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This study aims to investigate the compressive strength (CS) of foamcrete (FC), with a specific emphasis on its 7-day and 28-day performance. The multi-layer perceptron (MLP) and random forest (RF) machine learning techniques have been selected for forecasting the required outputs. Eight input parameters: density, cement, sand, sand-to-cement ratio, water-to-cement ratio, sand size, foam, and agent, were used to predict the CS of FC. Further, SHapley Additive exPlanations (SHAP) analysis was performed for feature interaction. The results indicate that the RF model had higher accuracy in forecasting the CS of FC. The assessment metrics, including R2 values of 0.952 for 7 days and 0.958 for 28 days strength, and the mean absolute errors (MAE) of 1.47 MPa and 1.71 MPa, respectively, verified the results. A user-friendly graphical user interface (GUI) was developed to predict the CS of foam concrete using a machine learning model based on key input parameters. The impact of the study's findings is of great importance in reducing project costs by avoiding costly laboratory work and speeding up achieving desired outcomes.

Geopolymers (GPs) are produced using a variety of alumina and silica-rich ingredients, including natural sources, such as calcined clays, as well as waste by-products, like fly ash and slag. It is difficult to measure the effect of each parameter on the strength of GP composites via experimental research. In this regard, this research aimed to build artificial intelligence (AI)-aided estimation models to quantify the impact of mix proportions on the compressive strength (CS) of MK-based GP mortar. Gene expression programming (GEP) and multi-expression programming (MEP) tools were used for models' development due to their advantage of yielding model equations for future predictions. Hyperparameters in GEP and MEP were systematically adjusted to enhance the models' optimal predictability. The regression and error analysis proved the superiority of MEP models over GEP models. In comparison to the GEP model, which had R2, MAE, MSE, RMSE, and objective function values of 0.90, 4.02, 25.3, 5.03, and 0.08, respectively, the MEP model demonstrated greater accuracy with values of 0.96, 3.24, 16.4, 4.05, and 0.02 respectively. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to determine the effect of various parameters on the CS of MK-based GP mortar. The established models' equations may be exploited to assess the CS of MK-based GP composites with various input parameters, hence reducing the need for further laboratory testing. Implementing this approach not only improves the efficiency of material design but also encourages the sustainable utilization of locally accessible resources in the manufacturing of geopolymers.

The reduction in compressive strength (CS) of cementitious composites incorporating waste plastic is the main concern limiting its applicability in the building sector. Using industrial wastes as cement substitutes to enhance the CS of plastic mortar is a sustainable approach. This study used fine powdered waste materials such as silica fume (SF), marble powder (MP), and glass powder (GP) in plastic-based mortar for their effectiveness in enhancing CS. Plastic mortar specimens were cast using plastic waste in 5–25 % contents as sand replacement by mass, and their 28-day CS was recorded as a reference. SF, GP, and MP were utilized in plastic mortar mixtures separately in proportions of 5–25 %, with a 5 % increment, substituting cement by mass. These waste powders were also used in combinations of two (SF+GP, SF+MP, and GP+MP) and three (SF+GP+MP) in plastic mortar mixtures. Moreover, prediction models were built using the experimental database for the CS of plastic mortar. Gradient boosting and bagging ensemble machine learning (ML) techniques were chosen for model development. The decrease in CS was limited by substituting SF, GP, and MP for cement in plastic mortar. It was determined that the most effective substitution levels for SF, GP, and MP in plastic mortar, according to the strength enhancement, were 15, 10, and 15 wt.% of cement, respectively. The ML models closely matched experimental results, and in terms of R2 and error evaluations, bagging model outputs were more accurate than gradient boosting. The gradient boosting and bagging models had R2 of 0.89 and 0.94, respectively, with average absolute errors of 0.87 and 0.65 MPa.

This study aims to investigate the blends of supplementary cementitious ground granulated blast furnace slag (GGBFS) with limestone powder (LSP) as mineral admixtures to partially replace cement (up to 30 %) in self-consolidating paste (SCP) systems made at respective water demands. Besides the control formulation (100 % cement), the first set of SCP systems includes GGBFS alone as a partial substitute of cement (5 %, 10 %, 15 %, and 20 %), whereas the second set contains blends of 10 % GGBFS with 10 % and 20 % LSP. For all the seven formulations (1 control, 4 binary and 2 ternary), tests were performed to determine their water demand, setting times and flow behavior, hydration kinetics, mechanical strengths and microstructural properties. Results showed that the fresh and hardened state properties of SCP systems incorporating GGBFS and LSP led to an increase in strength, hydration kinetics were slightly improved and hence enhancement of strength and microstructure of SCP systems. Reduction in initial and final setting times recorded with increased LSP from 10 % to 20 % in ternary SCP formulations. Early compressive strength due to LSP increased in ternary SCP systems as compared to binary formulations, which reflected synergy between GGBFS and LSP in SCP systems. Moreover, environmental assessment results indicate a significant reduction in CO2 emissions by substituting cement with GGBFS and LSP. These findings indicate that both GGBFS and LSP can be successfully used in developing high performance and environment friendly self-compacting concrete (SCC).

Ordinary Portland cement (OPC) is proving to be hazardous to the environment. To replace the OPC, geopolymers (GPs) are introduced. However, to fully replace the OPC by GPs extensive laboratory tests are required to assess the long-term and short-term properties of GPs in different scenarios. Given the shortage of time for performing such extensive testing, artificial intelligence (AI) is used to analyze the properties of GPs. In this study, different AI techniques such as artificial neuro network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) are used to obtain the predictive models for estimating the compressive strength of fly ash and ground granulated blast furnace slag-based GP concrete. Different statistical parameters are used to evaluate the performance of predictive models. Similarly, sensitivity and parametric analysis are also conducted on the input parameters. Additionally, multiple linear regression was also performed on the whole database. After comparing all the results, it was concluded that GEP is the best AI technique to predict the compressive strength of GP-based concrete.

