Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation

Abstract Chloride ingression is the main reason for causing durability degradation of reinforced concrete (RC) structures. In this study, the distinguishing features of artificial neural network (ANN) technique are utilized to develop a rational and effective predictive model for chloride diffusion coefficient of concrete. An extensive and reliable database comprising of 653 distinctive diffusion coefficient results, from literature, was utilized for establishing the network model. The developed ANN models used 13 most influential parameters, varying from concrete constituents, mechanical property and experimental process, as input to incorporate complex underlying physical phenomena for prediction of diffusion coefficient. The significance of normalization of the variables is highlighted through a comparative study. The performance of the developed model is assessed by conducting several in-depth statistical error analysis as per the recommendations in literature. The results of the study reveals that the models are robust and possess a strong prediction potential. The findings revealed that ANN can be an effective tool to identify the discrepancies in the experimental findings, and would be particularly useful for evaluating the chloride resistance of RC structures serving in complex or harsh environment.

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

[2]  Ki Yong Ann,et al.  Prediction of time dependent chloride transport in concrete structures exposed to a marine environment , 2010 .

[3]  Amir Hossein Gandomi,et al.  Assessment of artificial neural network and genetic programming as predictive tools , 2015, Adv. Eng. Softw..

[4]  Marcelo Henrique Farias de Medeiros,et al.  Surface treatment of reinforced concrete in marine environment: Influence on chloride diffusion coefficient and capillary water absorption , 2009 .

[5]  Hao Wang,et al.  Water absorption and chloride diffusivity of concrete under the coupling effect of uniaxial compressive load and freeze–thaw cycles , 2019, Construction and Building Materials.

[6]  Qing-feng Liu,et al.  Prediction of Chloride Distribution for Offshore Concrete Based on Statistical Analysis , 2020, Materials.

[7]  Jianxin Peng,et al.  Influence of cracks on chloride diffusivity in concrete: A five-phase mesoscale model approach , 2019, Construction and Building Materials.

[8]  Sze Dai Pang,et al.  High performance cement composites with colloidal nano-silica , 2019, Construction and Building Materials.

[9]  Nick R. Buenfeld,et al.  Computational investigation of capillary absorption in concrete using a three-dimensional mesoscale approach , 2014 .

[10]  Hosein Naderpour,et al.  Prediction of FRP-confined compressive strength of concrete using artificial neural networks , 2010 .

[11]  O. A. Hodhod,et al.  Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete , 2013 .

[12]  Pal Mangat,et al.  Effect of desulphurised waste on long-term porosity and pore structure of blended cement pastes , 2016 .

[13]  S. Kenai,et al.  Effects of granulated blast furnace slag and superplasticizer type on the fresh properties and compressive strength of self-compacting concrete , 2012 .

[14]  San-Shyan Lin,et al.  A multi-phase model for predicting the effective diffusion coefficient of chlorides in concrete , 2012 .

[15]  Nhat-Duc Hoang,et al.  Prediction of chloride diffusion in cement mortar using Multi-Gene Genetic Programming and Multivariate Adaptive Regression Splines , 2017 .

[16]  Mohammad Iqbal Khan.  Mix proportions for HPC incorporating multi-cementitious composites using artificial neural networks , 2012 .

[17]  Shazim Ali Memon,et al.  Durability of sustainable concrete subjected to elevated temperature – A review , 2019, Construction and Building Materials.

[18]  Mohammad Iqbal Khan,et al.  Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks , 2012 .

[19]  Yuya Sakai,et al.  Relationship between pore structure and chloride diffusion in cementitious materials , 2019 .

[20]  Pichai Nimityongskul,et al.  An integrated approach for optimum design of HPC mix proportion using genetic algorithm and artificial neural networks , 2009 .

[21]  B. Ramesh Babu,et al.  Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar , 2008 .

[22]  K. Ann,et al.  Factors influencing chloride transport in concrete structures exposed to marine environments , 2008 .

[23]  Julio Appleton,et al.  Chloride penetration into concrete in marine environment—Part I: Main parameters affecting chloride penetration , 1999 .

[24]  Muhammad Iqbal,et al.  Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. , 2020, Journal of hazardous materials.

[25]  Ming Sun,et al.  Modified Lucas-Washburn function of capillary transport in the calcium silicate hydrate gel pore: A coarse-grained molecular dynamics study , 2020 .

[26]  Dongshuai Hou,et al.  Numerical study of carbonation and its effect on chloride binding in concrete , 2019, Cement and Concrete Composites.

[27]  Mônica Batista Leite,et al.  Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks , 2013 .

[28]  Qing-feng Liu,et al.  Experimental study on the utilization of waste foundry sand as embankment and structural fill , 2019, IOP Conference Series: Materials Science and Engineering.

