Hany Ahmed Dahish

This study utilizes response surface methodology (RSM) to examine the effectiveness of several nanomaterials on the residual compressive strength (CS) of concrete exposed to high temperature. Three nanoparticles were used as partial cement replacement by weight at replacement ratios up to 5%: nano-granodiorite (NG), nano-magnetite (NM), and nano-lead oxide (NL). The specimens were exposed to temperatures of 400 °C, 600 °C, and 800 °C, and the residual compressive strength was determined. The experimental results exhibited that all nanoparticles in concrete specimens reduced the loss of strength at high temperature. The control specimen lost 48.39% of its compressive strength at 800 °C. A dose of 4% NG and NM resulted in an increase in residual strength of 37.87% and 22.06%, respectively, while 3% NL specimens resulted in 11.4% improvement. A regression model for predicting concrete CS was developed using RSM. The model was successful in capturing the high correlation between the input variables (temperature, type of nanomaterial, and nanoparticle dose) and the output of CS. A good agreement between model predictions and the experimental data was obtained. The results of CS optimization showed that 5% NG at 126 °C resulted in the highest compressive strength of 61.80 MPa, confirming its superiority over NM and NL for the formulation of concrete under thermal conditions. The results reveal that the utilized nanoparticles, especially NG, have great potential to be used as cementitious materials to improve the high-temperature resilience of concrete.

This research shows the combined effects of natural pozzolan (NP) and waste glass powder (WGP) on the mechanical strength of polypropylene fiber (PPF)-reinforced cement mortars. As the construction sector is looking for alternatives to traditional binding materials, this study addresses the challenge of reducing carbon footprints. A dataset of 405 test results for compressive strength (fc), tensile strength (ft) and flexural strength (fr) was used to model and optimize the impact of partial replacement of cement with NP and WGP at replacement levels up to 20 and 30% by weight, respectively, and PPF at volume levels of 0.5 and 1.5% on the strengths of mortars utilizing response surface methodology (RSM). A solution combining sodium hydroxide and sodium silicate (SH + SS) is utilized at a constant ratio. The results show that all mixes gained strength with curing age, increasing WGP generally reduced the strength at all ages, and a moderate NP content (10%) gave the highest compressive and tensile strengths. The importance and applicability of established models were evaluated, and the contributions of the variables were examined using analysis of variance (ANOVA) along with other statistical tests. Numerical optimization was utilized to identify the optimal NP, WGP, and PPF values for optimizing mortar mechanical properties. The statistical models generated via RSM exhibited high coefficients of determination (R²) of 0.9835, 0.9775, and 0.921 for fc, ft, and fr, respectively, indicating the reliability of the design in predicting mortar behavior. The highest measured strengths were obtained with 0.5% PPF, while the multi-response optimization gave about 1.0% PPF when the cement replacement was also maximized. The study concludes that an optimized blend of NP and WGP can produce eco-friendly mortars that meet structural requirements for specific masonry applications.

The construction industry’s growth trend has resulted in a considerable huge volume of demolished concrete. The proper usage of recycled aggregate (RA) obtained from demolished construction in concrete production provides a potential sustainable alternative to natural aggregates, but it leads to a loss in concrete strength. Incorporating polypropylene fibers (PPFs) into concrete with RA is considered an effective way to enhance the properties of RA concrete. However, the variability in RA and PPF properties makes correct estimates of splitting tensile strength (TS) extremely challenging. This study focuses on applying two machine learning (ML) algorithms, Extreme Gradient Boosting (XGB) and Random Forest (RF) improved by Particle Swarm Optimization (PSO), to predict the TS of polypropylene-fiber recycled-aggregate concrete (PPF-RA-concrete). To develop the models, a dataset of 556 TS data points was used with different amounts of twelve input parameters acquired from the literature. The input parameters covered the mixture proportions coupled with the properties of RA and PPF. The results show PSO-XGB achieved R² = 0.9708, a root mean squared error of 0.22 MPa, a mean absolute error of 0.152 MPa and a mean absolute percentage error of 4.74%, outperforming PSO-RF. Shapley additive explanations, individual conditional expectation and partial dependence plots were used to examine model responses. The results revealed that the RA replacement ratio has the largest negative impact on TS prediction, while the curing age has the largest positive impact on TS prediction, followed by the aspect ratio of PPF. The created Graphical User Interface (GUI) acts as an excellent tool for estimating TS of PPF-RA-concrete within the range of the dataset used.

