Predicting the damage to cementitious composites due to acid attack and evaluating the effectiveness of eggshell powder using interpretable artificial intelligence models

Jin, Conghe; Qian, Yongjiu; Khan, Kaffayatullah; Ahmad, Ayaz; Amin, Muhammad Nasir; Althoey, Fadi; Nawaz, R.

doi:10.1016/j.mtcomm.2023.107333

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…The experimental data were then utilized to develop bagging and random forest ML models. Jin et al [44] studied the application of genetic programming-based ML models to anticipate the percentage loss in CS when ESP-based mortar samples are subjected to an acidic environment. Amin et al [45] performed experimental and ML-based analyses of RGP-based concrete to study the flexural behavior of specimens.…”

Section: Figurementioning

confidence: 99%

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

Zhu,

Wu,

et al. 2024

Buildings

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The present study utilized machine learning (ML) techniques to investigate the effects of eggshell powder (ESP) and recycled glass powder (RGP) on cement composites subjected to an acidic setting. A dataset acquired from the published literature was employed to develop machine learning-based predictive models for the cement mortar’s compressive strength (CS) decrease. Artificial neural network (ANN), K-nearest neighbor (KNN), and linear regression (LR) were chosen for modeling. Also, RreliefF analysis was performed to study the relevance of variables. A total of 234 data points were utilized to train/test ML algorithms. Cement, sand, water, silica fume, superplasticizer, glass powder, eggshell powder, and 90 days of CS were considered as input variables. The outcomes of the research showed that the employed models could be applied to evaluate the reduction percentage of CS in cement composites, including ESP and RGP, after being exposed to acid. Based on the R2 values (0.87 for the ANN, 0.81 for the KNN, and 0.78 for LR), as well as the assessment of variation between test values and anticipated outcomes and errors (1.32% for ANN, 1.57% for KNN, and 1.69% for LR), it was determined that the accuracy of the ANN model was superior to the KNN and LR. The sieve diagram exhibited a correlation amongst the model predicted and target results. The outcomes of the RreliefF analysis suggested that ESP and RGP significantly influenced the CS loss of samples with RreliefF scores of 0.26 and 0.21, respectively. Based on the outcomes of the research, the ANN approach was determined suitable for predicting the CS loss of mortar subjected to acidic environments, thereby eliminating lab testing trails.

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Section: Figurementioning

confidence: 99%

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

Zhu,

Wu,

et al. 2024

Buildings

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Compressive strength of waste-derived cementitious composites using machine learning

Tian,

Lu,

Zhou

et al. 2024

Reviews on Advanced Materials Science

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Marble cement (MC) is a new binding material for concrete, and the strength assessment of the resulting materials is the subject of this investigation. MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithms can help with the formulation of better MC-based concrete. ML models that could predict the compressive strength (CS) of MC-based concrete that contained FA and RHA were built. Gene expression programming (GEP) and multi-expression programming (MEP) were used to build these models. Additionally, models were evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor’s diagram, and comparing theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R 2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash–Sutcliffe efficiency = 0.960). According to the sensitivity analysis, the prediction of CS was most affected by curing age and MC content, then by FA and RHA contents. Incorporating waste materials such as marble powder, RHA, and FA into building materials can help reduce environmental impacts and encourage sustainable development.

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Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions

Liu,

Khan,

Amin

et al. 2024

Reviews on Advanced Materials Science

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The cementitious composite’s resistance to the introduction of harmful ions is the primary criterion that is used to evaluate its durability. The efficacy of glass and eggshell powder in cement mortar exposed to 5% sulfuric acid solutions was investigated in this study using artificial intelligence (AI)-aided approaches. Prediction models based on AI were built using experimental datasets with multi-expression programming (MEP) and gene expression programming (GEP) to forecast the percentage decrease in compressive strength (CS) after acid exposure. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to examine the significance of prospective constituents. The results of the experiments substantiated these models. High coefficient of determination (R 2) values (MEP: 0.950 and GEP: 0.913) indicated statistical significance, meaning that test results and anticipated outcomes were consistent with each other and with the MEP and GEP models, respectively. According to SHAP analysis, the amount of eggshell and glass powder (GP) had the most significant link with CS loss after acid deterioration, showing a positive and negative correlation, respectively. In order to optimize efficiency and cost-effectiveness, the created models possess the capability to theoretically assess the decline in CS of GP-modified mortar across various input parameter values.

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Predicting the damage to cementitious composites due to acid attack and evaluating the effectiveness of eggshell powder using interpretable artificial intelligence models

Cited by 3 publications

References 52 publications

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

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

Compressive strength of waste-derived cementitious composites using machine learning

Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions

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