Sustainable development in the building industry can be achieved through the use of versatile cementitious composites. Thus, incorporating nanoparticles into cement composites can create materials with enhanced performance and numerous applications. The utilization of carbon nanotubes (CNTs) in the construction industry has great promise for developing efficient solutions to establish a sustainable ecosystem with diverse characteristics. However, forecasting the characteristics of these composites is a significant challenge due to their intricate composite structure and nonlinear behavior. Designing and conducting laboratory experiments on diverse samples and across multiple age groups is challenging, time-consuming, and costly. Moreover, there is presently a lack of a forecasting model that can predict concrete's compressive strength (fc') with nanoparticles. Three machine learning (ML) techniques, K-nearest neighbor (KNN), linear regression (LR), and artificial neural network (ANN), were used to predict the fc' of nanocomposites containing CNTs in this research. A thorough database consisting of 282 data entities for the CNTs-based concrete and the model's reliability was assessed using the R2 test and statistical error analysis. The ANN model had the most outstanding R2 value of 0.885, while the KNN and LR models had R2 values of 0.838 and 0.744, respectively. Moreover, RReliefF analysis is utilized to evaluate the primary components in predicting concrete outcomes. This research shows that the properties of CNT-based concrete composites are greatly affected by the water-to-binder ratio, followed by the proportions of cement and coarse aggregates. The ML algorithms exhibited superior generalization capabilities, suggesting their enhanced potential for accurate predictions of CNTs-based concrete properties.

Alkali-activated composites (AACs) have attracted considerable interest as a promising alternative to reduce CO2 emissions from Portland cement production and advance the decarbonisation of concrete construction. This study describes the data-driven predictive modelling to anticipate the compressive strength (CS) of AACs. Four different modelling techniques have been chosen to forecast the CS of AACs using the selected data set. The decision tree (DT), multi-layer perceptron (MLP), bagging regressor (BR), and AdaBoost regressor (AR) were employed to investigate the precision level of each model. When it comes to predicting the CS of AACs, the results show that the AR model performs better than the BR model, the MLP model, and the DT model by providing a higher value for the coefficient of determination, which is equal to 0.91, and a lower MAPE value, which is equal to 13.35%. However, the accuracy level of the BR model was very near to that of the AR model, with the R2 value suggesting a value of 0.90 and the MAPE value indicating a value of 14.43%. Moreover, the graphical user interface has also been developed for the strength prediction of alkali-activated composites, making it easy to get the required output from the selected inputs.

The optimized use of nanomaterials in concrete for developing resilient and eco-friendly infrastructure is vital for our built environment. Therefore, the current study aims to optimize the dosages and particle sizes of colloidal nano-silica in developing high-performance, sustainable cement mortar. For this purpose, compressive strength, water absorption, porosity, and microstructure tests were performed on mortars containing different dosages and particle sizes (NS80, NS250, NS250Al, NS500) of colloidal nano-silica. For comparison, tests were also performed on control mortars and mortars containing metakaolin and silica fume. The microstructure of cement pastes was examined through SEM-based BSE imaging with elemental mapping through EDS and FTIR analyses. Additionally, global warming potential (GWP) as well as embodied energy (EE) were also analyzed for all mortar mixtures. The results demonstrated that smaller nano-sized particles (NS500) at a lower dosage (3%) showed improved compressive strength and densified microstructure compared to all other mixes. However, larger nano-sized particles (NS80) at medium (5%) and high dosages (10%) had the highest impact on compressive strengths due to the formation of low and medium Ca-based C-S-H and C-A-S-H phases. Furthermore, the results of greater compressive strength of NS80 mortars under medium and high dosages were supported by their lowest water absorption and apparent porosity. When compared with the control and all other mixes, the mortar mixes containing medium and high doses of NS80 had lower embodied energy/MPa and CO2-eq intensity/MPa, indicating their significant contribution to producing green concrete with a lower environmental impact.

The present study used the techniques of gene expression programming (GEP) and multi-expression programming (MEP) to assess the compressive strength (CS) and flexural strength (FS) and develop predictive models of sustainable mortar modified with waste eggshell powder (WEP) and waste glass powder (WGP) as a replacement of cement. In order to get more insights into the impact and relation of raw components on the CS and FS of a developed sustainable mortar, a comprehensive study using the SHapley Additive exPlanations (SHAP) methodology was performed. When comparing the efficiency of both employed models, it was seen that the MEP model exhibited superior performance with an R2 value of 0.871 and 0.894 for CS and FS, as compared to the GEP model, which had an R2 value of 0.842 and 0.845 for CS and FS respectively.

High-performance concrete is an excellent choice for modern engineering structures due to its superior performance, low maintenance costs, and long service life. Therefore, this study was aimed to produce high-performance concrete by incorporating a high volume of basaltic volcanic ash along with metakaolin (MK), micro-silica (MS), and nano-silica (NS). An investigation was performed in regard to the mechanical and durability properties, autogenous and drying shrinkage, and microstructural and pore-structure properties. A control mix, binary mixes, and ternary mixes were examined. The binary mixes contained fine (VA) and ultra-fine (VAF) volcanic ashes, and the ternary mixes contained combinations of VA with MK, MS, and NS. Test results show that ternary mixes with 30% and 40% cement substitution demonstrated better strength development and higher resistance to chloride penetration and water absorption. All binary and ternary mixes displayed lower autogenous shrinkage, as well as lower or comparable drying shrinkage than the control mix. Moreover, the combination of VA with MK, MS, and NS up to 40% resulted in micro and pore structure refinement by forming high-density gels. The findings indicate that VA in combination with MK, MS, and NS, can effectively substitute cement at up to 40% for the production of sustainable high-performance concrete.

