Development of GP and GEP models to estimate an environmental issue induced by blasting operation

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, Dmax of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The R2 and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water−binder ratio, water−cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water−cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > fce (2.35%) > fct (1.35%).

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

Stabilized aggregate bases are vital for the long-term service life of pavements. Their stiffness is comparatively higher; therefore, the inclusion of stabilized materials in the construction of bases prevents the cracking of the asphalt layer. The effect of wet−dry cycles (WDCs) on the resilient modulus (Mr) of subgrade materials stabilized with CaO and cementitious materials, modelled using artificial neural network (ANN) and gene expression programming (GEP) has been studied here. For this purpose, a number of wet−dry cycles (WDC), calcium oxide to SAF (silica, alumina, and ferric oxide compounds in the cementitious materials) ratio (CSAFRs), ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ3), and deviator stress (σ4) were considered input variables, and Mr was treated as the target variable. Different ANN and GEP prediction models were developed, validated, and tested using 30% of the experimental data. Additionally, they were evaluated using statistical indices, such as the slope of the regression line between experimental and predicted results and the relative error analysis. The slope of the regression line for the ANN and GEP models was observed as (0.96, 0.99, and 0.94) and (0.72, 0.72, and 0.76) for the training, validation, and test data, respectively. The parametric analysis of the ANN and GEP models showed that Mr increased with the DMR, σ3, and σ4. An increase in the number of WDCs reduced the Mr value. The sensitivity analysis showed the sequences of importance as: DMR > CSAFR > WDC > σ4 > σ3, (ANN model) and DMR > WDC > CSAFR > σ4 > σ3 (GEP model). Both the ANN and GEP models reflected close agreement between experimental and predicted results; however, the ANN model depicted superior accuracy in predicting the Mr value.

Multi Expression Programming Model for Strength Prediction of Fly-Ash-Treated Alkali-Contaminated Soils

Rapid industrialization is leading to the pollution of underground natural soil by alkali concentration which may cause problems for the existing expansive soil in the form of producing expanding lattices. This research investigates the effect of stabilizing alkali-contaminated soil by using fly ash. The influence of alkali concentration (2 N and 4 N) and curing period (up to 28 days) on the unconfined compressive strength (UCS) of fly ash (FA)-treated (10%, 15%, and 20%) alkali-contaminated kaolin and black cotton (BC) soils was investigated. The effect of incorporating different dosages of FA (10%, 15%, and 20%) on the UCS_kaolin and UCS_BC soils was also studied. Sufficient laboratory test data comprising 384 data points were collected, and multi expression programming (MEP) was used to create tree-based models for yielding simple prediction equations to compute the UCS_kaolin and UCS_BC soils. The experimental results reflected that alkali contamination resulted in reduced UCS (36% and 46%, respectively) for the kaolin and BC soil, whereas the addition of FA resulted in a linear rise in the UCS. The optimal dosage was found to be 20%, and the increase in UCS may be attributed to the alkali-induced pozzolanic reaction and subsequent gain of the UCS due to the formation of calcium-based hydration compounds (with FA addition). Furthermore, the developed models showed reliable performance in the training and validation stages in terms of regression slopes, R, MAE, RMSE, and RSE indices. Models were also validated using parametric and sensitivity analysis which yielded comparable variation while the contribution of each input was consistent with the available literature.

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result, determining the bond strength (BS) qualities of FRP rebars is critical. In this work, BS data for FRP rebars was investigated, utilizing non-linear capabilities of gene expression programming (GEP) on 273 samples. The BS of FRP and concrete was considered a function of bar surface (Bs), bar diameter (d_b), concrete compressive strength (f_c′), concrete-cover-bar-diameter ratio (c/d), and embedment-length-bar-diameter ratio (l/d). The investigation of the variable number of genetic parameters such as number of chromosomes, head size, and number of genes was undertaken such that 11 different models (M1–M11) were created. The results of accuracy evaluation parameters, namely coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) imply that the M11 model outperforms other created models for the training and testing stages, with values of (0.925, 0.751, 1.08) and (0.9285, 0.802, 1.11), respectively. The values of R² and error indices showed that there is very close agreement between the experimental and predicted results. 30 number chromosomes, 9 head size, and 5 genes yielded the optimum model. The parametric analysis revealed that d_b, c/d, and l/d significantly affected the BS. The FRP rebar diameter size is greater than 10 mm, whereas a l/d ratio of more than 12 showed a considerable decrease in BS. In contrast, the rise in c/d ratio revealed second-degree increasing trend of BS.

