Multi Expression Programming Research Articles

Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)

Plain concrete is weak against tension and has low Tensile Strain Capacity (TSC) which significantly affects its long-term performance. To overcome this issue, Engineered Cementitious Composites (ECC) were developed by incorporating polymer fibres in the cement matrix which increases ductility and provides higher TSC than plain concrete and they have emerged as a viable alternative to brittle plain concrete. This study is conducted in an attempt to develop empirical prediction models for TSC prediction of ECC without requiring extensive experimental procedures. For this purpose, two evolutionary programming techniques known as Multi Expression Programming (MEP), Gene Expression Programming (GEP) along with two boosting-based techniques: AdaBoost and Extreme Gradient Boosting (XGB) were developed using data collected from published literature. The gathered dataset had seven input parameters including water-to-binder ratio, sand, fibre content, cement, fly ash, superplasticizer, and age etc. and only one output parameter i.e., TSC. The error assessment of developed models was done using correlation coefficient, Mean Absolute Error (MAE), and Objective Function (OF) etc. and the error comparison showed that XGB has the highest accuracy having the least OF value of 0.081 as compared to 0.11 of AdaBoost, 0.13 of GEP, and 0.16 of MEP. Shapley additive analysis was conducted on the XGB model since it proved to be the most accurate, and the results highlighted that fibre content, age, and water-to-binder ratio are the most important features to predict TSC of ECC.

Evolutionary Algorithms for Strength Prediction of Geopolymer Concrete

Geopolymer concrete (GPC) serves as a sustainable substitute for conventional concrete by employing alternative cementitious materials such as fly ash (FA) instead of ordinary Portland cement (OPC), contributing to environmental and durability benefits. To increase the rate of utilization of FA in the construction industry, distinctive characteristics of two machine learning (ML) methods, namely, gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to propose precise prediction models for the compressive strength and split tensile strength of GPC comprising FA as a binder. A comprehensive database was collated, which comprised 301 compressive strength and 96 split tensile strength results. Seven distinct input variables were employed for the modeling purpose, i.e., FA, sodium hydroxide, sodium silicate, water, superplasticizer, and fine and coarse aggregates contents. The performance of the developed models was assessed via numerous statistical metrics and absolute error plots. In addition, a parametric analysis of the finalized models was performed to validate the prediction ability and accuracy of the finalized models. The GEP-based prediction models exhibited better performance, accuracy, and generalization capability compared with the MEP-based models in this study. The GEP-based models demonstrated higher correlation coefficients (R) for predicting the compressive and split tensile strengths, with the values of 0.89 and 0.87, respectively, compared with the MEP-based models, which yielded the R values of 0.76 and 0.73, respectively. The mean absolute errors for the GEP- and MEP-based models for predicting the compressive strength were 5.09 MPa and 6.78 MPa, respectively, while those for the split tensile strengths were 0.42 MPa and 0.51 MPa, respectively. The finalized models offered simple mathematical formulations using the GEP and Python code-based formulations from MEP for predicting the compressive and tensile strengths of GPC. The developed models indicated practical application potential in optimizing geopolymer mix designs. This research work contributes to the ongoing efforts in advancing ML applications in the construction industry, highlighting the importance of sustainable materials for the future.

Performance evaluation of concrete made with plastic waste using multi-expression programming