The incorporation of nanomaterials (NMs) in concrete will produce the utmost properties. Nevertheless, investigational work takes a great deal of time with efforts to measure the strength of concrete. Thus, the process of estimating the properties of cement paste can be accelerated by employing soft machine learning (SML) techniques. Individual machine learning approaches (MLAs) like random forest (RF), decision tree (DT), and support vector machine (SVM) were utilized. These individual MLAs models then ensemble with two approaches namely bagging and boosting. The outcome of the model depicts a robust performance by showing significant correlations (R2) as compared to individual MLAs. Data points with 205 having contribution factors (e.g., curing time (days), water to binder (w/b), micro-silica (%), and nano-silica (%) were used as input parameters, and compressive strength as output for MLAs modeling. To ensure that each model is as accurate as possible, cross-validation with K-folding and statistical error analysis (i.e., MAE, MAE, and RMSE) were utilized. Thus, interpretable approaches with AdaBoost give a strong correlation of R2 ranging from 0.85 to 0.90 respectively with fewer errors. Out of which DT with ensemble Adaboost and RF give a robust performance. Moreover, the results of the cross-validation show that the response was accurate, with fewer errors. Therefore, the ensemble model represents a robust correlation. Therefore, its usage with individual ML would enhance the model's performance. In addition, the graphical user interface (GUI) is created by training a model that can predict the values of the desired outputs when the necessary input parameters are provided. It simplifies the procedure and provides a handy tool for employing the model's capabilities in the civil engineering domain.

This research used gene expression programming (GEP) and multi expression programming (MEP) to determine the compressive strength (CS) of alkali-activated material (AAM) to compare and develop more reliable genetic algorithm-based prediction models. To learn more about how raw ingredients affect and interact with the CS of AAM, a SHapley Additive exPlanations (SHAP) analysis was conducted. A comprehensive dataset containing 676 points with fifteen influential parameters was formulated from the previously published literature. According to this study, considering the impact of 15 input variables, both genetic algorithms produced results close to the experimental CS (retrieved from the literature). When the performance of the GEP and MEP models were compared, it was found that the MEP model, with an R2 of 0.86, performed better than the GEP model, with an R2 of 0.82. The assessment of the statistical parameters of generated models revealed that the MEP model was more effective. Additionally, SHAP analysis revealed that slag content, followed by the specimen's age, sodium silicate, and curing temperature, showed a positive correlation with CS of AAM, which were the most important parameters. The results also revealed the importance of chemical contents, i.e., CaO, SiO2, Al2O3, of FA and slag on the CS of AAM. The built models might be used to compute the CS of AAMs with varying input parameter values, minimizing the effort, time, and cost of unnecessary lab tests. Furthermore, the outcomes of the SHAP study might help researchers and the industry determine the quantity or composition of raw ingredients when producing AAMs.

Alkali-activated materials (AAMs) have gained significant attention in recent years due to their potential for sustainable construction and outstanding strength and durability characteristics. However, their wide application is constrained by their weak behavior under flexure and tensile loading, leading to brittleness. This study examines the formulation of an alkali-activated fiber-reinforced mortar, using fly ash and hydrated lime as main precursors, and investigates the capacity when reinforced with polyvinyl alcohol (PVA) and polypropylene (PP) fibers. The performance was measured through experimental investigation in terms of flowability, setting time, compressive strength (CS), water absorption, flexure strength (FS), and load-displacement curve under flexure loading. The durability was assessed through acid and sulfate attack tests after 120 days. The findings showed that adding both types of fibers improved the mechanical and durability performance of the one-part AAM. The highest CS of 44.95 MPa and 42.04 MPa and FS of 7.8 MPa and 6.9 MPa were achieved for PVA and PP fibers at a 1.5% fiber content. The FS was increased by 18.94% and 28.2% for PP1.5 and PVA1.5 fibers respectively. The control sample, PP 1.5 and PVA 1.5 recorded 31.1%, 12.5%, and 11% loss in CS after the acid attack test, respectively, after 120 days. A reduction of 15.3% and 13.3% was recorded in flow diameter with the addition of PP and PVA fibers at 2%. Overall, mortars reinforced with PVA fibers have shown better flowability, mechanical strength, and chemical resistance than PP fibers.

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of a robust cementitious matrix with utmost properties. The strength of HPC when subjected to compression test is determined by the combination and quantity of the materials used in its production. Thus, making its mixed design process challenging and ambiguous. The objective of this research is to forecast the strength of HPC containing RHA, by using a diverse range of machine learning techniques, including both individual and ensemble learners such as bagging and boosting. This study will cause significant implications for sustainable construction practices by facilitating the development of an efficient and effective method for forecasting the strength of HPC. Individual machine learning (ML) algorithms are incorporated with ensemble methods such as bagging, adaptive boosting, and random forest algorithms. These ensemble techniques is use to create twenty different sub-models. Afterward, these sub-models is train and optimized for achieving the best possible value for R2. The sub-models were further fine-tuned to obtain the best value for R2. In order to assess or evaluate the quality, reliability, and generalizability of the test data, the K-Fold cross-validation method is utilized. Furthermore, the index for measuring the statistical performance of models is use to validate and compare the assessment of ensemble models with individual models. The findings indicate that using bagging and boosting techniques enhances the prediction accuracy of individual or weak models, with minimum errors and an R2 value > 0.92 is achieved using bagging with decision tree and random forest. In general, the performance of the model is optimized by using ensemble learner methods in machine learning (ML).

Despite its collapsible nature and weakness, desert sand can be used for construction purposes all over the world if properly stabilized. Therefore, the aim of this study is to evaluate the effectiveness of cement and fiber in stabilizing locally available dune sand. A test plan was used to investigate the effects of varying quantities of alkali resistance glass (ARG) fiber (F: 0%, 0.2%, 0.4%, and 0.6%) and portland cement (C: 0.0%, 1.0%, 3.0%, and 5.0%) on the mechanical and microstructural properties of dune sand. Mechanical properties such as unconfined strength (UCS), strain at failure (εf), California bearing ratio (CBR), and modulus of elasticity (Es) were evaluated, and microstructure properties were investigated using Raman spectrum and laser-scanning microscopy (LSM) tests on stabilized sand samples. The results of the experimental study showed that the percentage of cement in the treated sand has a more significant impact on the investigated properties of the treated sand than the percentage of fibers. In addition, increasing fiber content results in an increase in the ductility of the sand mix. Raman analysis revealed significant interactions between sand mix components. Moreover, LSM results showed that fiber–cement interaction increased with increasing cement percentage, as calcium silicate hydrates (CSH) formed in the mix and filamentous and intrastrand binding occurred. The findings of this study indicate that ARG fiber and cement can be effective in the stabilization of dune sand for construction purposes even with the use of low percentages of ARG fiber (0.2%–0.4%) and cement (3%).

