Abstract
Concrete is one of the most essential construction materials used in construction industry. For assessment of concrete strength and quality, compressive strength is the most frequently utilized parameter. This paper establishes the application of Gaussian process, M5P model, random forest and random tree techniques for appropriate proportioning of the concrete mixes. The models proposed were based on six input parameters, namely cement, sand, coarse aggregate, water, curing period and fineness modulus, while the compressive strength was an output parameter. Five most popular statistical parameters such as Pearson correlation coefficient, mean absolute error, root mean square error, Scattering Index and Nash–Sutcliffe model efficiency were used for the assessment of the developed models. On comparison, it was found that better results were achieved with Radial bases kernel function based Gaussian process regression model as compared to other applied models. The suggested models are expected to save cost of materials, cost of labor work, time and contribute to greater accuracy. The concrete designed is anticipated to have more durability and therefore be more economical.
Similar content being viewed by others
References
Aggarwal S, Bhargava G, Sihag P (2021) Prediction of compressive strength of scc-containing metakaolin and rice husk ash using machine learning algorithms. In: Computational technologies in materials science, pp. 193–205. CRC Press, London.
Ali J, Khan R, Ahmad N, Maqsood I (2012) Random forests and Decision trees. Int J Comput Sci Issues 9(5):272–278
Atici U (2011) Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Exp Syst Appl 38:9609–9618
Ayaz Y, Kocamaz AF, Karakoc MB (2015) 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 94:235–240
Benosman M, Borggaard J (2019) Machine learning methods for predicting the field compressive strength of concrete. Mitsubishi Electric Res Lab, pp 1–33. http://www.merl.com
Breiman L (1999) Random forests—random features. University of California, Berkeley, p 567
Breiman L (2001) Random forests. Mach Learn 45(1):25–32
Breiman L, Friedman JH, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall, CRC Press, First Edition, New York
Chakraborty UK (2009) Static and dynamic modeling of solid oxide fuel cell using genetic programming. Energy 34(6):740–751
Chatur PN, Khobragade R, Asudani DS (2013) Effectiveness evaluation of regression models for predictive data-mining. Int J Manag IT Eng 3(3):465–483
Deepa C, Kumari KS, Sudha VP (2010) Prediction of the compressive strength of high performance concrete mix using tree based modeling. Int J Comput Appl 6(5):18–24
DeRousseau MA, Laftchiev E, Kasprzyk JR, Rajagopalan B, Srubar III WV (2019) Machine learning methods for predicting the field compressive strength of concrete. Mitsubishi Electric Res Lab, 1–33. http://www.merl.com
Domone P, Soutsos M (1994) An approach to the proportioning of high-strength concrete mixes. Concrete Int 16:26–31
Ekinci S, Celebi UB, Bala M, Amasyali MF, Boyaci UK (2011) Predictions of oil/chemical tanker main design parameters using computational intelligence techniques. Appl Soft Comput 11:2356–2366
Erdal H, Erdal M, Şimşek O, Erdal HI (2018) Prediction of concrete compressive strength using non-destructive test results. Comput Concrete, 21(48):407–17. Doi: https://doi.org/10.12989/cac.2018.21.4.407.
Erdal H, Karahanoglu İ (2016) Bagging ensemble models for bank profitability: An empirical research on Turkish development and investment banks. Appl Soft Comput 49:861–867
Erdal Hİ (2013) Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng Appl Artif Intell 26(7):1689–1697
Gaidhane VH, Kumar N, Mittal RK, Rajevenceltha J (2019) An efficient approach for cement strength prediction. Int J Comput Appl, pp 1–11
Galan A (1967) Estimate of concrete strength by ultrasonic pulse velocity and damping constant. ACI J Proc 64(10):678–684
Gupta S (2013) Concrete Mix Design Using Artificial Neural Network. Journal on Today’s ideas-Tomorrow’s Technologies, Chitkara University 1(1):19–43
Gupta S (2014) Using Fuzzy logic approach to find the compressive strength of concrete. Res Civil Environ Eng 2(3):120–132
Kumar V, Sihag P, Keshavarzi A, Pandita S, Rodríguez-Seijo A (2021) Soft computing techniques for appraisal of potentially toxic elements from Jalandhar (Punjab) India. Appl Sci 11(18):8362
Kuss M (2006) Gaussian process models for robust regression, classification, and reinforcement learning. In: Doctoral dissertation, echnische Universität Darmstadt Darmstadt, Germany
McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash-Sutcliffe efficiency index. J Hydrol Eng 11(6):597–602
Mehdipour V, Stevenson DS, Memarianfard M, Sihag P (2018) Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Quality Atmos Health Springer Nature. Doi: https://doi.org/10.1007/s11869-018-0615-z.
