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This paper presents a study of the combined effect of PVA (powder form) and fly ash on the mechanical properties of concrete. Three different parameters (PVA, fly ash, and different patterns of curing) were used. The Taguchi statistical method has been used to decrease the number of concrete specimens from 160 to 54. Different percentages of PVA (1% and 2%) with fly ash (10% and 20%) were used in 36 concrete samples. Afterward, all samples were cured in three different ways namely: Open air-dry curing, normal water curing, and temperature bath curing. Samples with 1% PVA and 10% Fly ash shows the highest compressive strength, tensile strength, and modulus of elasticity. The improvement in strength is due to the interaction of PVA and FA with cement, filling pores, and strengthening the bond between the cement. Furthermore, the samples kept in water curing exhibited the greatest strength among all types of curing.

Concrete and plastic are a major source of environmental contamination and unsustainability. This study has been employed to come up with the eco-friendly concrete by using the plastic waste to promote the sustainability. Gamma radiations were used to enhance the micro-structure of plastic waste to increase the mechanical properties of resulting concrete. The crystallinity and chemical properties of regular and irradiated plastic were studied using x-ray diffraction (Xrd) and x-ray fluorescence (Xrf), respectively. Enhancement in the crystallinity and chemical properties were observed in the irradiated plastic. Different compositions by weight were opted for replacement of cement and fine aggregate for both regular and irradiated plastic waste with silica fume and superplasticizers. A set of experiments were performed on concrete and the effect of plastic waste on the compressive strength and split tensile strength of concrete was examined. Untreated replacement of plastic waste has shown to decrease the compressive strength and anomalous behavior was observed in case of split tensile strength. Both the mechanical properties were enhanced by replacing the cement and fine aggregates with irradiated plastic waste including silica fume and superplasticizers. This study contributes towards sustainable development of green concrete by lowering the carbon emissions and overcoming the fine aggregate scarcity problem. Moreover, as plastic is ductile and inclusion of plastic in concrete lowers the overall weight of the structure; impact of an earthquake is reduced.

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete.

This study provides the application of a machine learning-based algorithm approach names “Multi Expression Programming” (MEP) to forecast the compressive strength of carbon fiber-reinforced polymer (CFRP) confined concrete. The suggested computational Multiphysics model is based on previously reported experimental results. However, critical parameters comprise both the geometrical and mechanical properties, including the height and diameter of the specimen, the modulus of elasticity of CFRP, unconfined strength of concrete, and CFRP overall layer thickness. A detailed statistical analysis is done to evaluate the model performance. Then the validation of the soft computational model is made by drawing a comparison with experimental results and other external validation criteria. Moreover, the results and predictions of the presented soft computing model are verified by incorporating a parametric analysis, and the reliability of the model is compared with available models in the literature by an experimental versus theoretical comparison. Based on the findings, the valuation and performance of the proposed model is assessed with other strength models provided in the literature using the collated database. Thus the proposed model outperformed other existing models in term of accuracy and predictability. Both parametric and statistical analysis demonstrate that the proposed model is well trained to efficiently forecast strength of CFRP wrapped structural members. The presented study will promote its utilization in rehabilitation and retrofitting and contribute towards sustainable construction material.

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.

This study presents the performance of quarry rock dust (QRD) incorporated fly ash (FA) and slag (SG) based geopolymer concretes (QFS-GPC) exposed to ambient and elevated temperatures. A total of five QFS-GPC mix types were prepared. The quantity of FA (50%) was kept constant in all the mixes, and SG was replaced by 5%, 10%, 15%, and 20% of QRD. The fresh, hardened properties of the QFS-GPC mixes, viz., workability, compressive strength, splitting tensile strength, and flexural strengths, and XRD for identification of reaction phases were evaluated. The prepared mixes were also heated up to 800 °C to evaluate the residual compressive strength and weight loss. The workability of the QFS-GPC mixes was observed to be reduced by increasing the dosage (0 to 20%) of QRD. Superplasticizer (SP) was used to maintain the medium standard of workability. The compressive, tensile, and flexural strengths were increased by replacing SG with QRD up to 15%, whereas a further higher dosage (20%) of QRD reduced the mechanical strengths of the QFS-GPC mixes. The strength of the QFS-GPC specimens, heated to elevated temperatures up to 800 °C, was reduced persistently with the increased contents of QRD from 0 to 20%. It was concluded from the study that QFS-GPC can be used to achieve 30 MPa strength of concrete.