Sustainable development of infrastructure requires the efficient and optimized utilization of nano-silica in concrete applications. Thus, current study aims to explore how the dosages of colloidal nano-silica (CNS) and its surface area affect the mechanical performance of cementitious mortar composites. To assess the compressive strength with aging (7, 28 and 56 days), CNS with different surface areas was used as cement substitutes at three different dosages levels. Additionally, to optimize nano-silica dosages and evaluate the outcomes of the experiment, statistical modelling based on Response Surface Methodology (RSM) and machine learning-based Artificial Neural Network (ANN) were applied. In comparison to the control and all other mixtures, the compressive strength was improved (51.5 MPa, 70.2 MPa, and 73.2 MPa) at 7, 28, and 56 days, respectively, by the CNS with higher surface area (CNS500) at their lower dosages (3%). However, at medium (5%) and high dosages (10%), CNS with a lower surface area (CNS80) resulted in better compressive strength (71.9 MPa, 87 MPa, and 90 MPa) at 7, 28, and 56 days, respectively, comparatively to control as well as all other mixtures. These findings were supported by both the RSM and ANN models, with the ANN model outperforming the RSM model in terms of performance. The ANN model was trained and validated using 70% and 30% of the data, respectively. For the validation data of ANN model, the Coefficient of Determination (R), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were revealed as 0.925, 2.263 and 2.974 MPa, respectively. Whereas, for the quadratic RSM models, these values were determined as 0.86, 4.485, and 5.45 MPa, respectively. The results of statistical models validated the experimental results, thus showing that the developed RSM and ANN can be used as reliable prediction model to optimize CNS use in producing stronger and more sustainable concrete.

Eco-friendly concrete is in great demand, and as a consequence, the necessity to find sustainable alternatives to ordinary cement has become critical. In recent years, there has been considerable interest in the potential benefits of utilizing rice husk ash (RHA) as a cement substitute in concrete. Evaluating concrete properties in a laboratory setup like compressive strength (CS) is a time-consuming and expensive process. The development of trustworthy and precise models for CS estimation might be a better strategy. To determine the CS of RHA concrete, this research used boosting ensemble algorithms, namely gradient boosting (GB), AdaBoost regressor (ABR), and extreme gradient boosting (XGB), over a vast literature database. The estimation models were validated using statistical measures, error distribution by plotting violin graphs, and k-fold analysis. Furthermore, SHapley Additive exPlanations (SHAP) analysis was utilized to assess the importance of contributing elements. The results showed that XGB had the greatest performance in estimating the CS of RHA concrete out of the three methods tested. While the GB and ABR models both had R2 values of 0.90 and 0.94, respectively, the XGB model achieved a value of 0.96. The violin graphs indicated that the average absolute error values for the GB, ABR, and XGB were 5.82, 4.65, and 3.53 MPa, implying the higher precision of the XGB model in estimating the CS of RHA concrete. The SHAP study demonstrated that the three most influential factors in increasing the strength were cement, specimen age, and rice husk ash. The construction sector may benefit from the application of such technologies by facilitating the development of quick and low-cost methods for identifying material qualities and the influence of input parameters.

This study used machine learning methods to predict the water absorption (W-A) of cement-based material (CBM) containing eggshell and glass powder as sand and cement substitutes. A dataset from the laboratory experiments consisting of 234 points and seven input variables was used to develop models, including multilayer perceptron neural network (MLPNN), support vector machine (SVM), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). Additionally, a SHapley Additive exPlanations (SHAP) analysis was performed to investigate the relevance and interaction of raw components. When evaluating the prediction models for the W-A of CBM, it was found that the MLPNN and SVM models were moderately accurate (R2 = 0.74 and 0.78, respectively), while the AdaBoost and XGBoost models showed good agreement with the lab test results (R2 = 0.86 and 0.91, respectively). The SHAP approach revealed that while the cement quantity had a higher negative association with W-A of CBM, the quantities of eggshell powder, sand, and glass powder showed both favourable and detrimental correlations. Therefore, eggshell and glass powder must be used in optimal proportions of around 60 kg/m3 and 80 kg/m3, respectively, for maximum resistance to W-A. The AdaBoost and XGBoost models can potentially compute the W-A of CBMs by utilising various input parameter values, which may help decrease unnecessary test trials in labs. Furthermore, the SHAP investigation revealed the impact and relationship of the inputs on the W-A of CBMs, which might potentially assist researchers and the industry in determining the appropriate amount of raw materials during CBM production.

This study aims to develop various machine learning (ML) models to investigate the self-healing capacity of engineered cementitious composites (ECC) and to evaluate the effect of input parameters (raw materials and crack width before the healing process) on output parameters (crack width after the healing process) via Shapley additive explanations (SHAP) analysis. The combination of the individual and ensemble ML models has been introduced to check and compare the accuracy level towards the prediction of crack width after the healing process to access the most suitable model for this application. The support vector machine (SVM) from the individual, while XGBoost (XGB) and random forest (RF) from ensemble ML models have been investigated for prediction purposes. As per the obtained results, the RF model outperforms both the SVM and XGB algorithms in forecasting the fracture width after the healing process for the selected ECC in concrete material. The performance indicator, such as the coefficient of determination (R2), was reported as 0.97 for RF, 0.96 for XGB, and 0.93 for the SVM model. Statistical results and k-fold cross-validation for the employed ML models also confirm their legitimacy. It was also noted from the result of the SHAP analysis that the significant contribution was for the crack width before healing towards the prediction of crack width after the healing process. Furthermore, the ML models can also be utilized to anticipate the healing ability of the other ECC, such as soda glass powder, marble powder, and bagasse ash.

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.

