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Genetic programming (GP) is presented as a new tool for the unconfined compression strength of expansive soils ('UCS-ES') by utilizing an experimental database with 195 datapoints. The five input parameters employed in formulation of gene expression programming (GEP) and multi gene expression programming (MEP) models are clay fraction (CF), liquid limit (LL), plasticity index (PI), specific gravity (Gs), maximum dry density (ρdmax), optimum moisture content (OMC), and swell percent (Sp). Simplified mathematical expressions were derived for both the GEP and MEP models to compute the UCS-ES. Various statistics, i.e., mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (R), scatter plots, and the sensitivity and parametric study, were used to evaluate the performance of these models. The results revealed that the UCS-ES are accurately determined by both the approaches, with the GEP model producing relatively superior performance (R2TrD = 0.806, R2TsD = 0.668). Furthermore, when the formulated models were compared with other AI models, it was found that they performed more efficiently. Hence, both the GEP and MEP models can reliably be deployed for determining the UCS-ES which reduces the time-consuming and laborious testing, thereby attaining sustainability in countering the problematic water-sensitive soils. [ABSTRACT FROM AUTHOR]

Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expression programming (GEP) and multi expression programming (MEP) are employed for forecasting the compressive strength of FGC. The comprehensive database is constructed comprising of 311 compressive strength results. The obtained equations relate the compressive strength of FGC with eight most effective parameters i.e., curing regime (T), time for curing (t) in hours, age of samples (A) in days, percentage of total aggregate by volume (% Ag), molarity of sodium hydroxide (NaOH) solution (M), silica (SiO2) solids percentage in sodium silicate (Na2SiO3) solution (%S), superplasticizer (%P) and extra water (%EW) as percent FA. The accurateness and predictive capacity of both GEP and MEP model is assessed via statistical checks, external validation criteria suggested by different researcher and then compared with linear regression (LR) and non-linear regression (NLR) models. In comparison with MEP equation, the GEP equation has lesser statistical error and higher correlation coefficient. Also, the GEP equation is short and it would be easy to use in the field. So, the GEP model is further utilized for sensitivity and parametric study. This research will increase the re-usage of hazardous FA in the development of green concrete that would leads to environmental safety and monetarist reliefs.

Annually, the thermal coal industries produce billion tons of fly-ash (FA) as a waste by-product. Which has been proficiently used for the manufacture of FA based geopolymer concrete (FGC). To accelerate the usage of FA in building industry, an innovative machine learning techniques namely gene expression programming (GEP) and multi expression programming (MEP) are employed for forecasting the compressive strength of FGC. The comprehensive database is constructed comprising of 311 compressive strength results. The obtained equations relate the compressive strength of FGC with eight most effective parameters i.e., curing regime (T), time for curing (t) in hours, age of samples (A) in days, percentage of total aggregate by volume (% Ag), molarity of sodium hydroxide (NaOH) solution (M), silica (SiO2) solids percentage in sodium silicate (Na2SiO3) solution (%S), superplasticizer (%P) and extra water (%E W) as percent FA. The accurateness and predictive capacity of both GEP and MEP model is assessed via statistical checks, external validation criteria suggested by different researcher and then compared with linear regression (LR) and non-linear regression (NLR) models. In comparison with MEP equation, the GEP equation has lesser statistical error and higher correlation coefficient. Also, the GEP equation is short and it would be easy to use in the field. So, the GEP model is further utilized for sensitivity and parametric study. This research will increase the re-usage of hazardous FA in the development of green concrete that would leads to environmental safety and monetarist reliefs. [ABSTRACT FROM AUTHOR]

As a clean and sustainable method, the microbially induced calcite precipitation (MICP) approach has been widely used for reinforcing weak soils. This study presents a state-of-the-art review on the unconfined compressive strength (UCS) of bio-cemented sand treated by MICP, followed by the high-performance prediction using a machine learning algorithm combined with the Monte-Carlo (MC) method. First, various influencing parameters affecting the UCS of bio-cemented sand are identified, such as initial relative density, angularity of particle shape, bacterial concentration, precipitated calcium carbonate content, temperature and degree of saturation. Besides, the particle size distribution, urea and calcium concentration, and initial pH level also influence the UCS of the bio-cemented sand, but the effects remain contradictory or unclear. Following the state-of-the-art review, a large database covering 351 bio-cemented sand samples is developed, with the UCS as the output and seven influencing parameters (median grain size, coefficient of uniformity, initial void ratio, optical density of bacterial suspension, urea concentration, calcium concentration and precipitated calcium carbonate content) as inputs for the correlation. The multi expression programming (MEP) method combined with the MC method is proposed to develop the prediction models. All data groups randomly generated from the database are with 80% of the samples as the training sets and 20% as the testing sets. Finally, the optimal prediction model is selected with the lowest mean absolute error, further based on the analyses of monotonicity, sensitivity and robustness regarding more general applications. [Display omitted] • State-of-the-art review is conducted for the UCS of the MICP-treated sand. • A large database covering the UCS of 351 MICP-treated sand samples is developed. • The UCS of the MICP-treated sand is predicted by MEP-MC-based models. • The optimum prediction model is determined by further validation analysis. [ABSTRACT FROM AUTHOR]