Pal M, Singh NK, Tiwari NK (2012) M5 model tree for pier scour prediction using field dataset. KSCE J Civil Eng 16(6):1079–1084. Doi: https://doi.org/10.1007/s12205-012-1472-1
Salih A, Rafiq S, Sihag P, Ghafor K, Mahmood W, Sarwar W (2021) Systematic multiscale models to predict the effect of high-volume fly ash on the maximum compression stress of cement-based mortar at various water/cement ratios and curing times. Measurement 171:108819
Sattari MT, Pal M, Mirabbasi R, Abraham J (2018) Ensemble of M5 model tree based modelling of sodium adsorption ratio. J Artif Data Mining 6(1):69–78. https://doi.org/10.22044/JADM.2017.5540.1663
Shabani S, Samadianfard S, Sattari MT, Mosavi A, Shamshirband S, Kmet T, Várkonyi-Kóczy AR (2020) Modeling pan evaporation using gaussian process regression k-nearest neighbours random forest and support vector machines. Compar Anal Atmos 2020 11:66. Doi:https://doi.org/10.3390/atmos11010066
Sihag P, Tiwari NK, Ranjan S (2019) Prediction of cumulative infiltration of sandy soil using random forest approach. J Appl Water Eng Res 7(2):118–142. https://doi.org/10.1080/23249676.2018.1497557
Sihag P, Tiwari NK, Ranjan S (2017) Modelling of infiltration of sandy soil using gaussian process regression. Modeling Earth Syst Environ 3(3):1091–1100
Singh B, Sihag P, Tomar A, Sehgad A (2019) Estimation of compressive strength of high-strength concrete by random forest and M5P model tree approaches. J Mater Eng Struct 6:583–592
Singh K, Dharmendra (2019) Power density analysis by using soft computing techniques for microbial fuel cell. J Environ Treat Techniq. Special Issue on Environment, Management and Economy, pp 1068–1073
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192
Thakur MS, Pandhiani SM, Kashyap V, Upadhya A, Sihag P (2021) Predicting bond strength of FRP bars in concrete using soft computing techniques. Arab J Sci Eng 46(5):4951–4969
Upadhya A, Thakur MS, Sharma N, Sihag P (2021) Assessment of soft computing-based techniques for the prediction of marshall stability of asphalt concrete reinforced with glass fiber. Int J Pave Res Technol, pp 1–20
Wang Y, Witten I (1997) Inducing model trees for continuous classes. In: Ninth European conference on machine learning, Prague, Czech Republic
Yeh IC (2007) Modeling slump flow of concrete using second order regressions and artificial neural networks. Cement Concrete Compos 29:474–480
Yeh IC, Lien LC (2009) Knowledge discovery of concrete material using genetic operation trees. Exp Syst Appl 36(3):5807–5812
Yetilmezsoy K, Sihag P, Kiyan E, Doran B (2021) A benchmark comparison and optimization of Gaussian process regression, support vector machines, and M5P tree model in approximation of the lateral confinement coefficient for CFRP-wrapped rectangular/square RC columns. Eng Struct, 246:113106
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We do not have any conflict of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gupta, S., Sihag, P. Prediction of the compressive strength of concrete using various predictive modeling techniques. Neural Comput & Applic 34, 6535–6545 (2022). https://doi.org/10.1007/s00521-021-06820-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06820-y