Genetic programming approach for predicting service life of tunnel structures subject to chloride-induced corrosion

Graphical abstract

[1]  Maryam Bitaraf,et al.  Analysis of chloride diffusion in concrete structures for prediction of initiation time of corrosion using a new meshless approach , 2008 .

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  O. Hodhod,et al.  Modeling the corrosion initiation time of slag concrete using the artificial neural network , 2014 .

[4]  Vagelis G. Papadakis.  Service life prediction of a reinforced concrete bridge exposed to chloride induced deterioration , 2013 .

[5]  Jun Peng,et al.  Neural Network Analysis of Chloride Diffusion in Concrete , 2002 .

[6]  Jun Sun.  Durability problems of lining structures for Xiamen Xiang ’ an subsea tunnel in China , 2011 .

[7]  Z. Bažant,et al.  Modeling Chloride Penetration in Saturated Concrete , 1999 .

[8]  Michał A. Glinicki,et al.  Prediction of the Chloride Resistance of Concrete Modified with High Calcium Fly Ash Using Machine Learning , 2015, Materials.

[9]  Sebastián Ventura.  Genetic Programming - New Approaches and Successful Applications , 2012 .

[10]  Yacoub M. Najjar,et al.  Characterizing the Permeability of Kansas Concrete Mixes Used in PCC Pavements , 2014 .

[11]  Ueli Angst,et al.  Critical Chloride Content in Reinforced Concrete: A Review , 2009 .

[12]  Carmen Andrade,et al.  Testing and modelling chloride penetration into concrete , 2013 .

[13]  Alfred Strauss,et al.  Prediction of the remaining service life of existing concrete bridges in infrastructural networks based on carbonation and chloride ingress , 2018 .

[14]  Edson Denner Leonel,et al.  Probabilistic models applied to safety assessment of reinforced concrete structures subjected to chloride ingress , 2013 .

[15]  Esko Sistonen,et al.  Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions , 2017 .

[16]  Murat Ozturk,et al.  Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI , 2013 .

[17]  Ali Akbar Ramezanianpour,et al.  The assessment of carbonation effect on chloride diffusion in concrete based on artificial neural network model , 2012 .

[18]  Meysam Najimi,et al.  Predicting Rapid Chloride Permeability of Self-Consolidating Concrete: A Comparative Study on Statistical and Neural Network Models , 2013 .

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

[20]  Gro Markeset,et al.  Need for Further Development in Service Life Modelling of Concrete Structures in Chloride Environment , 2017 .

[21]  Ali Akbar Ramezanianpour,et al.  Hybrid support vector regression – Particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin , 2012 .

[22]  S. Inthata,et al.  Prediction of chloride permeability of concretes containing ground pozzolans by artificial neural networks , 2013 .

[23]  Michael D. A. Thomas,et al.  Modelling chloride diffusion in concrete: Effect of fly ash and slag , 1999 .

[24]  C. Gehlen,et al.  Modelling of ageing and corrosion processes in reinforced concrete structures , 2010 .

[25]  Quanwang Li,et al.  Service life prediction of RC structures in marine environment using long term chloride ingress data: Comparison between exposure trials and real structure surveys , 2016 .

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

[27]  Wei Gao,et al.  Displacement back analysis for underground engineering based on immunized continuous ant colony optimization , 2016 .

[28]  Ki Yong Ann,et al.  Probabilistic assessment to predict the time to corrosion of steel in reinforced concrete tunnel box exposed to sea water , 2009 .

[29]  A. Costa,et al.  Chloride penetration into concrete in marine environment-Part II: Prediction of long term chloride penetration , 1999 .

[30]  P. Nimityongskul,et al.  Analysis of durability of high performance concrete using artificial neural networks , 2009 .

[31]  Seung-Jun Kwon,et al.  Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm , 2014 .

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