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

Reinforced concrete structures are subjected to frequent maintenance and repairs due to steel reinforcement corrosion. Fiber-reinforced polymer (FRP) laminates are widely used for retrofitting beams, columns, joints, and slabs. This study investigated the non-linear capability of artificial intelligence (AI)-based gene expression programming (GEP) modelling to develop a mathematical relationship for estimating the interfacial bond strength (IBS) of FRP laminates on a concrete prism with grooves. The model was based on five input parameters, namely axial stiffness (E_ft_f), width of FRP plate (b_f), concrete compressive strength (f_c′), width of groove (b_g), and depth of the groove (h_g), and IBS was considered the target variable. Ten trials were conducted based on varying genetic parameters, namely the number of chromosomes, head size, and number of genes. The performance of the models was evaluated using the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The genetic variation revealed that optimum performance was obtained for 30 chromosomes, 11 head sizes, and 4 genes. The values of R, MAE, and RMSE were observed as 0.967, 0.782 kN, and 1.049 kN for training and 0.961, 1.027 kN, and 1.354 kN. The developed model reflected close agreement between experimental and predicted results. This implies that the developed mathematical equation was reliable in estimating IBS based on the available properties of FRPs. The sensitivity and parametric analysis showed that the axial stiffness and width of FRP are the most influential parameters in contributing to IBS.

Ensemble Tree-Based Approach towards Flexural Strength Prediction of FRP Reinforced Concrete Beams

Due to rise in infrastructure development and demand for seawater and sea sand concrete, fiber-reinforced polymer (FRP) rebars are widely used in the construction industry. Flexural strength is an important component of reinforced concrete structural design. Therefore, this research focuses on estimating the flexural capacity of FRP-reinforced concrete beams using novel artificial intelligence (AI) decision tree (DT) and gradient boosting tree (GBT) approaches. For this purpose, six input parameters, namely the area of bottom flexural reinforcement, depth of the beam, width of the beam, concrete compressive strength, the elastic modulus of FRP rebar, and the tensile strength of rebar at failure, are considered to predict the moment bearing capacity of the beam under bending loads. The models were trained using 60% of the database and were validated first-hand on the remaining 40% database employing the correlation coefficient (R), error indices namely, mean absolute error, root mean square error (MAE, RMSE) and slope of the regression line between observed and predicted results. The developed models were further validated using sensitivity and parametric analysis. Both models revealed comparable performance; however, based on the comparison of the slope of the validation data (0.83 for GBT model against 0.75 for the DT model) and higher R for the validation phase in case of the GBT model in comparison to the DT, the GBT model can be considered more accurate and robust. The sensitivity analysis yielded depth of the beam as the most influential parameter in contributing flexural strength of the beam, followed by the area of flexural reinforcement. The developed GBT model surpasses the existing gene expression programming (GEP) model in terms of accuracy; however, the current American Concrete Institute (ACI) model equations are more reliable than AI models in predicting the flexural strength of FRP-reinforced concrete beams.

Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices

Water contamination is indeed a worldwide problem that threatens public health, environmental protection, and agricultural productivity. The distinctive attributes of machine learning (ML)-based modelling can provide in-depth understanding into increasing water quality challenges. This study presents the development of a multi-expression programming (MEP) based predictive model for water quality parameters, i.e., electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River at two different outlet locations using 360 readings collected on a monthly basis. The optimized MEP models were assessed using different statistical measurements i.e., coefficient-of-determination (R2), root-mean-square error (RMSE), mean-absolute error (MAE), root-mean-square-logarithmic error (RMSLE) and mean-absolute-percent error (MAPE). The results show that the R2 in the testing phase (subjected to unseen data) for EC-MEP and TDS-MEP models is above 0.90, i.e., 0.9674 and 0.9725, respectively, reflecting the higher accuracy and generalized performance. Also, the error measures are quite lower. In accordance with MAPE statistics, both the MEP models shows an “excellent” performance in all three stages. In comparison with traditional non-linear regression models (NLRMs), the developed machine learning models have good generalization capabilities. The sensitivity analysis of the developed MEP models with regard to the significance of each input on the forecasted water quality parameters suggests that Cl and HCO3 have substantial impacts on the predictions of MEP models (EC and TDS), with a sensitiveness index above 0.90, although the influence of the Na is the less prominent. The results of this research suggest that the development of intelligence models for EC and TDS are cost effective and viable for the evaluation and monitoring of the quality of river water.

Use of the Gene-Expression Programming Equation and FEM for the High-Strength CFST Columns

The ultimate strength of composite columns is a significant factor for engineers and, therefore, finding a trustworthy and quick method to predict it with a good accuracy is very important. In the previous studies, the gene expression programming (GEP), as a new methodology, was trained and tested for a number of concrete-filled steel tube (CFST) samples and a GEP-based equation was proposed to estimate the ultimate bearing capacity of the CFST columns. In this study, however, the equation is considered to be validated for its results, and to ensure it is clearly capable of predicting the ultimate bearing capacity of the columns with high-strength concrete. Therefore, 32 samples with high-strength concrete were considered and they were modelled using the finite element method (FEM). The ultimate bearing capacity was obtained by FEM, and was compared with the results achieved from the GEP equation, and both were compared to the respective experimental results. It was evident from the results that the majority of values obtained from GEP were closer to the real experimental data than those obtained from FEM. This demonstrates the accuracy of the predictive equation obtained from GEP for these types of CFST column.

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (P_sUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP). An extensive database comprising 168 P_s and 145 UCS records was established after a comprehensive literature search. The nine most influential and easily determined geotechnical parameters were taken as the predictor variables. The network was trained and tested, and the predictions of the proposed models were compared with the observed results. The performance of all the models was tested using mean absolute error (MAE), root squared error (RSE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), regression coefficient (R²) and relative root mean square error (RRMSE). The sensitivity analysis indicated that the increasing order of inputs importance in case of P_s followed the order: maximum dry density MDD (30.5%) > optimum moisture content OMC (28.7%) > swell percent SP (28.1%) > clay fraction CF (9.4%) > plasticity index PI (3.2%) > specific gravity G_s (0.1%), whereas, in case of UCS it followed the order: sand (44%) > PI (26.3%) > MDD (16.8%) > silt (6.8%) > CF (3%) > SP (2.9%) > G_s (0.2%) > OMC (0.03%). Parametric analysis was also performed and the resulting trends were found to be in line with findings of past literature. The comparison results reflected that GEP and ANN are efficacious and reliable techniques for estimation of P_sUCS-ES. The derived mathematical GP-based equations portray the novelty of GEP model and are comparatively simple and reliable. The R_overall values for P_sUCS-ES followed the order: ANN > GEP > ANFIS, with all values lying above the acceptable range of 0.80. Hence, all the proposed AI approaches exhibit superior performance, possess high generalization and prediction capability, and evaluate the relative importance of the input parameters in predicting the P_sUCS-ES. The GEP model outperformed the other two models in terms of closeness of training, validation and testing data set with the ideal fit (1:1) slope. Evidently the findings of this study can help researchers, designers and practitioners to readily evaluate the swell-strength characteristics of the widespread expansive soils thus curtailing their environmental vulnerabilities which leads to faster, safer and sustainable construction from the standpoint of environment friendly waste management.

Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization

A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor.

Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.