The immense production of plastic waste due to its non-biodegradable nature has become a major issue for the world. Several researchers have recently tried to incorporate plastic waste in building materials, particularly as a substitute for cement in concrete, using conventional experimental testing. However, it is difficult to examine the optimal mix design in experimental investigations due to time and resource constraints. Therefore, this study aims to provide robust multi-expression programming (MEP) based predictive equations to evaluate the compressive strength (CS) and tensile strength (TS) of concrete containing plastic waste. Based on the literature, a comprehensive data record of 276 and 235 samples of the CS and TS of plastic concrete was generated for model development. The models' prediction capabilities were assessed by evaluating the various statistical indicators and comparing the results with a multi-linear regression (MLR) model. Furthermore, a sensitivity analysis was conducted to find out the contribution of each parameter to the CS and TS of plastic concrete. According to the statistical findings, the MEP model demonstrated higher efficacy in prediction, with an R2 value of 0.87 and 0.89 for the CS and TS models. In addition, MEP generates a simple mathematical formula, which can be employed as a design tool for estimating the CS and TS of plastic concrete. In sensitivity analysis, age demonstrated the highest sensitivity (24.8% and 31%), demonstrating its substantial impact on the model's outputs. Cement (17.63% and 17.3%) and plastic (17.4% and 15.3%) have similar contributions to the CS and TS. These results are consistent with previous research, highlighting the agreement between the results of this study and the literature. Therefore, this study can be employed for sustainable construction practices.

Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming

Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.

Experimenting the influence of corncob ash on the mechanical strength of slag-based geopolymer concrete

Abstract The construction sector has been under growing public attention recently as one of the leading causes of climate change and its detrimental effects on local communities. In this regard, geopolymer concrete (GPC) has been proposed as a replacement for conventional concrete. Predicting the concrete’s strength before pouring is, therefore, quite useful. The mechanical strength of slag and corncob ash (SCA–GPC), a GPC made from slag and corncob ash, was predicted utilizing multi-expression programming (MEP). Modeling parameters’ relative importance was determined using sensitivity analysis. When estimating the compressive, flexural, and split tensile strengths of SCA–GPC with MEP, 0.95, 0.93, and 0.92 R 2-values were noted between the target and predicted results. The developed models were validated using statistical tests for error and efficiency. The sensitivity analysis revealed that within the mix proportions, the slag quantity (65%), curing age (25%), and fine aggregate (3.30%) quantity significantly influenced the mechanical strength of SCA–GPC. The MEP models result in distinct empirical equations for the strength characteristics of SCA–GPC, unlike Python-based models, which might aid industry and researchers worldwide in determining optimal mix design proportions, thus eliminating unneeded test repetitions in the laboratory.

Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete

Abstract As a potential replacement for traditional concrete, which has cracking and poor durability issues, self-healing concrete (SHC) has been the research subject. However, conducting lab trials can be expensive and time-consuming. Therefore, machine learning (ML)-based predictions can aid improved formulations of self-healing concrete. The aim of this work is to develop ML models that could analyze and forecast the rate of healing of the cracked area (CrA) of bacteria- and fiber-containing SHC. These models were constructed using gene expression programming (GEP) and multi-expression programming (MEP) tools. The discrepancy between expected and desired results, statistical tests, Taylor’s diagram, and R 2 values were additional metrics used to assess the constructed models. A SHapley Additive exPlanations (SHAP) approach was used to evaluate which input attributes were highly relevant. With R 2 = 0.93, MAE = 0.047, MAPE = 12.60%, and RMSE = 0.062, the GEP produced somewhat worse predictions than the MEP (R 2 = 0.93, MAE = 0.033, MAPE = 9.60%, and RMSE = 0.044). Bacteria had an indirect (negative) relationship with the CrA of SHC, while fiber had a direct (positive) association, according to the SHAP study. The SHAP study might help researchers and companies figure out how much of each raw material is needed for SHCs. Therefore, MEP and GEP models can be used to generate and test SHC compositions based on bacteria and polymeric fibers.

