Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis.

The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods­namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models­based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc'), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP.

Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming.

The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.

Prediction of Axial Capacity of Concrete Filled Steel Tubes Using Gene Expression Programming.

The safety and economy of an infrastructure project depends on the material and design equations used to simulate the performance of a particular member. A variety of materials can be used in conjunction to achieve a composite action, such as a hollow steel section filled with concrete, which can be successfully utilized in the form of an axially loaded member. This study aims to model the ultimate compressive strength (Pu) of concrete-filled hollow steel sections (CFSS) by formulating a mathematical expression using gene expression programming (GEP). A total of 149 datapoints were obtained from the literature, considering ten input parameters, including the outer diameter of steel tube (D), wall thickness of steel tube, compressive strength of concrete (fc'), elastic modulus of concrete (Ec), yield strength of steel (fv), elastic modulus of steel (Es), length of the column (L), confinement factor (ζ), ratio of D to thickness of column, and the ratio of length to D of column. The performance of the developed models was assessed using coefficient of regression R2, root mean squared error RMSE, mean absolute error MAE and comparison of regression slopes. It was found that the optimal GEP Model T3, having number of chromosomes Nc = 100, head size Hs = 8 and number of genes Ng = 3, outperformed all the other models. For this particular model, R2overall equaled 0.99, RMSE values were 133.4 and 162.2, and MAE = 92.4 and 108.7, for training (TR) and testing (TS) phases, respectively. Similarly, the comparison of regression slopes analysis revealed that the Model T3 exhibited the highest R2 of 0.99 with m = 1, in both the TR and TS stages, respectively. Finally, parametric analysis showed that the Pu of composite steel columns increased linearly with the value of D, t and fy.

Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches.

This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks-normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease.

Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques.

Interface yield stress (YS) and plastic viscosity (PV) have a significant impact on the pumpability of concrete mixes. This study is based on the application of predictive machine learning (PML) techniques to forecast the rheological properties of fresh concrete. The artificial neural network (NN) and random forest (R-F) PML approaches were introduced to anticipate the PV and YS of concrete. In comparison, the R-F model outperforms the NN model by giving the coefficient of determination (R2) values equal to 0.92 and 0.96 for PV and YS, respectively. In contrast, the model's legitimacy was also verified by applying statistical checks and a k-fold cross validation approach. The mean absolute error, mean square error, and root mean square error values for R-F models by investigating the YS were noted as 30.36 Pa, 1141.76 Pa, and 33.79 Pa, respectively. Similarly, for the PV, these values were noted as 3.52 Pa·s, 16.48 Pa·s, and 4.06 Pa·s, respectively. However, by comparing these values with the NN's model, they were found to be higher, which also gives confirmation of R-F's high precision in terms of predicting the outcomes. A validation approach known as k-fold cross validation was also introduced to authenticate the precision of employed models. Moreover, the influence of the input parameters was also investigated with regard to predictions of PV and YS. The proposed study will be beneficial for the researchers and construction industries in terms of saving time, effort, and cost of a project.

Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions.

Incorporating waste material, such as recycled coarse aggregate concrete (RCAC), into construction material can reduce environmental pollution. It is also well-known that the inferior properties of recycled aggregates (RAs), when incorporated into concrete, can impact its mechanical properties, and it is necessary to evaluate the optimal performance. Accordingly, artificial intelligence has been used recently to evaluate the performance of concrete compressive behaviour for different types of construction material. Therefore, supervised machine learning techniques, i.e., DT-XG Boost, DT-Gradient Boosting, SVM-Bagging, and SVM-Adaboost, are executed in the current study to predict RCAC's compressive strength. Additionally, SHapley Additive exPlanations (SHAP) analysis shows the influence of input parameters on the compressive strength of RCAC and the interactions between them. The correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the model's performance. Subsequently, the k-fold cross-validation method is executed to validate the model's performance. The R2 value of 0.98 from DT-Gradient Boosting supersedes those of the other methods, i.e., DT- XG Boost, SVM-Bagging, and SVM-Adaboost. The DT-Gradient Boosting model, with a higher R2 value and lower error (i.e., MAE, RMSE) values, had a better performance than the other ensemble techniques. The application of machine learning techniques for the prediction of concrete properties would consume fewer resources and take less time and effort for scholars in the respective engineering field. The forecasting of the proposed DT-Gradient Boosting models is in close agreement with the actual experimental results, as indicated by the assessment output showing the improved estimation of RCAC's compressive strength.

Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.

