Supervised machine learning and its algorithm is an emerging trend for the prediction of mechanical properties of concrete. This study uses an ensemble random forest (RF) and gene expression programming (GEP) algorithm for the compressive strength prediction of high strength concrete. The parameters include cement content, coarse aggregate to fine aggregate ratio, water, and superplasticizer. Moreover, statistical analyses like MAE, RSE, and RRMSE are used to evaluate the performance of models. The RF ensemble model outbursts in performance as it uses a weak base learner decision tree and gives an adamant determination of coefficient R² = 0.96 with fewer errors. The GEP algorithm depicts a good response in between actual values and prediction values with an empirical relation. An external statistical check is also applied on RF and GEP models to validate the variables with data points. Artificial neural networks (ANNs) and decision tree (DT) are also used on a given data sample and comparison is made with the aforementioned models. Permutation features using python are done on the variables to give an influential parameter. The machine learning algorithm reveals a strong correlation between targets and predicts with less statistical measures showing the accuracy of the entire model.

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R² = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R², MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R²), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R² equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R² of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R²), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.

To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R²), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R²) value of 0.95 as compared to ANN, which gave a value of R² equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response.

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA‐based geopolymer concrete (FGPC). To avoid time‐consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature (T), curing time (t), age of the specimen (A), the molarity of NaOH solution (M), percent SiO₂ solids to water ratio (% S/W) in sodium silicate (Na₂SiO₃) solution, percent volume of total aggregate ( % A_G), fine aggregate to the total aggregate ratio (F/A_G), sodium oxide (Na₂O) to water ratio (N/W) in Na₂SiO₃ solution, alkali or activator to the FA ratio (A_L/F_A), Na₂SiO₃ to NaOH ratio (N_s/N_o), percent plasticizer (% P), and extra water added as percent FA (E_W%). RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

Compressive strength is one of the important property of concrete and depends on many factors. Most of the concrete compressive strength predictive models mainly rely on available literature data, which are too simple to consider all the contributing factors. This study adopted a new approach to predict the compressive strength of sugarcane bagasse ash concrete (SCBAC). A vast amount of data from the literature study and fifteen laboratory tested concrete samples with different dosage of bagasse ash, were respectively used to calibrate and validate the models. The novel Gene Expression Programming, Multiple Linear Regression and Multiple Non-Linear Regression were used to model SCBAC compressive strength. The water cement ratio, bagasse ash percent replacement, quantity of fine and coarse aggregate and cement content were used as an input for models development. Various statistical indicators, i.e., NSE, R² and RMSE were used to assess the performance of the models. The results indicated a strong correlation between observed and predicted values with NSE and R² both above 0.8 during calibration and validation for the Gene Expression Programming (GEP). The outcomes from GEP outclassed all the models to predict SCBAC compressive strength. The validity of the model is further verified using data of fifteen tests conducted in the laboratory. Moreover, the cement content in the mix was revealed as the most sensitive parameter followed by water cement ratio form sensitivity analysis. The GEP fulfilled all the criteria for external validity. The simple formulae derived in this study could be used reliably for the prediction of SCBAC compressive strength.