernd Möller,et al.  Discussion on “Structural reliability analysis through fuzzy number approach, with application to stability” , 2004 .

[49]  Matthew A. Miltenberger,et al.  PREDICTING THE SERVICE LIFE OF CONCRETE MARINE STRUCTURES: AN ENVIRONMENTAL METHODOLOGY , 1998 .

[50]  Daksh Baweja,et al.  Chloride Induced Steel Corrosion in Concrete: Part 2—Gravimetric and Electrochemical Comparisons , 1999 .

[51]  G G Clemena,et al.  Corrosion Protection: Concrete Bridges , 1998 .

[52]  J. Sobhani,et al.  Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models , 2010 .

[53]  Dan M. Frangopol,et al.  Performance evaluation of concrete slabs of existing bridges using neural networks , 2003 .

[54]  Yubin Liu,et al.  Fuzzy random reliability of structures based on fuzzy random variables , 1997, Fuzzy Sets Syst..

[55]  Marco Savoia,et al.  Structural reliability analysis through fuzzy number approach, with application to stability , 2002 .

[56]  María Angeles Gil,et al.  Fuzzy random variables , 2001, Inf. Sci..

[57]  Hong-Guang Ni,et al.  Prediction of compressive strength of concrete by neural networks , 2000 .

[58]  Y. Cheng,et al.  Artificial neural network technology for the data processing of on-line corrosion fatigue crack growth monitoring , 1999 .

[59]  C. Tuan,et al.  Conductive concrete overlay for bridge deck deicing , 1999 .

[60]  M. B. Anoop,et al.  Application of fuzzy sets for estimating service life of reinforced concrete structural members in corrosive environments , 2002 .