To investigate the effect of eggshell powder on the water-absorption capacity of cement mortar, this research employed experimental testing followed by machine learning (ML) modeling techniques. On cement mortar specimens, the eggshell powder was partially substituted for cement and sand in concentrations ranging from 0% to 15% with a 2.5% increment, and water absorption tests were conducted. Experimental tests were used to generate the dataset, which was then used to train ML algorithms and construct ML-based prediction models. The integration of eggshell powder lowered the water absorption capacity of cement mortar, as determined by the test findings. The highest reduction in water absorption was seen at 5% eggshell powder as a cement replacement, resulting in up to 41.7% decrease, and 7.5% eggshell powder as a sand replacement, resulting in up to 45.8% decrease in water absorption compared to the control sample. In addition, the constructed ML models exhibited good agreement with the actual results and may be utilized to determine the water absorption of cement mortar incorporating eggshell powder. Nevertheless, based on the R2 value, the disparity between experimental and predicted results, and error analysis, it was determined that the random forest model was more accurate than the decision tree model.

This study evaluates the compressive strength (C–S) of nano-silica-based fiber-reinforced concrete (NS-FRC) by using advanced machine learning (ML) individual and ensembled techniques. The employed advanced ML approaches used for the analysis are Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and eXtreme Gradient Boosting (XGB). Furthermore, the level of accuracy for the employed advanced algorithms is also evaluated by the k-fold cross-validation technique. Statistical checks, i.e., root mean square error (RMSE), mean absolute error (MAE) and mean absolute percent error (MAPE), are also applied to validate the performance of algorithms. Sensitivity analysis is also made to explore the influence of input parameters on the C–S of NS-FRC. Among all, the XGB technique is found most effective for an accurate C–S prediction of NS-FRC. In XGB model, the coefficient of determination (R2) is 0.95, which is comparatively more than that of SVM (0.90) and MLP (0.90). The MAE value of XGB algorithm is 3.3 MPa which is lower than that of SVM (4.8 MPa) and MLP (4.5 MPa). In addition, RMSE value is also less for XGB algorithm (3.8 MPa) as compared to that of SVM (5.5 MPa) and MLP (5.9 MPa). Furthermore, the employed XGB models exhibited highest R2 of 0.95 as compared to the models reported in the available literature. The sensitivity analysis revealed that the nano-silica influenced the C–S of NS-FRC by 7%. Moreover, discussion reveals that nano-silica in concrete can have several benefits, such as improved microstructure, enhanced strength, prolonged durability, reduced cement content, and less carbon emission.

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75–100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical.

In this research, test methods followed by supervised machine learning (SML) modeling were used to examine the effect of glass powder as a partial sand and cement substitute on the water absorption capability of cement mortar. This study also utilized SHapley Additive exPlanations (SHAP) analysis to determine the significance of raw materials. The dataset was derived from experiments and was used to build SML-based prediction models, including support vector machine, bagging, and AdaBoost. According to the test results, using glass powder reduced the water absorption capacity of cement mortar, and the optimum glass powder contents were noted to be 10 % as cement and 15 % as a sand substitute. Additionally, the built SML models indicated good accord with test outcomes and could be applied to calculate the water absorption of cement mortar comprising glass powder. Moreover, the AdaBoost model was found to be more accurate (R2 = 0.98) than the support vector machine (R2 = 0.95) and bagging regressor (R2 = 0.95) models in terms of R2, the difference between test and estimated findings, and the assessment of the errors.

To produce highly effective electrically conductive cementitious composites and advance non-destructive structural-health monitoring techniques, graphite-based nanomaterials (GrNs) are viable conductive fillers. A comprehensive database was created for CS and ER pertinent to GrNCC’s experimental studies from open sources. It is a challenging multi objective optimization problem (MOOP) due to the requirement of both mechanical strength and electrical resistivity for the design of Graphene nanoparticles-reinforced cementitious composites (GrNCC). The current study provides an ample data-driven approach employing machine learning (ML) techniques and a non-dominated sorting genetic algorithm (NSGA-II) to address this multi objective design optimization (MODO) problem for GrNCC. ML theories quantify how important parameters affect GrNCC’s qualities. First, using processed experimental datasets, gene expression programming (GEP) is used to develop prediction models for GrNCC’s uniaxial compressive strength (CS) and electrical resistivity (ER). The findings demonstrate its superior predictive accuracy, as evidenced by high R2 of 0.92 and 0.91 and low mean absolute error (MAE) scores of 1.57 MPa, and 60.46 kΩ.cm for CS and ER, respectively. For the MODO technique, parameter analysis was performed, which aids in identifying the parameters that need to be optimized and defining their constraints. Finally, NSGA-II is used as the foundation for the MODO technique. With the proposed prediction models as objective functions, it simultaneously optimizes the CS and ER of the GrNCC. It successfully produces a collection of Pareto solutions that make it easier to choose the right parameters for the GrNCC design. The proposed MODO technique can be efficiently applied to multiobjective design and optimization of electrical and mechanical properties of GrNCC.

Sustainable development might be promoted if waste eggshells are used in cement-based materials (CBMs) by decreasing waste disposal problems, CO2 emissions, and material costs. In this regard, experimental and machine learning (ML) strategies were used to assess the flexural strength (FS) of CBMs comprising waste eggshell powder (ESP). Initially, experimental methods were carried out to evaluate the FS of ESP-based CBMs. The acquired data was utilized to predict the FS by applying ML algorithms like a decision tree and an AdaBoost regressor. The efficacy of ML techniques was measured by comparing experimental and predicted FS and applying statistical checks and k-fold evaluations. The experiment outcomes demonstrated that the FS of CBMs was improved by the inclusion of ESP at lower replacement ratios. Additionally, based on the assessment of modeling results, the AdaBoost regressor model demonstrated a higher exactness in estimating the FS of CBMs containing ESP compared to the decision tree model. The assessment of error readings also indicated the higher precision of the AdaBoost regressor model than the decision tree. According to the feature importance analysis, the fine aggregate was the component with the lowest impact for FS, whereas cement had the prime impact. The findings showed that as opposed to using ESP as a cement replacement, using ESP as an FA replacement to keep FA in less quantity and cement in larger quantity would result in enhanced strength. The construction industry can gain from ML techniques since they allow for more time- and cost-effective approaches to assessing material attributes.