[29]  Diederik Jacques,et al.  A three-dimensional lattice Boltzmann method based reactive transport model to simulate changes in cement paste microstructure due to calcium leaching , 2018 .

[30]  Y. Murad.  Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading , 2020 .

[31]  P. Richard,et al.  Superplasticizer effects on setting and structuration mechanisms of ultrahigh-performance concrete , 2001 .

[32]  Ruoyu Jin,et al.  Measurement of reinforcement corrosion in concrete adopting ultrasonic tests and artificial neural network , 2018, Construction and Building Materials.

[33]  Min-Hong Zhang,et al.  A model to estimate the durability performance of both normal and light-weight concrete , 2015 .

[34]  Nick R. Buenfeld,et al.  Modelling the diffusivity of mortar and concrete using a three-dimensional mesostructure with several aggregate shapes , 2013 .

[35]  Ravindra K. Dhir,et al.  Concrete surface treatment: Effect of exposure temperature on chloride diffusion resistance , 1995 .

[36]  Mohammad Shekarchi,et al.  Long-term chloride diffusion in silica fume concrete in harsh marine climates , 2009 .

[37]  Rana Imam,et al.  Regression versus artificial neural networks: Predicting pile setup from empirical data , 2014 .

[38]  A. Öztas,et al.  Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks , 2007 .

[39]  Peng Zhang,et al.  Comparison of Mercury Intrusion Porosimetry and multi-scale X-ray CT on characterizing the microstructure of heat-treated cement mortar , 2020 .

[40]  Mehrdad Arashpour,et al.  Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer , 2020 .

[41]  Sze Dai Pang,et al.  Improvement in concrete resistance against water and chloride ingress by adding graphene nanoplatelet , 2016 .

[42]  I-Cheng Yeh,et al.  Modeling slump flow of concrete using second-order regressions and artificial neural networks , 2007 .

[43]  Mark Alexander,et al.  A chloride conduction test for concrete , 1995 .

[44]  Sudhir Varma,et al.  Optimizing asphalt mix design process using artificial neural network and genetic algorithm , 2018 .

[45]  Yasmin Zuhair Murad,et al.  Interior Reinforced Concrete Beam-to-Column Joints Subjected to Cyclic Loading: Shear Strength Prediction using Gene Expression Programming , 2020 .

[46]  Roberto Todeschini,et al.  The data analysis handbook , 1994, Data handling in science and technology.

[47]  Jorge de Brito,et al.  Statistical modelling of the influential factors on chloride penetration in concrete , 2017 .

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

[49]  Tarek Uddin Mohammed,et al.  Chloride diffusion, microstructure, and mineralogy of concrete after 15 years of exposure in tidal environment , 2002 .

[50]  Dave Easterbrook,et al.  A three-phase, multi-component ionic transport model for simulation of chloride penetration in concrete , 2015 .

[51]  Hailong Wang,et al.  Time-Dependent and Stress-Dependent Chloride Diffusivity of Concrete Subjected to Sustained Compressive Loading , 2016 .

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

[53]  S. Siddique,et al.  Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN , 2019 .

[54]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[55]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[56]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[57]  Qiang Wang,et al.  Investigation on the poor fluidity of electrically conductive cement-graphite paste: Experiment and simulation , 2019, Materials & Design.

[58]  Dawang Li,et al.  An analytical solution for chloride diffusion in concrete with considering binding effect , 2019, Ocean Engineering.

[59]  Mustafa Saridemir,et al.  Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks , 2009, Adv. Eng. Softw..

[60]  Yi Wang,et al.  Bond Strength Assessment of Concrete-Corroded Rebar Interface Using Artificial Neutral Network , 2020, Applied Sciences.

[61]  Dookie Kim,et al.  An improved application technique of the adaptive probabilistic neural network for predicting concrete strength , 2009 .

[62]  M. Getahun,et al.  Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes , 2018, Construction and Building Materials.

[63]  Alireza Rahai,et al.  Prediction of chloride content in concrete using ANN and CART , 2016 .

[64]  Gurpreet Singh,et al.  Comparative investigation on the influence of spent foundry sand as partial replacement of fine aggregates on the properties of two grades of concrete , 2015 .

[65]  R. Corotis.  Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp. , 1988 .

[66]  Wei Sun,et al.  Influence on GGBS to Time Dependent Chloride Diffusion Coefficient of HPC , 2011 .

[67]  M Hunger,et al.  Long-term chloride migration coefficient in slag cement-based concrete and resistivity as an alternative test method , 2016 .

[68]  İlker Bekir Topçu,et al.  Prediction of properties of waste AAC aggregate concrete using artificial neural network , 2007 .

[69]  Mohamed Lachemi,et al.  Corrosion resistance and chloride diffusivity of volcanic ash blended cement mortar , 2004 .

[70]  Emadaldin Mohammadi Golafshani,et al.  Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves , 2018, Journal of Cleaner Production.

