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

Abstract

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.

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

“…LiDAR sensors can overcome these challenges by providing detailed 3D mapping and object detection capabilities. This ensures that proximity analysis is not solely dependent on GPS signals but extends to a more robust and versatile solution that can operate effectively in the complex, GPS-denied topography of underground mines [ 46 , 47 ].…”

Understanding Penetration Attenuation of Permeable Concrete: A Hybrid Artificial Intelligence Technique Based on Particle Swarm Optimization

“…The model provides a reference for the maintenance engineers of permeable pavement to understand the blocking behavior, propose a reasonable maintenance time, and determine the corresponding maintenance strategies. To understand the predictive results of the clogging behavior with other research and standard codes, an in-depth comparison was conducted with the previous studies [65,66]. Figure 12 demonstrates the comparison between the proposed model and the previous studies (one is a physical-based prediction model considering the clogging behavior [67,68]; the other is a hybrid machine learning algorithm based on a PSO-SVM model [52]).…”

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

“…Compared with traditional cement concrete, its material and preparation process have changed. The main components of geopolymer are silicate, aluminate and other alkaline active substances, and polymer formed by the reaction of silicic acid and aluminic acid [5,6]. In addition, compared with traditional cement, industrial by-products that are now treated as waste can be used as feedstocks for geopolymers.…”