The intention of this study was to examine the use of the most prevalent industrial byproduct, silica fume, as a supplementary cementitious material (SCM). Along with the typical review, this study used the scientometric review technique to statistically examine the various features of the existing literature. A scientometric-based study can deal with the massive literature data using the applicable software tool. The bibliometric data on silica fume in concrete was gathered using the Scopus search engine between 2001 and July 2022. The top publication sources, co-occurrence of keywords, highly cited authors, and active contributing countries were all examined using scientometric analysis. Additionally, the properties of composites using silica fume as the SCM were discussed. The effect of silica fume addition on compressive, split-tensile, and flexural strength, as well as the durability and microstructure of cementitious composites, were described. It was concluded that the inclusion of silica fume as an SCM improved the material's properties while also providing environmental benefits. However, there is a limit beyond which silica fume addition showed an unfavorable effect on the performance of cementitious composites. The optimum proportion of silica fume in concrete was found to be in the range of 15–25 % by mass of cement. Moreover, literature data were utilized to develop prediction models for the strengths of cementitious composites containing silica fume, and the prediction models agreed with the actual results. These prediction models could be used to test the material strength, saving both time and cost.

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.

New concepts for improving the performance of cementitious materials have recently surfaced due to the advancement in nanotechnology. In this context, nano silica (NS) and carbon nanotubes (CNTs) have been widely used in recent years. These nanomaterials significantly enhance the mechanical and durable performance of cement mixtures, although there are a few drawbacks related to high cost and workability issues. As a result, these components must be consumed at specific rates in order to achieve the desired qualities. This study aimed to create models to forecast the compressive strength (CS) for concrete mixed with two types of nanomaterials utilizing machine-learning-algorithms (MLA), such as the decision tree algorithm (DTA) and random forest algorithm (RFA). The results of both models were compared and verified by external K-fold cross-validation. A comprehensive database was collected containing 72 and 55 data points for the CS of CNTs and NS-modified concrete respectively. Four input variables such as fine aggregate (FA), cement content (CC), coarse aggregate (CA) and water-to-cement ratio (W/C) were used for the calibration of the models. Additionally, predicted results were checked through k-fold-cross-validation and other performance gauges such as mean-absolute-error (MAE), mean-squared-error (MSE), correlation coefficient (R2), root-mean-square-error (RMSE), relative-root-mean-square-error (RRMSE) and performance-index-factor (Pif). The RFA (CNTs and NS) models were found with better performance and accuracy than the DTA models by having the lowest MAE of 3.51, 4.17 RRMSE of 0.0783, 0.0584, and Pif of 0.0398 and 0.0299 respectively. In addition, the value of R2 for RFA models was observed higher such as 0.90 (CNTs) and 0.93 (NS), while for DTA models R2 was found as 0.88 (CNTs) and 0.86 (NS) respectively. The ensembled ML methods demonstrated a better generalization capability, which indicates their better ability for future prediction of CNTs and NS mixed concrete.

Abstract In this study, researchers examined the effect of replacing a high-volume of cement with sugarcane bagasse ash (BA) and silica fume (SF). In addition to the control, three binary and three ternary blends of concrete containing different percentages of cement/BA and cement/BA/SF were tested to determine the various mechanical and microstructural properties of concrete. For each mix, eighteen cylindrical concrete specimens were cast followed by standard curing (moist at 20 °C) to test the compressive and tensile strengths of three identical specimens at 7, 28, and 91 days. The test results indicated that the binary mix with 20% BA and ternary mix with 33% BA and 7% SF exhibited higher strengths than all the other mixes, including the control. The higher strengths of these mixes are also validated by their lower water absorption and apparent porosity than the other mixes. Following mechanical testing, the micro and pore structures of all mixes were investigated by performing scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM–EDS), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and nitrogen (N2) adsorption isotherm analysis. In SEM–EDS analysis, a dense and compact microstructure was observed for the BA20 and BA33SF7 mixtures due to the formation of high-density C–S–H and C–H phases. The formation of a large amount of C–S–H phases was observed through FTIR, where a prominent shift in peaks from 955 to 970 cm−1 was observed in the spectra of these mixes. Moreover, in N2 adsorption isotherm analysis, a decrease in the intruded pore volume and an increase in the BET surface area of the paste matrix indicate the densification of the pore structure of these mixes. As observed through TGA, a reduction in the amount of the portlandite phase in these mixes leads to the formation of their more densified micro and pore structures. The current findings indicate that BA (20%) and its blend with SF (40%) represents a potential revenue stream for the development of sustainable and high-performance concretes in the future.

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.

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches' results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

In the construction industry, pursuing sustainable development by using sustainable materials necessitates using renewable resources. Among different renewable materials, mineral-derived natural fibers are relatively cheaper and abundantly available in various countries. This study summarizes the research advancements on concrete reinforced with mineral-derived natural fibers. This review on the incorporation of mineral fibers in concrete evaluates, identifies, and synthesizes research outcomes for creating a summary of current evidence which can contribute to evidence-based practice. Mapping knowledge, c/o-occurrence, and co-citation are hard gears for innovative research. Accordingly, the present study is aimed at exploring the literature on key features of mineral fiber-reinforced concrete by performing a scientometric analysis. The current study implemented an advanced approach for mining, processing, and analyzing data, interpretation, and presentation of available bibliographic data on mineral fibers in concrete. Furthermore, the discussion on the applications and limitations of using mineral fiber-reinforced concrete in the construction industry is also made. The current research may aid academics in exchanging new ideas and techniques and developing collective efforts.

