The determination of ground granulated concrete compressive strength based machine learning models

Mohammed H. Mohana


The advancement of machine learning (ML) models has received remarkable attention by several science and engineering applications. Within the material engineering, ML models are usually utilized for building an expert system for supporting material design and attaining an optimal formulation material sustainability and maintenance. The current study is conducted on the based of the utilization of ML models for modeling compressive strength (Cs) of ground granulated blast furnace slag concrete (GGBFSC). Random Forest (RF) model is developed for this purpose. The predictive model is constructed based on multiple correlated properties for the concrete material including coarse aggregate (CA), curing temperature (T), GGBFSC to total binder ratio (GGBFSC/B), water to binder ratio (w/b), water content (W), fine aggregate (FA), superplasticizer (SP). A total of 268 experimental dataset are gather form the open-source previous published researches, are used to build the predictive model. For the verification purpose, a predominant ML model called support vector machine (SVM) is developed. The efficiency of the proposed predictive and the benchmark models is evaluated using statistical formulations and graphical presentation. Based on the attained prediction accuracy, RF model demonstrated an excellent performance for predicting the Cs using limited input parameters. Overall, the proposed methodology showed an exceptional predictive model that can be utilized for modeling compressive strength of GGBFSC.


Compressive strength prediction, granulated blast furnace slag concrete, variability performance, machine learning models, random forest.


P. J. M. Monteiro, S. A. Miller, and A. Horvath, “Towards sustainable concrete,” Nature Materials, vol. 16, no. 7, pp. 698–699, 2017.

A. Kendall, G. A. Keoleian, and M. D. Lepech, “Materials design for sustainability through life cycle modeling of engineered cementitious composites,” Materials and Structures/Materiaux et Constructions, 2008.

J. W. Bullard, P. E. Stutzman, L. M. Ordoñez Belloc, E. J. Garboczi, and D. P. Bentz, “Virtual cement and concrete testing laboratory for quality testing and sustainability of concrete,” in American Concrete Institute, ACI Special Publication, 2009.

S. C. Pal, A. Mukherjee, and S. R. Pathak, “Investigation of hydraulic activity of ground granulated blast furnace slag in concrete,” Cement and Concrete Research, 2003.

Y.-C. Tang, L.-J. Li, W.-X. Feng, F. Liu, and M. Zhu, “Study of seismic behavior of recycled aggregate concrete-filled steel tubular columns,” Journal of Constructional Steel Research, vol. 148, pp. 1–15, 2018.

Y. Tang et al., “Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision,” Robotics and Computer-Integrated Manufacturing, vol. 59, pp. 36–46, 2019.

A. Behnood and E. M. Golafshani, “Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves,” Journal of Cleaner Production, 2018.

S. J. Barnett, M. N. Soutsos, S. G. Millard, and J. H. Bungey, “Strength development of mortars containing ground granulated blast-furnace slag: Effect of curing temperature and determination of apparent activation energies,” Cement and Concrete Research, 2006.

D. P. Bentz, “Influence of water-to-cement ratio on hydration kinetics: Simple models based on spatial considerations,” Cement and Concrete Research, vol. 36, no. 2, pp. 238–244, 2006.

S. E. Chidiac and D. K. Panesar, “Evolution of mechanical properties of concrete containing ground granulated blast furnace slag and effects on the scaling resistance test at 28 days,” Cement and Concrete Composites, vol. 30, no. 2, pp. 63–71, 2008.

A. Cheng, R. Huang, J.-K. Wu, and C.-H. Chen, “Influence of GGBS on durability and corrosion behavior of reinforced concrete,” Materials Chemistry and Physics, vol. 93, no. 2–3, pp. 404–411, 2005.

E. Özbay, M. Erdemir, and H. İ. Durmuş, “Utilization and efficiency of ground granulated blast furnace slag on concrete properties – A review,” Construction and Building Materials, vol. 105, pp. 423–434, 2016.

H. SONG and V. SARASWATHY, “Studies on the corrosion resistance of reinforced steel in concrete with ground granulated blast-furnace slag—An overview,” Journal of Hazardous Materials, vol. 138, no. 2, pp. 226–233, 2006.

A. M. Neville, Properties of Concrete. 2011.

A. Oner and S. Akyuz, “An experimental study on optimum usage of GGBS for the compressive strength of concrete,” Cement and Concrete Composites, vol. 29, no. 6, pp. 505–514, 2007.

V. G. Papadakis and S. Tsimas, “Supplementary cementing materials in concrete,” Cement and Concrete Research, vol. 32, no. 10, pp. 1525–1532, 2002.

E. Vintzileou and E. Panagiotidou, “An empirical model for predicting the mechanical properties of FRP-confined concrete,” Construction and Building Materials, vol. 22, no. 5, pp. 841–854, 2008.

M. Azimi-Pour, H. Eskandari-Naddaf, and A. Pakzad, “Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete,” Construction and Building Materials, 2020.