Multi-expression programming based prediction of the seismic capacity of reinforced concrete rectangular columns

This article presents an innovative artificial intelligence based multi-expression programming approach to predict the seismic capacity of reinforced concrete rectangular columns. For the evolution of these multi-expression programming based computational models, an investigational test database created by pacific earthquake engineering research centre was employed. This database consisted of the experimental record of 250 specimens of reinforced concrete rectangular columns exposed to seismic loading. The overall seismic capacity of the aforementioned columns was considered to be the function of their flexural and shear capacities, for the prediction of which five input variables were used. Once these models were evolved utilizing MEPX version 2022.3.19.0-beta, their performance was determined on the basis of ten most frequently used performance indicators, one of which is the coefficient of determination i.e., R2. Based on the findings of this research, a strong correlation (i.e., an R2 of 0.9747 and 0.9664 for the flexural and shear capacity prediction models respectively) was found to exist between the predicted and tested seismic capacities of reinforced concrete rectangular columns. Moreover, the proposed seismic capacity prediction models were found to outperform that of ACI 318-19 in terms of precision and prediction accuracy. The proposed seismic capacity prediction models were also found to be well trained on the correlation between input and output variables which verifies their ability to capture the underlying physical processes involved in the seismic action on reinforced concrete rectangular columns. Therefore, the proposed multi-expression programming based computational models can be confidently recommended for predicting the seismic capacity of reinforced concrete rectangular columns for practical design applications.

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite.

Cementitious composites' performance degrades in extreme conditions, making it more important to enhance its resilience. To further the adaptability of eco-friendly construction, waste materials are increasingly being repurposed. Cementitious composites deteriorate in both direct and indirect ways due to the facilitation of hostile ion transport by water. The effects of using eggshell and glass powder as partial substitutes for cement and sand in mortar on the water-absorption capacity were investigated using machine learning (ML) modeling techniques such as Gene Expression Programming (GEP) and Multi Expression Programming (MEP). To further assess the importance of inputs, sensitivity analysis and interaction research were carried out. The water absorption property of cementitious composites was precisely estimated by the generated ML models. It was noted that the MEP model, with an R2 of 0.90, and the GEP model, with an R2 of 0.88, accurately predicted results. The sensitivity analysis revealed that the absorption capacity of the mortar was most affected by the presence of eggshell powder, sand, and glass powder. GEP and MEP model's significance lies in the fact that they offer one-of-a-kind mathematical formulas that can be applied to the prediction of features in another database. The mathematical models resulting from this study can help scientists and engineers rapidly assess, enhance, and rationalize mixture proportioning. The built models can theoretically compute the water absorption of cement mortar made from eggshell powder and glass powder based on varied input parameters, resulting in cost and time savings.

Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms

Fiber-reinforced polymers (FRP) are widely utilized to improve the efficiency and durability of concrete structures, either through external bonding or internal reinforcement. However, the response of FRP-strengthened reinforced concrete (RC) members, both in field applications and experimental settings, often deviates from the estimation based on existing code provisions. This discrepancy can be attributed to the limitations of code provisions in fully capturing the nature of FRP-strengthened RC members. Accordingly, machine learning methods, including gene expression programming (GEP) and multi-expression programming (MEP), were utilized in this study to predict the flexural capacity of the FRP-strengthened RC beam. To develop data-driven estimation models, an extensive collection of experimental data on FRP-strengthened RC beams was compiled from the experimental studies. For the assessment of the accuracy of developed models, various statistical indicators were utilized. The machine learning (ML) based models were compared with empirical and conventional linear regression models to substantiate their superiority, providing evidence of enhanced performance. The GEP model demonstrated outstanding predictive performance with a correlation coefficient (R) of 0.98 for both the training and validation phases, accompanied by minimal mean absolute errors (MAE) of 4.08 and 5.39, respectively. In contrast, the MEP model achieved a slightly lower accuracy, with an R of 0.96 in both the training and validation phases. Moreover, the ML-based models exhibited notably superior performances compared to the empirical models. Hence, the ML-based models presented in this study demonstrated promising prospects for practical implementation in engineering applications. Moreover, the SHapley Additive exPlanation (SHAP) method was used to interpret the feature's importance and influence on the flexural capacity. It was observed that beam width, section effective depth, and the tensile longitudinal bars reinforcement ratio significantly contribute to the prediction of the flexural capacity of the FRP-strengthened reinforced concrete beam.

Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.

Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete

Alkali-activated materials (AAMs) are a potential class of construction materials that are well-known for their versatility and capacity for long-term sustainability. As a result of its ability to lessen the negative effects that the building industry has on the environment, AAMs have become increasingly popular in recent years. However, it can be difficult and time-consuming to figure out what proportions of alkali-activated concrete (AAC) would work best for a given project. Compressive strength (CS) and slump, both of which are important properties of AAC's viability in construction, were predicted using machine learning (ML) techniques, such as multi-expression programming (MEP)and gene expression programming (GEP)in this study. The mathematical formulations of AAC for both slump and CS for theAAC were effectively derived with the application of these MLapproaches. According to the study's findings, MEP models performed better than GEP models in making accurate predictions, with MEP achieving R2 values of 0.92 and 0.93 for slump and CS in AAC, respectively, whereas GEP provided R2 values of 0.86 and 0.89. The hyper-parameters of the AI models were fine-tuned, and the models were verified with statistical measurements and Taylor diagrams. It's possible that using the findings from sensitivity analysis to estimate the relative importance of factors impacting the slumpand CS of AAC might be helpful. The artificial intelligence-based models that were built showed a strong connection with the desired outcomes, suggesting that they might be used to estimate the slump and CS of AAC for different values of the input components.

Investigating the feasibility of genetic algorithms in predicting the properties of eco-friendly alkali-based concrete

This study employed an AI-based strategy to determine the accurate parameters in alkali-activated concrete (AAC) mix design that contribute to optimal performance. The data mining approach was used to generate a dataset comprising seven crucial input parameters and three outputs. Gene expression programming (GEP) models and multi-expression programming (MEP) models were developed to predict the rheological characteristics (in terms of static/dynamic yield stress and plastic viscosity) of AAC. R2 values, statistical checks, Taylor's diagram, and the difference between experimental and anticipated rheological parameters were used to assess the suitability of the created models. All prediction models developed using the MEP approach were found to be highly accurate (R2 > 0.90), while all GEP models were found to be in the acceptable range of accuracy (R2 near 0.90). Moreover, the greater accuracy of MEP models over GEP models was also confirmed by error evaluation by statistical tests. The empirical equations presented by the models might be useful for comprehending the mix design of AAC and the effect of each input parameter. Precursor content was found to have a significantly positive impact on ACC's rheological properties, as determined by the sensitivity analysis.

Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete

Construction industry is indirectly the largest source of CO2 emissions in the atmosphere, due to the use of cement in concrete. These emissions can be reduced by using industrial waste materials in place of cement. Self-Compacting Concrete (SCC) is a promising material to enhance the use of industrial wastes in concrete. However, there are very few methods available for accurate prediction of its strength, therefore, reliable models for estimating 28-day Compressive Strength (C–S) of SCC are developed in current study by using three Machine Learning (ML) algorithms including Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Random Forest (RF). The ML models were meticulously developed using a dataset of 231 points collected from internationally published literature considering seven most influential parameters including cement content, quantities of fly ash and silica fume, water content, coarse aggregate, fine aggregate, and superplasticizer dosage to predict C–S. The developed models were evaluated using different statistical errors including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) etc. The results showed that the XGB model outperformed the MEP and RF model in terms of accuracy with a correlation R2 = 0.998 compared to 0.923 for MEP and 0.986 for RF. Similar trend was observed for other error metrices. Thus, XGB is the most accurate model for estimating C–S of SCC. However, it is pertinent to mention here that it does not give its output in the form of an empirical equation like MEP model. The construction of these empirical models will help to efficiently estimate C–S of SCC for practical purposes.