It is evident that preparing materials, casting samples, curing, and testing all need time and money. The construction sector will benefit if these problems can be handled using cutting-edge techniques like machine learning. Also, a material’s ultrasonic pulse velocity (UPV) is affected by various variables, and it is difficult to study their combined effect experimentally. This research used machine learning to assess the UPV and SHapley Additive ExPlanations techniques to study the impact of input parameters of hybrid fiber-reinforced concrete modified with nano-silica (HFRNSC). Three ML algorithms were employed, i.e., gradient boosting regressor, adaptive boosting regressor, and extreme gradient boosting, for ultrasonic pulse velocity evaluation. The accuracy of machine learning models was measured via the coefficient of determination (R2), k-fold analysis, statistical tests, and comparing the predicted and actual ultrasonic pulse velocity. This study determined that the gradient boosting and adaptive boosting models had a good level of accuracy for ultrasonic pulse velocity, but the extreme gradient boosting method estimated the ultrasonic pulse velocity of HFRNSCs with a greater degree of precision. Also, from the statistical checks and k-fold approach, it was discovered that the extreme gradient boosting method is more exact in estimating the ultrasonic pulse velocity of HFRNSCs. The SHapley Additive ExPlanations analysis revealed that the age of the specimen and nano-silica had a greater positive impact on the ultrasonic pulse velocity of HFRNSCs, whereas the coarse aggregate to fine aggregate ratio had a negative impact. In addition, fiber volume was found to have both positive and negative effects. By aiding the development of rapid and low-cost methods for determining material properties and the influence of input parameters, the construction industry may profit from the use of such technologies.

A progressive advance in the construction sector is attained by employing ultra-high performance concrete (UHPC) technology. Rigorous efforts have been made in this research domain to have remarkable quality levels with 150 MPa or more strength and significant durability, which was impossible previously. Steel fiber incorporation in UHPC is vital in improving its mechanical characteristics. This review on the incorporation of steel fibers in UHPC evaluates, identifies, and synthesizes research outcomes for creating a summary of current evidence that can contribute to evidence-based practice. This study summarized a review of the literature on steel fibers’ effect on UHPC, intending to explore its essential aspects. The aim is to summarize the literature in this research domain and provide guidance for future research. Moreover, the basic requirements and materials, mixing and casting, mechanical properties, modern applications, advantages and disadvantages, and future perspectives associated with steel fibers reinforced UHPC in the construction sector are discussed. It is revealed from the conducted analysis that the most widely applied keyword is “steel fibers.” Due to the graphical illustration of the contributing studies, the current work may benefit academic scholars in sharing novel techniques and ideas and establishing collaborative efforts. Furthermore, the present work reveals that steel fibers have the potential to enhance the mechanical properties of UHPC; however, the large-scale production and applications of steel fiber-reinforced UHPC are controlled by parameters like fiber content and geometry.

The worldwide enhancement in the accumulation of waste tires and associated upcoming environmental and social concerns are becoming the primary intimidations. Waste tires are considered an undervalued resource globally, and their sheer volume is used to be disposed of in landfills, a primary concern. Instead of disposing of waste materials after recycling, the utilization can have a considerable impact. The trend of extracting steel fibers from waste tires is emerging nowadays. Incorporating waste recycled tire steel (WRTS) fibers, obtained from waste tires in concrete is a potential profit-gaining engineering application. But there are still some challenges, like WRTS fibers' suitable volumetric content and length selection, which need to be addressed by having in-depth exploration. Research has been conducted on incorporating reused waste materials in cementitious composites. Concrete ingredients are meant to provide strength, but adding reused waste materials to concrete helps reduce environmental pollution and make it sustainable and economical. In previous literature, the impact of such waste fibers on the key properties of concrete, such as ultrasonic pulse velocity, compressive, splitting-tensile and flexural strengths, energy absorption, modulus of elasticity, toughness, and ductility, were investigated. Adding WRTS fibers to concrete can enhance its compressive strength by more than 10 % and flexural strength by more than 50 %. This paper aims to review the application of WRTS fiber-reinforced concrete to identify the research gap for researchers in this direction. The available research outcomes and the recycling process of WRTS fibers are summarized and discussed. It is concluded that incorporating WRTS fibers in concrete would result in sustainable development by providing economical and environment-friendly construction materials with improved mechanical properties.

Concrete structures frequently experience the phenomena of crack development. The researchers used certain healing agents to boost the frequently observed autogenous crack-healing capacity of concrete. Although various artificial intelligence (AI) techniques have been used to forecast a number of concrete properties, the application of AI to forecast self-healing capacity of engineering cementitious composites (ECC) is relatively rare. For this purpose, three gene expression programming (GEP) models were created to predict the potential of admixture-based concrete to self-heal. A total of 619 data points were extracted from the literature with four contributing factors i.e., amount of fly ash (FA), silica fume (SF), limestone powder (LP), and crack width before self-healing (CWB) in order to forecast the crack width after self-healing (CWA). The data points were divided into training (70%) and testing (30%) sets. Various performance indicators were employed to assess the efficacy of the derived GEP models, which includes coefficient-of-correlation (R), relative-root-mean-square-error (RRMSE), root-mean-squared-error (RMSE), root-mean-squared-logarithmic-error (RMSLE), Nash-Sutcliff efficiency (NSE), root-squared-error (RSE), mean-absolute-error (MAE), performance-index (PI), and objective-function (OBF). The best optimized GEP model (SHC-GEP 1), was found with R = 0.938, NSE = 0.944, RMSE = 2.799 µm, MAE = 3.72 µm, RMSLE = 0.006 µm, RSE = 0.124 µm, and RRMSE = 0.439 µm, in the testing phase. While the OBF was less than 0.2 (i.e., 0.119), indicating that the model is free from overfitting issue. In contrary, the conventional linear regression model fails to meet this criterion and gives many negative predicted values. Moreover, the sensitivity analysis results indicate that the CWB has the greatest impact on the CWA. Also, the parametric trends between input variables and CWB, are in-line with the previous literature, indicating the robustness of the established model. Additionally, the suggested GEP model may be used as a cutting-edge alternative for forecasting the final crack width after concrete self-heals, helping engineers to assess the crack-reduction capability. Furthermore, it is recommended to explore the crack healing capabilities of other supplementary cementitious material like bagasse ash, wheat straw ash, and soda glass powder, subjected to modeling their healing ability using AI techniques.

