A Refined Methodology for Durability‐Based Service Life Estimation of Reinforced Concrete Structural Elements Considering Fuzzy and Random Uncertainties

A reliable method for service life estimation of the structural element is a prerequisite for service life design. A new methodology for durability-based service life estimation of reinforced concrete flexural elements with respect to chloride-induced corrosion of reinforcement is proposed. The methodology takes into consideration the fuzzy and random uncertainties associated with the variables involved in service life estimation by using a hybrid method combining the vertex method of fuzzy set theory with Monte Carlo simulation technique. It is also shown how to determine the bounds for characteristic value of failure probability from the resulting fuzzy set for failure probability with minimal computational effort. Using the methodology, the bounds for the characteristic value of failure probability for a reinforced concrete T-beam bridge girder has been determined. The service life of the structural element is determined by comparing the upper bound of characteristic value of failure probability with the target failure probability. The methodology will be useful for durability-based service life design and also for making decisions regarding in-service inspections.

[1]  Zhen Zhou Lu,et al.  Fuzzy-Random FOSM and its Application in Low Cycle Fatigue Life Reliability Analysis of an Aeronautical Engine Turbine Disk , 2006 .

[2]  Dimitris C. Theodoridis,et al.  Indirect Adaptive Control of Unknown Multi Variable nonlinear Systems with Parametric and Dynamic Uncertainties Using a New Neuro-Fuzzy System Description , 2010, Int. J. Neural Syst..

[3]  Pavla Rovnaníková,et al.  Modeling of Chloride Concentration Effect on Reinforcement Corrosion , 2009, Comput. Aided Civ. Infrastructure Eng..

[4]  Hojjat Adeli,et al.  Fuzzy clustering approach for accurate embedding dimension identification in chaotic time series , 2003, Integr. Comput. Aided Eng..

[5]  Witold Pedrycz,et al.  Fuzzy Monte Carlo Simulation and Risk Assessment in Construction , 2010, Comput. Aided Civ. Infrastructure Eng..

[6]  Wided Ben Tagherouit,et al.  A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks , 2011, Comput. Aided Civ. Infrastructure Eng..

[7]  Xiao-Hua Jin,et al.  Modeling Risk Allocation in Privately Financed Infrastructure Projects Using Fuzzy Logic , 2009, Comput. Aided Civ. Infrastructure Eng..

[8]  Hojjat Adeli,et al.  Life‐cycle cost optimization of steel structures , 2002 .

[9]  Thierry Vidal,et al.  ANALYZING CRACK WIDTH TO PREDICT CORROSION IN REINFORCED CONCRETE , 2004 .

[10]  T. Liu,et al.  Modeling the Dynamic Corrosion Process in Chloride Contaminated Concrete Structures , 1998 .

[11]  Hojjat Adeli,et al.  NEURO-FUZZY LOGIC MODEL FOR FREEWAY WORK ZONE CAPACITY ESTIMATION , 2003 .

[12]  Didier Dubois,et al.  Hybrid approach for addressing uncertainty in risk assessments , 2003 .

[13]  Hojjat Adeli,et al.  Comparison of fuzzy-wavelet radial basis function neural network freeway incident detection model with California algorithm , 2002 .

[14]  Kamal C. Sarma,et al.  FUZZY GENETIC ALGORITHM FOR OPTIMIZATION OF STEEL STRUCTURES , 2000 .

[15]  Uwe Reuter,et al.  Artificial Neural Networks for Forecasting of Fuzzy Time Series , 2010, Comput. Aided Civ. Infrastructure Eng..

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

[17]  G. Klir,et al.  PROBABILITY-POSSIBILITY TRANSFORMATIONS: A COMPARISON , 1992 .

[18]  N. Lakshmanan,et al.  Safety assessment of austenitic steel nuclear power plant pipelines against stress corrosion cracking in the presence of hybrid uncertainties , 2008 .

[19]  Giuseppe Quaranta,et al.  Fuzzy Time-Dependent Reliability Analysis of RC Beams Subject to Pitting Corrosion , 2008 .

[20]  Michael Raupach,et al.  Models for the propagation phase of reinforcement corrosion – an overview , 2006 .

[21]  H. Adeli,et al.  Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification , 2006 .

[22]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

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

[24]  W. Dong,et al.  Vertex method for computing functions of fuzzy variables , 1987 .