Predicting the damage to cementitious composites due to acid attack and evaluating the effectiveness of eggshell powder using interpretable artificial intelligence models

The resistance of cement-based composites (CBCs) to the intervention of aggressive ions is the primary determinant of their durability. H2SO4 is a highly corrosive acid capable of causing structural damage. The corrosive properties of H2SO4 can pose a significant threat to building materials, resulting in accelerated deterioration and structural failure. This study employed advanced computing tools to assess the compressive strength loss (CSL) in mortar modified with eggshell powder (ESP-CM) subjected to acid conditions. The modeling process utilized an experimental dataset of six different raw materials as inputs, and the output variable was the CSL. ESP-CM samples were subjected to a 5% H2SO4 solution during the experimental work. Two machine learning interpretable models, including gene-expression programming (GEP) and multi-expression programming (MEP), were developed for the said purpose. In addition, sensitivity analysis (SA) and SHapley Additive exPlanations (SHAP) analysis were used to assess the influence and correlation of raw inputs. The established models for predicting the CSL of ESP-CM due to acid attack corresponded well with the actual results. The accuracy of constructed models was evaluated using statistical analyses and analyzing the difference amongst target and model-estimated results. Comparing the R2 of the models, it was determined that the MEP model performed better than the GEP model in predicting the CSL of ESP-CM after an acid attack, with an R2 of 0.90 versus 0.87. The interaction diagrams derived from the SHAP study exhibited that a replacement level of ESP between 40 and 60 kg/m3 provided the greatest resistance to acid attack.

An ensemble tree-based prediction of Marshall mix design parameters and resilient modulus in stabilized base materials

A flexible pavement with an adequate Marshall mix design for the asphalt mixture surface layer(s) and appropriate subbase/base design offers a proper pavement structure for driving safety and comfortability. The conventional approach of calculating the Marshall mix design properties (e.g., stability and flow) and resilient modulus of base materials typically requires laborious, expensive, and time-consuming laboratory sample preparation and testing, such as the Marshall stability test and Resilient modulus test. Therefore, this study conducts research on the application of tree-based ensemble methods specifically, random forest (RFR) and eXtreme gradient boosting regression (XGBR) to predict the properties (mainly including Marshall stability, flow, VMA, VFA, and unit weigh) of asphalt mix following Marshall mix design and resilient modulus (Mr) of stabilized aggregate base material. To develop the prediction models, a comprehensive database was established using existing literature. The performance of the prediction models was evaluated using statistical parameters, performance index (p), and other suggested model performance criteria from external research. Additionally, the Shapley Additive exPlainations (SHAP) interpretation technique was employed to explain the models. The analysis results demonstrate that both RFR and XGBR methods have the excellent fitting capability (R > 0.9). However, XGBR has superior performance in predicting Marshall mix properties and Mr value. Comparison with previous research studies reveals that the proposed models show superior performance than genetic programming-based Marshall mix design models. The RFR model performs approximately similar to ANN, while XGBR outperforms ANN, and particle swarm optimization-extreme learning machine-based models for Mr prediction. Furthermore, the equation developed based on multi expression programming for Mr value prediction exhibits superior performance compared to previous research study gene expression programming model.

Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.

Mechanical behaviour of E-waste aggregate concrete using a novel machine learning algorithm: Multi expression programming (MEP)