J. A. Abdalla, R. Hawileh, and A. Al-Tamimi, “Prediction of FRP-concrete ultimate bond strength using Artificial Neural Network,” in 2011 4th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2011, 2011.

D. Van Dao, H.-B. Ly, S. H. Trinh, T.-T. Le, and B. T. Pham, “Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete,” Materials, vol. 12, no. 6, p. 983, 2019.

K. Yan and C. Shi, “Prediction of elastic modulus of normal and high strength concrete by support vector machine,” Construction and Building Materials, vol. 24, no. 8, pp. 1479–1485, 2010.

M. H. Mohana, “Reinforced concrete confinement coefficient estimation using soft computing models,” Periodicals of Engineering and Natural Sciences, vol. 7, no. 4, pp. 1833–1844, 2019.

Z. Dahou, Z. Mehdi Sbartaï, A. Castel, and F. Ghomari, “Artificial neural network model for steel-concrete bond prediction,” Engineering Structures, vol. 31, no. 8, pp. 1724–1733, 2009.

M. I. Khan, “Predicting properties of High Performance Concrete containing composite cementitious materials using Artificial Neural Networks,” Automation in Construction, vol. 22, pp. 516–524, 2012.

M. Azimi-Pour and H. Eskandari-Naddaf, “ANN and GEP prediction for simultaneous effect of nano and micro silica on the compressive and flexural strength of cement mortar,” Construction and Building Materials, vol. 189, pp. 978–992, 2018.

Z. M. Yaseen, H. A. Afan, and M. T. Tran, “Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm,” in IOP Conference Series: Earth and Environmental Science, 2018.

A. A. H. Alwanas, A. A. Al-Musawi, S. Q. Salih, H. Tao, M. Ali, and Z. M. Yaseen, “Load-carrying capacity and mode failure simulation of beam-column joint connection: Application of self-tuning machine learning model,” Engineering Structures, vol. 194, no. November 2018, pp. 220–229, 2019.

A. A. Al-Musawi, A. A. H. Alwanas, S. Q. Salih, Z. H. Ali, M. T. Tran, and Z. M. Yaseen, “Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model,” Engineering with Computers, 2018.

L. M. R. Mahmmod, Z. M. R. A. Rasoul, M. S. Radhi, and S. S. Wajde, “Sustainable utilization of polyethylene terephthalate in producing local precast flooring concrete slabs,” Periodicals of Engineering and Natural Sciences, vol. 7, no. 4, pp. 1990–1995, 2019.

A. Ashrafian, F. Shokri, M. J. T. Amiri, Z. M. Yaseen, and M. Rezaie-Balf, “Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model,” Construction and Building Materials, vol. 230, p. 117048, 2020.

C. Bilim, C. D. Atiş, H. Tanyildizi, and O. Karahan, “Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network,” Advances in Engineering Software, vol. 40, no. 5, pp. 334–340, 2009.

B. Boukhatem, M. Ghrici, S. Kenai, and A. Tagnit-Hamou, “Prediction of Efficiency Factor of Ground-Granulated Blast-Furnace Slag of Concrete Using Artificial Neural Network.,” ACI Materials Journal, vol. 108, no. 1, 2011.

H. Naderpour and S. A. Alavi, “A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of Adaptive Neuro-Fuzzy Inference System,” Composite Structures, 2017.

M. T. Uddin, A. H. Mahmood, M. R. I. Kamal, S. M. Yashin, and Z. U. A. Zihan, “Effects of maximum size of brick aggregate on properties of concrete,” Construction and Building Materials, vol. 134, pp. 713–726, 2017.

A. Lee, Z. W. Geem, and K.-D. Suh, “Determination of optimal initial weights of an artificial neural network by using the harmony search algorithm: application to breakwater armor stones,” Applied Sciences, vol. 6, no. 6, p. 164, 2016.

S.-W. Liou, C.-M. Wang, and Y.-F. Huang, “Integrative Discovery of Multifaceted Sequence Patterns by Frame-Relayed Search and Hybrid PSO-ANN.,” J. UCS, vol. 15, no. 4, pp. 742–764, 2009.

X. L. Chen, J. P. Fu, J. L. Yao, and J. F. Gan, “Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model,” Engineering with Computers, 2018.

H. Moon and H. Shin, “Utilization of ready mixed concrete sludge for improving the strength of concrete with GGBF slag,” J. Korean Soc. Civ. Eng, vol. 22, pp. 315–326, 2002.

K.-M. Lee, K.-H. Kwon, H.-K. Lee, S.-H. Lee, and G.-Y. Kim, “Characteristics of Autogenous Shrinkage for Concrete Containing Blast-Furnace Slag,” Journal of the Korea Concrete Institute, vol. 16, no. 5, pp. 621–626, 2004.