[25]  Rafal Scherer,et al.  Designing Boosting Ensemble of Relational Fuzzy Systems , 2010, Int. J. Neural Syst..

[26]  Mark G. Stewart,et al.  Structural reliability of concrete bridges including improved chloride-induced corrosion models , 2000 .

[27]  Hyun-Ho Choi,et al.  Safety Assessment Using Imprecise Reliability for Corrosion‐Damaged Structures , 2009, Comput. Aided Civ. Infrastructure Eng..

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

[29]  K. Balaji Rao,et al.  A METHODOLOGY FOR DURABILITY-BASED SERVICE LIFE DESIGN OF REINFORCED CONCRETE FLEXURAL MEMBERS , 2003 .

[30]  Xiaoou Li,et al.  Automated Nonlinear System Modeling with Multiple Fuzzy Neural Networks and Kernel Smoothing , 2010, Int. J. Neural Syst..

[31]  José Ramón Villar,et al.  A fuzzy logic based efficient energy saving approach for domestic heating systems , 2009, Integr. Comput. Aided Eng..

[32]  Qiang Li,et al.  Pavement Smoothness Prediction Based on Fuzzy and Gray Theories , 2009, Comput. Aided Civ. Infrastructure Eng..

[33]  Xiang Li,et al.  An Online Self-Organizing Scheme for Parsimonious and Accurate Fuzzy Neural Networks , 2010, Int. J. Neural Syst..

[34]  Prakash Desayi,et al.  Probabilistic analysis of the cracking of RC beams , 1987 .

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

[36]  Subrata Chakraborty,et al.  Probabilistic safety analysis of structures under hybrid uncertainty , 2007 .

[37]  Dimitri V. Val,et al.  Life-cycle cost analysis of reinforced concrete structures in marine environments , 2003 .

[38]  Aminah Robinson Fayek,et al.  A multi-criteria optimization framework for industrial shop scheduling using fuzzy set theory , 2010, Integr. Comput. Aided Eng..

[39]  Zongmin Ma,et al.  Extracting knowledge from fuzzy relational databases with description logic , 2011, Integr. Comput. Aided Eng..

[40]  Paola Bandini,et al.  Prediction of Pavement Performance through Neuro‐Fuzzy Reasoning , 2010, Comput. Aided Civ. Infrastructure Eng..

[41]  Didier Dubois,et al.  Merging Fuzzy Information , 1999 .

[42]  Chun-Qing Li,et al.  A Risk‐Cost Optimized Maintenance Strategy for Corrosion‐Affected Concrete Structures , 2007, Comput. Aided Civ. Infrastructure Eng..

[43]  Plamen P. Angelov,et al.  Human Activity Recognition Based on Evolving Fuzzy Systems , 2010, Int. J. Neural Syst..

[44]  Hojjat Adeli,et al.  Enhancing Neural Network Traffic Incident‐Detection Algorithms Using Wavelets , 2001 .

[45]  Fuchun Sun,et al.  A Dual-Model Jumping Fuzzy System Approach to Networked Control Systems Design , 2010, Int. J. Neural Syst..

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

[47]  Kamal C. Sarma.  Fuzzy discrete multicriteria cost optimization of steel structures using genetic algorithm , 2000 .

[48]  Hojjat Adeli,et al.  Dynamic fuzzy wavelet neuroemulator for non‐linear control of irregular building structures , 2008 .

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

[50]  Wellington Pinheiro dos Santos,et al.  An adaptive fuzzy-based system to simulate, quantify and compensate color blindness , 2017, Integr. Comput. Aided Eng..

[51]  M. B. Anoop,et al.  Determination of bounds on failure probability in the presence of hybrid uncertainties , 2008 .

[52]  Reza Langari,et al.  Model‐Based Multi‐input, Multi‐output Supervisory Semi‐active Nonlinear Fuzzy Controller , 2010, Comput. Aided Civ. Infrastructure Eng..

[53]  Michael Raupach,et al.  Damage process due to corrosion of Reinforcement bars – Current and future activities – , 2006 .

[54]  Cruz Alonso,et al.  Comparison of rates of general corrosion and maximum pitting penetration on concrete embedded steel reinforcement , 1995 .

[55]  X Li,et al.  Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection , 2010, Int. J. Neural Syst..

[56]  M. B. Anoop,et al.  Conversion of probabilistic information into fuzzy sets for engineering decision analysis , 2006 .