[71]  Simon Smith,et al.  Estimating key characteristics of the concrete delivery and placement process using linear regression analysis , 2003 .

[72]  Jin Xia,et al.  Ionic transport features in concrete composites containing various shaped aggregates: a numerical study , 2018 .

[73]  F. Wittmann,et al.  Influence of freeze-thaw cycles on capillary absorption and chloride penetration into concrete , 2017 .

[74]  Jamshid M Armaghani,et al.  ASPECTS OF CONCRETE STRENGTH AND DURABILITY , 1992 .

[75]  Jun Zhang,et al.  Hydrogen embrittlement risk control of prestressed tendons during electrochemical rehabilitation based on bidirectional electro-migration , 2019, Construction and Building Materials.

[76]  Walid A. Al-Kutti,et al.  Correlation between compressive strength and certain durability indices of plain and blended cement concretes , 2009 .

[77]  Amir Hossein Alavi,et al.  Novel Approach to Strength Modeling of Concrete under Triaxial Compression , 2012 .

[78]  Feiliang Wang,et al.  Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming , 2020, Structures.

[79]  E. Kadri,et al.  Effect of using metakaolin as supplementary cementitious material and recycled CRT funnel glass as fine aggregate on the durability of green self-compacting concrete , 2020 .

[80]  H. Okamura,et al.  Effect of Superplasticizer on Self-Compactability of Fresh Concrete , 1997 .

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

[82]  Peng Zhang,et al.  Experimental and numerical study on chloride transport in cement mortar during drying process , 2020, Construction and Building Materials.

[83]  Qing-feng Liu,et al.  Binding capacity and diffusivity of concrete subjected to freeze-thaw and chloride attack: A numerical study , 2019, Ocean Engineering.

[84]  İlker Bekir Topçu,et al.  Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic , 2008 .

[85]  O. Kayali,et al.  Corrosion performance of medium-strength and silica fume high-strength reinforced concrete in a chloride solution , 2005 .

[86]  Peng Hao,et al.  Combine ingress of chloride and carbonation in marine-exposed concrete under unsaturated environment: A numerical study , 2019, Ocean Engineering.

[87]  Joseph R. Kasprzyk,et al.  Computational design optimization of concrete mixtures: A review , 2018, Cement and Concrete Research.

[88]  N. Xie,et al.  Durability of steel reinforced concrete in chloride environments: An overview , 2012 .

[89]  Rao Arsalan Khushnood,et al.  Comparative performance of different bacteria immobilized in natural fibers for self-healing in concrete , 2020 .

[90]  Xinying Lu,et al.  Application of the Nernst-Einstein equation to concrete , 1997 .

[91]  Diederik Jacques,et al.  Quantification of leaching kinetics in OPC mortars via a mesoscale model , 2018, Construction and Building Materials.

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

[93]  Xinying Lu,et al.  An experimental study on the properties of resistance to diffusion of chloride ions of fly ash and blast furnace slag concrete , 2000 .

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

[95]  Yimin Tang,et al.  Experimental and theoretical analysis on coupled effect of hydration, temperature and humidity in early-age cement-based materials , 2020 .

[96]  Velu Saraswathy,et al.  Studies on the corrosion resistance of reinforced steel in concrete with ground granulated blast-furnace slag--An overview. , 2006, Journal of hazardous materials.

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

[98]  K. M. Yusof,et al.  Atmospheric chloride penetration into concrete in semitropical marine environment , 1994 .

[99]  Turan Özturan,et al.  Estimation of chloride permeability of concretes by empirical modeling: Considering effects of cement type, curing condition and age , 2009 .

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

[101]  Tao Ji,et al.  A concrete mix proportion design algorithm based on artificial neural networks , 2006 .

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

[103]  Mohammad Shekarchi,et al.  Effect of Curing Conditions on the Service Life Design of RC Structures in the Persian Gulf Region , 2008 .

[104]  T. Zhao,et al.  Influence of the incorporation of recycled coarse aggregate on water absorption and chloride penetration into concrete , 2020 .

[105]  Diederik Jacques,et al.  Diffusivity of saturated ordinary Portland cement-based materials: A critical review of experimental and analytical modelling approaches , 2016 .

[106]  Jin Xia,et al.  Multi-phase modelling of electrochemical rehabilitation for ASR and chloride affected concrete composites , 2019, Composite Structures.

[107]  C. Poon,et al.  Prediction of compressive strength of recycled aggregate concrete using artificial neural networks , 2013 .

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

[109]  Mehdi Nikoo,et al.  Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm , 2019, Journal of Building Engineering.

[110]  A. Gandomi,et al.  Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures , 2011 .

[111]  Ahmed M. Azmy,et al.  Neural networks for predicting compressive strength of structural light weight concrete , 2009 .