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.

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.

The experimental design of high‐strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python‐based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.

The emission of CO₂ and energy requirement in the production of Ordinary Portland Cement (OPC) causes the continuous depletion of ozone layer and global warming. The introduction of geopolymer concrete (GPC) technology in the construction industry leads to sustainable development and cleaner environment by reducing environmental pollution. In this article, constituents of GPC and their influence on properties of GPC has been reviewed critically. Fresh and hardened properties of GPC as well as the factors influencing these properties are discussed in detail. Flow charts have been proposed to show which factors have higher/lower impact on the fresh and hardened properties of GPC. A comprehensive review on the mix design of GPC, nanomaterial-based GPC, 3D printing using GPC, reinforced GPC and Global warming potential (GWP) assessment was conducted. Finally, the practical applications of GPC in the construction industry are provided.