Steel-fiber-reinforced concrete (SFRC) has been introduced as an effective alternative to conventional concrete in the construction sector. The incorporation of steel fibers into concrete provides a bridging mechanism to arrest cracks, improve the post-cracking behavior of concrete, and transfer stresses in concrete. Artificial intelligence (AI) approaches are in use nowadays to predict concrete properties to conserve time and money in the construction industry. Accordingly, this study aims to apply advanced and sophisticated machine-learning (ML) algorithms to predict SFRC compressive strength. In the current work, the applied ML approaches were gradient boosting, random forest, and XGBoost. The considered input variables were cement, fine aggregates (sand), coarse aggregates, water, silica fume, super-plasticizer, fly ash, steel fiber, fiber diameter, and fiber length. Previous studies have not addressed the effects of raw materials on compressive strength in considerable detail, leaving a research gap. The integration of a SHAP analysis with ML algorithms was also performed in this paper, addressing a current research need. A SHAP analysis is intended to provide an in-depth understanding of the SFRC mix design in terms of its strength factors via complicated, nonlinear behavior and the description of input factor contributions by assigning a weighing factor to each input component. The performances of all the algorithms were evaluated by applying statistical checks such as the determination coefficient (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The random forest ML approach had a higher, i.e., 0.96, R2 value with fewer errors, producing higher precision than other models with lesser R2 values. The SFRC compressive strength could be anticipated by applying the random forest ML approach. Further, it was revealed from the SHapley Additive exPlanations (SHAP) analysis that cement content had the highest positive influence on the compressive strength of SFRC. In this way, the current study is beneficial for researchers to effectively and quickly evaluate SFRC compressive strength.

A Hybrid SVR-Based Prediction Model for the Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prisms.

The current work presents a comparative study of hybrid models that use support vector machines (SVMs) and meta-heuristic optimization algorithms (MOAs) to predict the ultimate interfacial bond strength (IBS) capacity of fiber-reinforced polymer (FRP). More precisely, a dataset containing 136 experimental tests was first collected from the available literature for the development of hybrid SVM models. Five MOAs, namely the particle swarm optimization, the grey wolf optimizer, the equilibrium optimizer, the Harris hawks optimization and the slime mold algorithm, were used; five hybrid SVMs were constructed. The performance of the developed SVMs was then evaluated. The accuracy of the constructed hybrid models was found to be on the higher side, with R2 ranges between 0.8870 and 0.9774 in the training phase and between 0.8270 and 0.9294 in the testing phase. Based on the experimental results, the developed SVM-HHO (a hybrid model that uses an SVM and the Harris hawks optimization) was overall the most accurate model, with R2 values of 0.9241 and 0.9241 in the training and testing phases, respectively. Experimental results also demonstrate that the developed hybrid SVM can be used as an alternate tool for estimating the ultimate IBS capacity of FRP concrete in civil engineering projects.

Investigating the Bond Strength of FRP Rebars in Concrete under High Temperature Using Gene-Expression Programming Model.

In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70% of the data was used for training the model and remaining 30% was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations.

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.

Computational AI Models for Investigating the Radiation Shielding Potential of High-Density Concrete.

Concrete is an economical and efficient material for attenuating radiation. The potential of concrete in attenuating radiation is attributed to its density, which in turn depends on the mix design of concrete. This paper presents the findings of a study conducted to evaluate the radiation attenuation with varying water-cement ratio (w/c), thickness, density, and compressive strength of concrete. Three different types of concrete, i.e., normal concrete, barite, and magnetite containing concrete, were prepared to investigate this study. The radiation attenuation was calculated by studying the dose absorbed by the concrete and the linear attenuation coefficient. Additionally, artificial neural network (ANN) and gene expression programming (GEP) models were developed for predicting the radiation shielding capacity of concrete. A correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE) were calculated as 0.999, 1.474 mGy, 2.154 mGy and 0.994, 5.07 mGy, 5.772 mGy for the training and validation sets of the ANN model, respectively. Similarly, for the GEP model, these values were recorded as 0.981, 13.17 mGy, and 20.20 mGy for the training set, whereas the validation data yielded R = 0.985, MAE = 12.2 mGy, and RMSE = 14.96 mGy. The statistical evaluation reflects that the developed models manifested close agreement between experimental and predicted results. In comparison, the ANN model surpassed the accuracy of the GEP models, yielding the highest R and the lowest MAE and RMSE. The parametric and sensitivity analysis revealed the thickness and density of concrete as the most influential parameters in contributing towards radiation shielding. The mathematical equation derived from the GEP models signifies its importance such that the equation can be easily used for future prediction of radiation shielding of high-density concrete.

Exploring the Use of Waste Marble Powder in Concrete and Predicting Its Strength with Different Advanced Algorithms.

