The application of artificial intelligence approaches like machine learning (ML) to forecast material properties is an effective strategy to reduce multiple trials during experimentation. This study performed ML modeling on 481 mixes of geopolymer concrete with nine input variables, including curing time, curing temperature, specimen age, alkali/fly ash ratio, Na₂SiO₃/NaOH ratio, NaOH molarity, aggregate volume, superplasticizer, and water, with CS as the output variable. Four types of ML models were employed to anticipate the compressive strength of geopolymer concrete, and their performance was compared to find out the most accurate ML model. Two individual ML techniques, support vector machine and multi-layer perceptron neural network, and two ensembled ML methods, AdaBoost regressor and random forest, were employed to achieve the study’s aims. The performance of all models was confirmed using statistical analysis, k-fold evaluation, and correlation coefficient (R²). Moreover, the divergence of the estimated outcomes from those of the experimental results was noted to check the accuracy of the models. It was discovered that ensembled ML models estimated the compressive strength of the geopolymer concrete with higher precision than individual ML models, with random forest having the highest accuracy. Using these computational strategies will accelerate the application of construction materials by decreasing the experimental efforts.

Every year, millions of tons of red mud (RDM) are created across the globe. Its storage is a major environmental issue due to its high basicity and tendency for leaching. This material is often kept in dams, necessitating previous attention to the disposal location, as well as monitoring and maintenance during its useful life. As a result, it is critical to develop an industrial solution capable of consuming large quantities of this substance. Many academics have worked for decades to create different cost-effective methods for using RMD. One of the most cost-effective methods is to use RMD in cement manufacture, which is also an effective approach for large-scale RMD recycling. This article gives an overview of the use of RMD in concrete manufacturing. Other researchers’ backgrounds were considered and examined based on fresh characteristics, mechanical properties, durability, microstructure analysis, and environmental impact analysis. The results show that RMD enhanced the mechanical properties and durability of concrete while reducing its fluidity. Furthermore, by integrating 25% of RDM, the environmental consequences of cumulative energy demand (CED), global warming potential (GWP), and major criteria air pollutants (CO, NO_X, Pb, and SO₂) were minimized. In addition, the review assesses future researcher guidelines for concrete with RDM to improve performance.

According to the authors’ best information, the majority of research focuses on other waste materials, such as recycling industrial waste (glass, silica fume, marble and waste foundry sand), etc. However, some researchers suggest dune sand as an alternative material for concrete production, but knowledge is still scarce. Therefore, a comprehensive review is required on dune sand to evaluate its current progress as well as its effects on the strength and durability properties of concrete. The review presents detailed literature on dune sand in concrete. The important characteristics of concrete such as slump, compressive, flexural, cracking behaviors, density, water absorption and sulfate resistance were considered for analysis. Results indicate that dune sand can be used in concrete up to 40% without any negative effect on strength and durability. The negative impact of dune sand on strength and durability was due to poor grading and fineness, which restricts the complete (100%) substation of dune sand. Furthermore, a decrease in flowability was observed. Finally, the review highlights the research gap for future studies.

The use of superabsorbent polymers, sometimes known as SAP, is a tremendously efficacious method for reducing the amount of autogenous shrinkage (AS) that occurs in high-performance concrete. This study utilizes support vector regression (SVR) as a standalone machine-learning algorithm (MLA) which is then ensemble with boosting and bagging approaches to reduce the bias and overfitting issues. In addition, these ensemble methods are optimized with twenty sub-models with varying the n^th estimators to achieve a robust R². Moreover, modified bagging as random forest regression (RFR) is also employed to predict the AS of concrete containing supplementary cementitious materials (SCMs) and SAP. The data for modeling of AS includes water to cement ratio (W/C), water to binder ratio (W/B), cement, silica fume, fly ash, slag, the filer, metakaolin, super absorbent polymer, superplasticizer, super absorbent polymer size, curing time, and super absorbent polymer water intake. Statistical and k-fold validation is used to verify the validation of the data using MAE and RMSE. Furthermore, SHAPLEY analysis is performed on the variables to show the influential parameters. The SVM with AdaBoost and modified bagging (RF) illustrates strong models by delivering R² of approximately 0.95 and 0.98, respectively, as compared to individual SVR models. An enhancement of 67% and 63% in the RF model, while in the case of SVR with AdaBoost, it was 47% and 36%, in RMSE and MAE of both models, respectively, when compared with the standalone SVR model. Thus, the impact of a strong learner can upsurge the efficiency of the model.

The utilization of carbon-fiber-reinforced polymer (CFRP) composites as strengthening materials for structural components has become quite famous over the last couple of decades. The present experimental study was carried out to examine the effect of varied widths of externally bonded CFRP on the debonding strain of CFRP and the failure mode of plain concrete beams. Twelve plain concrete prims measuring 100 mm × 100 mm × 500 mm were cast and tested under identical loading conditions. The twelve specimens include two control prisms, i.e., without CFRP strips, and the remaining ten prisms were reinforced with CFRP strips with widths of 10 mm, 20 mm, 30 mm, 40 mm, and 50 mm, respectively, i.e., two prisms in each group. Four-point loading flexural testing was carried out, and the resulting data are presented in the form of peak load vs. midpoint displacement, load vs. concrete strain, and load vs. CFRP strain. The peak load was directly recorded from the testing machine, while the midpoint deflection was recorded through the linear variable differential transducer (LVDT) installed at the midpoint. To measure the strain, two separate strain gauges were installed at the bottom of each concrete prism, i.e., one on the concrete surface and the other on the surface of the CFRP strip. The results of this study indicate that the debonding strain is a function of CFRP strip width and that the failure patterns of beams are significantly affected by the CFRP reinforcement ratio.