Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Afnan Nafees; Muhammad Faisal Javed; Sherbaz Khan; Kashif Nazir; Furqan Farooq; Fahid Aslam; Muhammad Ali Musarat; Nikolai Ivanovich Vatin

doi:10.3390/ma14247531

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Materials (Basel). 2021 Dec 8;14(24):7531. doi: 10.3390/ma14247531.

Authors

Afnan Nafees¹, Muhammad Faisal Javed¹, Sherbaz Khan¹, Kashif Nazir², Furqan Farooq^{1

3

4}, Fahid Aslam⁵, Muhammad Ali Musarat⁶, Nikolai Ivanovich Vatin⁷

Affiliations

¹ Department of Civil Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
² Department of Civil Engineering, School of Engineering, Nazabayev University, Astana 010000, Kazakhstan.
³ Military Engineering Service (MES), Ministry of Defence (MoD), Rawalpindi 43600, Pakistan.
⁴ Faculty of Civil Engineering, Cracow University of Technology, 24 Warszawska Str., 31-155 Cracow, Poland.
⁵ Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
⁶ Department of Civil and Environmental Engineering, Bandar Seri Iskandar 32610, Perak, Malaysia.
⁷ Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.

Abstract

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO₂ emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R² value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases' features to promote the usage of green concrete.

Keywords: cross-validation; green concrete; industrial waste; machine learning; predictive modeling; sensitivity analysis.