Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches

Abstract

This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid attack, the dataset produced through testing methods was utilized. The test results indicated that the CS loss of the cement mortar might be reduced by utilizing glass powder. For maximum resistance to acidic conditions, the optimum proportion of glass powder was noted to be 10% as cement, which restricted the CS loss to 5.54%, and 15% as a sand replacement, which restricted the CS loss to 4.48%, compared to the same mix poured in plain water. The built ML models also agreed well with the test findings and could be utilized to calculate the CS of cementitious composites incorporating glass powder after the acid attack. On the basis of the R2 value (random forest: 0.97 and bagging regressor: 0.96), the variance between tests and forecasted results, and errors assessment, it was found that the performance of both the bagging regressor and random forest models was similarly accurate.

Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

“…Wang et al [42] developed ML models to forecast the CS loss after an acid-attack test on ESP-based mortar. Similarly, Alfaiad et al [43] claimed to address the CS loss in an acidic environment after performing lab tests on RGPbased mortar. The experimental data were then utilized to develop bagging and random forest ML models.…”

“…Furthermore, a deliberate attempt was undertaken to choose publications with comparable components. The relevant data required for determining the proportion loss in the CS of ESP and RGP-based reformed mortar has been acquired from the existing literature sources [42,43]. Cement, glass, and eggshell types were the same throughout the database.…”

“…The CS data, after an acid attack, rely on eight input parameters: cement, s ter, silica fume, superplasticizer, glass powder, eggshell powder, and 90 days of and RGP are often observed ingredients frequently included in the database s Furthermore, a deliberate attempt was undertaken to choose publications with rable components. The relevant data required for determining the proportion lo CS of ESP and RGP-based reformed mortar has been acquired from the existing l sources [42,43]. Cement, glass, and eggshell types were the same throughout t base.…”

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite

“…The trash produced by agriculture and industry is staggering [ 1 ]. Substantial economic losses have come from the reckless disposal of hazardous solid wastes, many of which are pathogenic, flammable, highly reactive, and degrading [ 2 ].…”