Technological advancement encourages the usage of electronic appliances in daily life and makes it possible for users to switch to more advanced devices very easily and at a reasonable cost. As new devices are produced and manufactured at an alarming rate around the world, outdated old devices become e-waste. This research work aims at using a popular machine learning (ML) method known as Multi-expression programming (MEP) to examine the compressive strength (CS) and tensile strength (TS) of E-waste aggregate-based concrete (EWAC). 279 and 105 scientific entries for CS and TS, respectively, were culled from reputable literature. The ten convincing input parameters selected based on multicollinearity analysis (correlation matrix and variance inflation factor) were E-waste coarse aggregate (ECA%), E-waste fine aggregate (EFA%), water-cement ratio (w/c), age of concrete (A in days), fine aggregate water-absorption (WAF%), coarse aggregate water-absorption (WAC%), E-waste aggregate water-absorption (WAE%), E-waste aggregate specific-gravity (SGE), coarse aggregate specific-gravity (SGC), and fine aggregate specific-gravity (SGF). To estimate the functioning of the projected models, root-squared-error (RSE), mean-absolute error (MAE), mean-absolute-percent error (MAPE), Nash-Sutcliffe-efficiency (NSE), root-mean-squared error (RMSE), objective-function (OF), coefficient-of-correlation (R), root-mean-squared-logarithmic error (RMSLE), and performance-index (PI) were used. The R-value for both MEP models exceeds 0.9, showing “excellent” with MAPE values in the testing stage equals to 6.68% and 6.78% for the CS-MEP and TS-MEP models, respectively. While for non-linear regression (NLR) models, the MAPE exceeds 20% and 30%, respectively, making them unsuitable for future prediction. Moreover, the sensitivity analysis carried out to evaluate the MEP equations' consistency with the observed physical phenomena, indicates that for both CS and TS, the w/c, ECA%, and EFA% remain the most sensitive parameters with a sensitivity index greater than 0.60. Due to the accuracy and viability of developed models, they can be used to reduce the time needed for laborious laboratory tests.

RETRACTED: Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational investigation

Concerning environmental concerns, it has become crucial to invent and develop alternative construction materials that can replace ordinary Portland cement and use waste materials instead of natural aggregates in the production of concrete. An important advancement in this area is the utilization of recycled plastics to create geopolymer concrete, which helps reduce plastic waste and minimize the environmental impact of construction materials. Since plastics have become a significant environmental issue, it is imperative to find ways to recycle them for a sustainable future. Thus, this study was conducted involving the preparation of seventeen different mixtures of geopolymer concrete (GPC) in three stages. In the first stage, five GPC mixes were created with varying amounts of nano-silica (NS) ranging from 0 % to 4 %. The main objective of this stage was to determine the optimal NS content that could enhance the performance of the GPC matrix. In the second stage, recycled plastic aggregates (RPA) were introduced to replace a portion of the fine aggregates, ranging from 0.05 to 0.5 volume percentages. This aimed to assess the impact of RPAs on the fresh GPC and its mechanical properties. The third stage involved an analysis to improve the performance of the GPC compound incorporating RPAs. The optimal NS content identified in the first stage was utilized for all mixtures with RPA. Lastly, several modeling techniques, including artificial neural network, M5P tree, multiple linear regression, full quadratic, multi-expression programming, and linear regression, were employed to predict the compressive strength of the GPC compound. The results demonstrated that the addition of nano-silica increased the compressive strength of the GPC mixtures by 6.3 %, 13.4 %, 20.5 %, 21 %, and 21.9 % at curing ages of 3, 7, 28, 90, and 180 days, respectively, when using 3 % NS content compared to the control GPC mixture. However, the inclusion of NS had a negative impact on the workability of the GPC mixes. Furthermore, incorporating RPAs reduced both the workability and compressive strength of the GPC compound. In the end, the results obtained from the modeling analysis revealed that the compressive strength of GPC could be predicted with a high degree of accuracy by taking into account the mixture proportions of its constituents. Among the various techniques used, it was found that the artificial neural network model outperformed other methods in accurately forecasting the compressive strength of GPC. This indicates that the neural network model is a superior and reliable approach for estimating the strength characteristics of GPC, providing valuable insights for optimizing its composition and enhancing its overall performance.

A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis

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.

Prediction of soil thermal conductivity using artificial intelligence approaches

In this study, three artificial intelligence models, namely group method of data handing (GMDH), multi expression programming (MEP) and random forest (RF), are proposed to predict soil thermal conductivity. A large database of 444 data samples collected from existing literature was used to construct the three models. Three statistical indexes were used to evaluate the performance of the proposed three models and the other five existing models. The results show that the predicted results of the three models are in good agreement with the experimental results, and the RF model has the best comprehensive performance. Therefore, RF model is recommended to predict the thermal conductivity of unsaturated soil. To facilitate practical engineering applications, we have built a web program for the three proposed models, and users can use these three models by visiting the website.