In this paper performance of graphite nano platelets and carbon nanotubes was investigated in both conventional as well as self-compacting mortar. Workability, linear-shrinkage, rheological properties, flexural and compressive strength, air content and water absorption tests were conducted. Results reveal that by addition of nano media in cement matrix workability, shrinkage, air content, and water absorption adamantly reduces as compared to control mixtures. On the other hand yield stress, plastic viscosity, compressive and flexural strength enhances significantly. The shrinkage response of SCM-3 and CCM-3 depicts reduction of 66% and 61% respectively. Furthermore, yield stress and plastic viscosity increase 53% and 12% respectively. By addition of nano media deviation in crack propagation, more nucleation sites, capturing the micro voids, filling the pores and dense packing of the hydrated products were found. Increase in compressive strength and flexural strength was observed. Hence hybrid nano media can be intruded as a potential candidate in cement matrix for enhancing the structural properties. Keywords: Linear shrinkage, Rheological properties, Stress strain response, Air content, Water absorption and microstructure

The majority of experimental and analytical studies on fiber-reinforced polymer (FRP) confined concrete has largely concentrated on plain (unreinforced) small-scale concrete columns, on which the efficiency of strengthening is much higher compared with large-scale columns. Although reinforced concrete (RC) columns subjected to combined axial compression and flexural loads (i.e., eccentric compression) are the most common structural elements used in practice, research on eccentrically-loaded FRP-confined rectangular RC columns has been much more limited. More specifically, the limited research has generally been concerned with small-scale RC columns, and hence, the proposed eccentric-loading stress-strain models were mainly based on the existing concentric-loading models of FRP-confined concrete columns of small scale. In the light of such demand to date, this paper is aimed at developing a mathematical model to better predict the strength of FRP-confined rectangular RC columns. The strain distribution of FRP around the circumference of the rectangular sections was investigated to propose equations for the actual rupture strain of FRP wrapped in the horizontal and vertical directions. The model was accomplished using 230 results of 155 tested specimens compiled from 19 studies available in the technical literature. The test database covers an unconfined concrete strength ranging between 9.9 and 73.1 MPa, and section’s dimension ranging from 100–300 mm and 125–435 mm for the short and long sides, respectively. Other test parameters, such as aspect ratio, corner radius, internal hoop steel reinforcement, FRP wrapping layout, and number of FRP wraps were all considered in the model. The performance of the model shows a very good correlation with the test results.

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (fc′), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of fc′, fSTS and fFS of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study’s findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete.

Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.

In the current study, the utilization of glass and nylon fibers in various percentages are added to enhance the mechanical performance of peach shell lightweight concrete. Glass and nylon fibers were added at 2%, 4%, 6%, and 8% by cement weight. The results showed that, as we added the glass and nylon fibers, the density of peach shell concrete was reduced by 6.6%, and the compressive, split tensile and flexural strength were enhanced by 10.20%, 60.1%, and 63.49%. The highest strength that was obtained in compressive, split tensile, and flexural strength at 56 days was 29.4 MPa, 5.2 MPa, and 6.3 MPa, respectively, with 6% of glass fiber in peach shell concrete. Mechanical test results showed that post-failure toughness and modulus of elasticity of peach shell concrete is enhanced with the utilization of fibers. To verify our lab results, a statistical analysis, such as response surface methodology, was performed to make a statistical model, it was confirmed by both lab results and statistical analysis that the mechanical performance of peach shell concrete could be significantly improved by adding glass fibers as compared to nylon fibers. With the use of fibers, the water absorption and porosity were slightly increased. Hence, the glass and nylon fibers can be used to improve the peach shell concrete mechanical properties to make concrete eco-friendly, sustainable, and lightweight.