K. M. Lee, H. K. Lee, S. H. Lee, and G. Y. Kim, “Autogenous shrinkage of concrete containing granulated blast-furnace slag,” Cement and Concrete Research, vol. 36, no. 7, pp. 1279–1285, 2006.

Q. Li, Z. Li, and G. Yuan, “Effects of elevated temperatures on properties of concrete containing ground granulated blast furnace slag as cementitious material,” Construction and Building Materials, vol. 35, pp. 687–692, 2012.

P. J. Wainwright and N. Rey, “The influence of ground granulated blastfurnace slag (GGBS) additions and time delay on the bleeding of concrete,” Cement and Concrete Composites, vol. 22, no. 4, pp. 253–257, 2000.

L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

A. Sharafati et al., “The potential of novel data mining models for global solar radiation prediction,” International Journal of Environmental Science and Technology, no. 0123456789, 2019.

S. Janitza, G. Tutz, and A.-L. Boulesteix, “Random forest for ordinal responses: prediction and variable selection,” Computational Statistics & Data Analysis, vol. 96, pp. 57–73, 2016.

S. P. Kumar and A. Raaza, “Study and analysis of intrusion detection system using random forest and linear regression,” Periodicals of Engineering and Natural Sciences, vol. 6, no. 1, pp. 197–200, 2018.

Z. M. Yaseen, M. T. Tran, S. Kim, T. Bakhshpoori, and R. C. Deo, “Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach,” Engineering Structures, vol. 177, no. April, pp. 244–255, 2018.

P. Cortez, “Data mining with neural networks and support vector machines using the R/rminer tool,” in Industrial conference on data mining, 2010, pp. 572–583.

C. Cortes and V. Vapnik, “Support vector machine,” Machine learning, vol. 20, no. 3, pp. 273–297, 1995.

A. J. Smola, “Regression estimation with support vector learning machines.” Master’s thesis, Technische Universität München, 1996.

P. Cortez, M. Portelinha, S. Rodrigues, V. Cadavez, and A. Teixeira, “Lamb meat quality assessment by support vector machines,” Neural Processing Letters, vol. 24, no. 1, pp. 41–51, 2006.

A. T. C. Goh and S. H. Goh, “Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data,” Computers and Geotechnics, vol. 34, no. 5, pp. 410–421, 2007.

L. H. Hamel, Knowledge discovery with support vector machines, vol. 3. John Wiley & Sons, 2011.

C.-M. Huang, Y.-J. Lee, D. K. J. Lin, and S.-Y. Huang, “Model selection for support vector machines via uniform design,” Computational Statistics & Data Analysis, vol. 52, no. 1, pp. 335–346, 2007.

L. Diop et al., “The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River,” Environmental Earth Sciences, vol. 77, no. 5, p. 182, 2018.

Z. M. Yaseen et al., “Predicting compressive strength of lightweight foamed concrete using extreme learning machine model,” Advances in Engineering Software, 2017.

B. Keshtegar, M. Bagheri, and Z. M. Yaseen, “Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model,” Composite Structures, 2019.

S. K. Zamim, N. S. Faraj, I. A. Aidan, F. M. S. Al-Zwainy, M. A. AbdulQader, and I. A. Mohammed, “Prediction of dust storms in construction projects using intelligent artificial neural network technology,” Periodicals of Engineering and Natural Sciences, vol. 7, no. 4, pp. 1659–1666, 2019.

Z. S. Khozani et al., “Determination of compound channel apparent shear stress: application of novel data mining models,” Journal of Hydroinformatics, 2019.

S. Q. Salih, A. A. Alsewari, B. Al-Khateeb, and M. F. Zolkipli, “Novel Multi-swarm Approach for Balancing Exploration and Exploitation in Particle Swarm Optimization,” in Recent Trends in Data Science and Soft Computing, 2019, pp. 196–206.

S. Q. Salih, “A New Training Method based on Black Hole Algorithm for Convolutional Neural Network,” Journal of Southwest Jiaotong University, vol. 54, no. 3, 2019.

S. Q. Salih and A. A. Alsewari, “A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer,” Neural Computing and Applications, pp. 1–28, 2019.

M. A. Ghorbani, R. C. Deo, V. Karimi, Z. M. Yaseen, and O. Terzi, “Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey,” Stochastic Environmental Research and Risk Assessment, pp. 1–15, 2017.

S. Naganna, P. Deka, M. Ghorbani, S. Biazar, N. Al-Ansari, and Z. Yaseen, “Dew Point Temperature Estimation: Application of Artificial Intelligence Model Integrated with Nature-Inspired Optimization Algorithms,” Water, 2019.

MUSA, Kanaan Mohammad; SHATTNAN, Adnan Turki; SALEH, Amjed Hassoon. Manufacturing Enamel Resin Using Furancarboxalehyde-3 Compound. Journal of Computational and Theoretical Nanoscience, 2019, 16.1: 130-133.



  • There are currently no refbacks.

Copyright (c) 2020 Mohammed H. Mohana

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License