Application and Analysis of Machine Learning Algorithms for Design of Concrete Mix with Plasticizer and without Plasticizer

Document Type : Regular Article

Authors

1 Post Graduate Student, Department of Civil Engineering, Indian Institute of Technology (IIT) BHU, Varanasi, India

2 Professor, Department of Civil Engineering, Indian Institute of Technology (IIT) BHU, Varanasi, India

Abstract

The objective of this paper is to find an alternative to conventional method of concrete mix design. For finding the alternative, 4 machine learning algorithms viz. multi-variable linear regression, Support Vector Regression, Decision Tree Regression and Artificial Neural Network for designing concrete mix of desired properties. The multi-variable linear regression model is just a simplistic baseline model, support vector regression Artificial Neural Network model were made because past researchers worked heavily on them, Decision tree model was made by authors own intuition. Their results have been compared to find the best algorithm. Finally, we check if the best performing algorithm is accurate enough to replace the convention method. For this, we utilize the concrete mix designs done in lab for various on site designs. The models have been designed for both mixes types – with plasticizer and without plasticizer The paper presents detailed comparison of four models Based on the results obtained from the four models, the best one has been selected based on high accuracy and least computational cost. Each sample had 24 features initially, out of which, most significant features were chosen which were contributing towards prediction of a variable using f regression and p values and models were trained on those selected features. Based on the R squared value, best fitting models were selected among the four algorithms used. From the paper, the author(s) conclude that decision tree regression is best for calculating the amount of ingredients required with R squared values close to 0.8 for most of the models. DTR model is also computationally cheaper than ANN and future works with DTR in mix design is highly recommended in this paper.

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References

[1]     Antoniou C, Dimitriou L, Pereira F. Mobility patterns, big data and transport analytics: tools and applications for modeling. Elsevier; 2019.
[2]     Abrams DA. Design of concrete mixtures. Structural Materials Research Laboratory. Bulletin 1918;1.
[3]     Kasperkiewicz J, Racz J, Dubrawski A. HPC Strength Prediction Using Artificial Neural Network. J Comput Civ Eng 1995;9:279–84. doi:10.1061/(ASCE)0887-3801(1995)9:4(279).
[4]     Yeh I-C. Modeling of strength of high-performance concrete using artificial neural networks. Cem Concr Res 1998;28:1797–808. doi:10.1016/S0008-8846(98)00165-3.
[5]     Nehdi M, El Chabib H, El Naggar MH. Predicting performance of self-compacting concrete mixtures using artificial neural networks. Mater J 2001;98:394–401.
[6]     Siddique R, Aggarwal P, Aggarwal Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Adv Eng Softw 2011;42:780–6. doi:10.1016/j.advengsoft.2011.05.016.
[7]     Dias WPS, Pooliyadda SP. Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 2001;15:371–9. doi:10.1016/S0950-0618(01)00006-X.
[8]     Öztaş A, Pala M, Özbay E, Kanca E, Çagˇlar N, Bhatti MA. Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 2006;20:769–75. doi:10.1016/j.conbuildmat.2005.01.054.
[9]     Ghafari E, Bandarabadi M, Costa H, Júlio E. Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model. J Mater Civ Eng 2015;27:04015017. doi:10.1061/(ASCE)MT.1943-5533.0001270.
[10]    Topçu İB, Sarıdemir M. Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput Mater Sci 2007;41:117–25. doi:10.1016/j.commatsci.2007.03.010.
[11]    Alshihri MM, Azmy AM, El-Bisy MS. Neural networks for predicting compressive strength of structural light weight concrete. Constr Build Mater 2009;23:2214–9. doi:10.1016/j.conbuildmat.2008.12.003.
[12]    Loh W-Y. Fifty Years of Classification and Regression Trees. Int Stat Rev 2014;82:329–48. doi:10.1111/insr.12016.
[13]    Erdal HI. Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng Appl Artif Intell 2013;26:1689–97. doi:10.1016/j.engappai.2013.03.014.
[14]    Chou J-S, Chiu C-K, Farfoura M, Al-Taharwa I. Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques. J Comput Civ Eng 2011;25:242–53. doi:10.1061/(ASCE)CP.1943-5487.0000088.
[15]    Deshpande N, Londhe S, Kulkarni S. Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression. Int J Sustain Built Environ 2014;3:187–98. doi:10.1016/j.ijsbe.2014.12.002.
[16]    Deepa C, SathiyaKumari K, Sudha VP. Prediction of the compressive strength of high performance concrete mix using tree based modeling. Int J Comput Appl 2010;6:18–24.
[17]    Mansouri I, Ozbakkaloglu T, Kisi O, Xie T. Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques. Mater Struct 2016;49:4319–34. doi:10.1617/s11527-015-0790-4.
[18]    Ayaz Y, Kocamaz AF, Karakoç MB. Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers. Constr Build Mater 2015;94:235–40. doi:10.1016/j.conbuildmat.2015.06.029.
[19]    Vapnik V. A note one class of perceptrons. Autom Remote Control 1964.
[20]    Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. Proc. fifth Annu. Work. Comput. Learn. theory - COLT ’92, New York, New York, USA: ACM Press; 1992, p. 144–52. doi:10.1145/130385.130401.
[21]    Gupta SM. Support vector machines based modelling of concrete strength. World Acad Sci Eng Technol 2007;36:305–11.
[22]    Yan K, Shi C. Prediction of elastic modulus of normal and high strength concrete by support vector machine. Constr Build Mater 2010;24:1479–85. doi:10.1016/j.conbuildmat.2010.01.006.
[23]    Cheng M-Y, Chou J-S, Roy AFV, Wu Y-W. High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model. Autom Constr 2012;28:106–15. doi:10.1016/j.autcon.2012.07.004.
[24]    Siddique R, Aggarwal P, Aggarwal Y, Gupta SM. Modeling properties of self-compacting concrete: support vector machines approach. Comput Concr 2008;5:123–9.
[25]    Aiyer BG, Kim D, Karingattikkal N, Samui P, Rao PR. Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. KSCE J Civ Eng 2014;18:1753–8. doi:10.1007/s12205-014-0524-0.
[26]    Yan K, Xu H, Shen G, Liu P. Prediction of splitting tensile strength from cylinder compressive strength of concrete by support vector machine. Adv Mater Sci Eng 2013;2013.
[27]    Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.

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History

  • Receive Date: 17 September 2020
  • Revise Date: 06 January 2021
  • Accept Date: 14 January 2021

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