Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods

Kaffayatullah Khan; Waqas Ahmad; Muhammad Nasir Amin; Muhammad Isfar Rafiq; Abdullah Mohammad Abu Arab; Inas Abdulalim Alabdullah; Hisham Alabduljabbar; Abdullah Mohamed

doi:10.1016/j.heliyon.2023.e16288

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods

Heliyon. 2023 May 13;9(5):e16288. doi: 10.1016/j.heliyon.2023.e16288. eCollection 2023 May.

Authors

Kaffayatullah Khan¹, Waqas Ahmad², Muhammad Nasir Amin¹, Muhammad Isfar Rafiq², Abdullah Mohammad Abu Arab¹, Inas Abdulalim Alabdullah³, Hisham Alabduljabbar⁴, Abdullah Mohamed⁵

Affiliations

¹ Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.
² Department of Civil Engineering, COMSATS University Islamabad, Abbottabad, 22060, Pakistan.
³ Department of Chemistry, College of Science, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.
⁴ Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia.
⁵ Research Centre, Future University in Egypt, New Cairo, 11835, Egypt.

Abstract

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R²), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

Keywords: AdaBoost; Cement mortar; Compressive strength; Decision tree; Waste glass powder.