Recently, the high demand for marble stones has progressed in the construction industry, ultimately resulting in waste marble production. Thus, environmental degradation is unavoidable because of waste generated from quarry drilling, cutting, and blasting methods. Marble waste is produced in an enormous amount in the form of odd blocks and unwanted rock fragments. Absence of a systematic way to dispose of these marble waste massive mounds results in environmental pollution and landfills. To reduce this risk, an effort has been made for the incorporation of waste marble powder into concrete for sustainable construction. Different proportions of marble powder are considered as a partial substitute in concrete. A total of 40 mixes are prepared. The effectiveness of marble in concrete is assessed by comparing the compressive strength with the plain mix. Supervised machine learning algorithms, bagging (Bg), random forest (RF), AdaBoost (AdB), and decision tree (DT) are used in this study to forecast the compressive strength of waste marble powder concrete. The models' performance is evaluated using correlation coefficient (R2), root mean square error, and mean absolute error and mean square error. The achieved performance is then validated by using the k-fold cross-validation technique. The RF model, having an R2 value of 0.97, has more accurate prediction results than Bg, AdB, and DT models. The higher R2 values and lesser error (RMSE, MAE, and MSE) values are the indicators for better performance of RF model among all individual and ensemble models. The implementation of machine learning techniques for predicting the mechanical properties of concrete would be a practical addition to the civil engineering domain by saving effort, resources, and time.

Split Tensile Strength Prediction of Recycled Aggregate-Based Sustainable Concrete Using Artificial Intelligence Methods.

Sustainable concrete is gaining in popularity as a result of research into waste materials, such as recycled aggregate (RA). This strategy not only protects the environment, but also meets the demand for concrete materials. Using advanced artificial intelligence (AI) approaches, this study anticipates the split tensile strength (STS) of concrete samples incorporating RA. Three machine-learning techniques, artificial neural network (ANN), decision tree (DT), and random forest (RF), were examined for the specified database. The results suggest that the RF model shows high precision compared with the DT and ANN models at predicting the STS of RA-based concrete. The high value of the coefficient of determination and the low error values of the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) provided significant evidence for the accuracy and precision of the RF model. Furthermore, statistical tests and the k-fold cross-validation technique were used to validate the models. The importance of the input parameters and their contribution levels was also investigated using sensitivity analysis and SHAP analysis.

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach.

Coal mining waste in the form of coal gangue (CG) was established recently as a potential fill material in earthworks. To ascertain this potential, this study forecasts the strength and California Bearing Ratio (CBR) characteristics of chemically stabilized CG by deploying two widely used artificial intelligence approaches, i.e., artificial neural network (ANN) and random forest (RF) regression. In this research work, varied dosage levels of lime (2, 4, and 6%) and gypsum (0.5, 1, and 1.5%) were employed for determining the unconfined compression strength (UCS) and CBR of stabilized CG mixes. An experimental study comprising 384 datasets was conducted and the resulting database was used to develop the ANN and RF regression models. Lime content, gypsum dosage, and 28 d curing period were considered as three input attributes in obtaining three outputs (i.e., UCS, unsoaked CBR, and soaked CBR). While modelling with the ANN technique, different algorithms, hidden layers, and the number of neurons were studied while selecting the optimum model. In the case of RF regression modelling, optimal grid comprising maximal depth of tree, number of trees, confidence, random splits, enabled parallel execution, and guess subset ratio were investigated, alongside the variable number of folds, to obtain the best model. The optimum models obtained using the ANN approach manifested relatively better performance in terms of correlation coefficient values, equaling 0.993, 0.995, and 0.997 for UCS, unsoaked CBR and soaked CBR, respectively. Additionally, the MAE values were observed as 45.98 kPa, 1.41%, and 1.18% for UCS, unsoaked CBR, and soaked CBR, respectively. The models were also validated using 2-stage validation processes. In the first stage of validation of the model (using unseen 30% of the data), it was revealed that reliable performance of the models was attained, whereas in the second stage (parametric analysis), results were achieved which are corroborated with those in existing literature.

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming.

Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen's age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models' performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.

Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models.

An accurate calculation of the flexural capacity of flexural members is vital for the safe and economical design of FRP reinforced structures. The existing empirical models are not accurately calculating the flexural capacity of beams and columns. This study investigated the estimation of the flexural capacity of beams using non-linear capabilities of two Artificial Intelligence (AI) models, namely Artificial neural network (ANN) and Random Forest (RF) Regression. The models were trained using optimized hyperparameters obtained from the trial-and-error method. The coefficient of correlation (R), Mean Absolute Error, and Root Mean Square Error (RMSE) were observed as 0.99, 5.67 kN-m, and 7.37 kN-m, for ANN, while 0.97, 7.63 kN-m, and 8.02 kN-m for RF regression model, respectively. Both models showed close agreement between experimental and predicted results; however, the ANN model showed superior accuracy and flexural strength performance. The parametric and sensitivity analysis of the ANN models showed that an increase in bottom reinforcement, width and depth of the beam, and increase in compressive strength increased the bending moment capacity of the beam, which shows the predictions by the model are corroborated with the literature. The sensitivity analysis showed that variation in bottom flexural reinforcement is the most influential parameter in yielding flexural capacity, followed by the overall depth and width of the beam. The change in elastic modulus and ultimate strength of FRP manifested the least importance in contributing flexural capacity.