This study evaluated the compressive strength (CS) of cement-based materials (CBMs) incorporating eggshell powder (ESP) as a partial substitute for cement and fine aggregate using both experimental and machine learning (ML)-based strategies. Initially, the CS of CBMs based on ESP was measured experimentally. ML techniques, including support vector machine and bagging regressor, were applied for CS estimation. Comparing the coefficient of determination (R2), statistical checks, k-fold evaluation, and measuring the difference between experimental and projected CS were used to evaluate the performance of ML models. According to the results of the experiment, the addition of ESP increased the CS of CBMs when used in lower replacement ratios. In addition, the support vector machine model had a reasonable degree of precision, while the bagging regressor model predicted the CS of ESP-based CBMs with a higher degree of precision. Incorporating waste eggshells into building materials will promote sustainable development by minimizing environmental problems connected with eggshell disposal, conserving natural resources, offering cost-effective materials, and decreasing CO2 emissions. In addition, the use of ML approaches will benefit the construction sector by facilitating efficient and economical ways of analyzing the properties of materials.

This study performed a scientometric evaluation of the bibliometric data on self-healing concrete (SHC) in order to determine its major components. Manual review articles are inadequate in their capability to connect diverse areas of the literature in a systematic and ordered manner. Current research's most difficult parts include scientific mapping, co-occurrence, and co-citation. The Scopus search engine was utilized to locate and collect the necessary information for the investigation. During the data review, the important publishing sources, assessment of keywords, prolific authors based on citations and publications, most-cited articles, and areas actively engaged in SHC research are identified. VOSviewer was used to examine the literature records from 1294 relevant publications, which comprised abstract, citation, bibliographic, funding, keyword, and other data. In addition, the limitations associated with the use of SHC in the building industry were investigated, as well as potential solutions to these limitations. Due to the quantitative and graphical description of participating nations and researchers, this study can aid academics in developing joint projects and disseminating new ideas and approaches.

Due to the increasing prices of cement and its harmful effect on the environment, the use of cement has become highly unsustainable in concrete. There is a considerable need for promoting the use of cement replacement materials. This study investigates the effect of variable percentages of metakaolin (MK) on the mechanical and durability performance of concrete. Kaolin clay (KC) was used in the current research to prepare the MK by the calcination process; it was ground in a ball mill to its maximum achievable fineness value of 2550 m2/Kg. Four replacement levels of MK, i.e., 5%, 10%, 15%, and 20% by weight of cement, in addition to control samples, at a constant water-to-cement (w/c) ratio of 0.55 were used. For evaluating the mechanical and durability performance, 27 cubes (6 in. × 6 in. × 6 in.) and 6 cylinders (3.875 in. diameter, 2 in. height) were cast for each mix. These samples were tested for compressive strength under standard conditions and in an acidic environment, in addition to being subjected to water permeability, sorptivity, and water absorption tests. Chemical analysis revealed that MK could be used as pozzolana as per the American Society for Testing and Materials (ASTM C 618:2003). The results demonstrated an increased compressive strength of concrete owing to an increased percentage of MK in the mix with aging. In particular, the concrete having 20% MK after curing under standard conditions exhibited 33.43% higher compressive strength at 90 days as compared to similarly aged control concrete. However, with increasing MK, the workability of concrete decreased drastically. After being subjected to an acid attack (immersing concrete cubes in 2% sulfuric acid solution), the samples exhibited a significant decrease in compressive strength at 90 days in comparison to those without acid attack at the same age. The density of acid attack increased with increasing MK with a maximum corresponding to 5% MK concrete. The current findings suggest that the local MK has the potential to produce good-quality concrete in a normal environment.

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study’s aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R2, RMSE, and a comparison of regression slopes. It was observed that the GEP model with 100 chromosomes, a head size of 10, and five genes resulted in an optimum GEP model, as apparent from its high R2 value of 0.80 and 0.70 in the TR and TS phase, respectively. However, the ANN model performed better than the GEP model, as evident from its higher R2 value of 0.94 and 0.88 in the TR and TS phase, respectively. Similarly, lower values of RMSE and MAE were observed for the ANN model in comparison to the GEP model. The regression slope analysis revealed that the predicted values obtained from the ANN model were in good agreement with the experimental values, as shown by its higher R2 value (0.89) compared with that of the GEP model (R2 = 0.80). Subsequently, parametric analysis of the ANN model revealed that the addition of pozzolanic materials enhanced the compressive strength of the ternary blended concrete samples. Additionally, we observed that the compressive strength of the ternary blended concrete samples increased rapidly within the first 28 days of casting.

In recent decades, a variety of organizational sectors have demanded and researched green structural materials. Concrete is the most extensively used manmade material. Given the adverse environmental effect of cement manufacturing, research has focused on minimizing environmental impact and cement-based product costs. Metakaolin (MK) as an additive or partial cement replacement is a key subject of concrete research. Developing predictive machine learning (ML) models is crucial as environmental challenges rise. Since cement-based materials have few ML approaches, it is important to develop strategies to enhance their mechanical properties. This article analyses ML techniques for forecasting MK concrete compressive strength (fc’). Three different individual and ensemble ML predictive models are presented in detail, namely decision tree (DT), multilayer perceptron neural network (MLPNN), and random forest (RF), along with the most effective factors, allowing for efficient investigation and prediction of the fc’ of MK concrete. The authors used a database of MK concrete mechanical features for model generalization, a key aspect of any prediction or simulation effort. The database includes 551 data points with relevant model parameters for computing MK concrete’s fc’. The database contains cement, metakaolin, coarse and fine aggregate, water, silica fume, superplasticizer, and age, which affect concrete’s fc’ but were seldom considered critical input characteristics in the past. Finally, the performance of the models is assessed to pick and deploy the best predicted model for MK concrete mechanical characteristics. K-fold cross validation was employed to avoid overfitting issues of the models. Additionally, ML approaches were utilized to combine SHapley Additive exPlanations (SHAP) data to better understand the MK mix design non-linear behaviour and how each input parameter’s weighting influences the total contribution. Results depict that DT AdaBoost and modified bagging are the best ML algorithms for predicting MK concrete fc’ with R2 = 0.92. Moreover, according to SHAP analysis, age impacts MK concrete fc’ the most, followed by coarse aggregate and superplasticizer. Silica fume affects MK concrete’s fc’ least. ML algorithms estimate MK concrete’s mechanical characteristics to promote sustainability.

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.

The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods—namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models—based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP.

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 nth 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.

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R2), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique’s higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC’s compressive and flexural strengths, respectively.