Nanoparticle-modified concrete can exhibit improved mechanical performance, yet its residual compressive strength (Fc) after fire-like thermal exposure is difficult to predict because the response depends on both mixture design and heating conditions. Building on recent advances in explainable machine learning (ML) for cementitious materials, this study compiles 218 literature datapoints of post-heating Fc from 100 mm concrete cubes incorporating carbon nanotubes (CNTs) and nano-alumina (NA), exposed to 20–800 °C for up to 2 h. Seven input variables are used: cement-to-total aggregate ratio, CNT-to-cement ratio, NA-to-cement ratio, coarse-to-fine aggregate ratio, water-to-cement ratio, peak temperature, and exposure duration at temperature. Two particle-swarm-optimized ensemble regression models, Extreme Gradient Boosting (XGB-PSO) and Random Forest (RF-PSO), were developed and evaluated using a 70/30 train–test split with K-fold cross-validation on the training set. SHAP, individual conditional expectation (ICE), and partial dependence plots (PDPs) were employed to study the individual and combined effects of each input parameter on Fc prediction. The results demonstrated that the XGB-PSO model provides the best predictive performance (training R² = 0.9983; testing R² = 0.9434; testing MAE = 1.3168 MPa). Model interpretability was assessed using SHAP, ICE, and PDP analyses, revealing that temperature and exposure duration dominate strength loss, while CNTs and NA contribute positively within dose-dependent regimes. The highest predicted strengths occur for CNTs of 0.05% to 0.15% and NA of 0.65 to 2.71% (by cement mass) under moderate temperature exposure. A Python-based graphical user interface is provided to support rapid what-if assessment of CNT–NA mixtures under elevated-temperature scenarios.

This research highlights the mechanical performance of alkali-activated polypropylene fiber (PPF) mortar containing natural pozzolan (NP) and waste glass powder (WGP) as partial replacements for cement. A total of 45 mix combinations and 405 samples were prepared by varying the levels of NP, WGP, PPF, and sodium silicate (SS), with sodium hydroxide (SH) as an alkali activator. The levels for these variables are NP (0%, 10%, and 20%) and WGP (0%, 10%, 20%, and 30%) by weight of the cement; PPF (0%, 0.5%, and 1.5%) by volume of mortar; and SS + SH (30%, 40%, and 50%) by weight of the binder. The molarity of the SH solution was kept at 10 M, while the SS/SH ratio was maintained at 2.5. Compressive (f’c), flexural (fr), and split tensile strength (ft) were evaluated at 7, 28, and 90 days. The results showed that strength development is strongly age-dependent, with 85–90% of the total strength achieved at 28 days and continued moderate gains to 90 days. SS + SH was the most significant variable, with 50% of activator content achieving the highest f’c, fr, and ft values. Within the tested ranges of NP (0–20%) and WGP (0–30%), strength showed a decreasing trend with increasing replacement due to dilution. PPF had a very minute effect on f’c but significantly improved fr and ft at 0.5% dosage because of crack-bridging. Correlation analysis confirmed that cement and SS + SH are the most dominant strength-controlling factors. The results suggest that the combined use of NP, WGP, and PPF maintains mechanical performance while reducing cement consumption, highlighting the feasibility of this hybrid alkali-activated mortar as a low-carbon construction material.

This study develops advanced machine learning (ML) algorithms to predict the tensile strength (Ft) of sustainable recycled aggregate (RA) concrete incorporating supplementary cementitious materials (SCMs—silica fume and fly ash) and polypropylene fibers (PPF). A dataset of 375 Ft results from the literature, characterized by ten input parameters (including cement content, natural and RA contents, SCM dosages, PPF percentage, water–cement ratio, superplasticizer content, and curing period), was used to train and validate two ML algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). All models demonstrated high predictive accuracy, with results consistently aligning with experimental values, though the XGBoost model outperformed the RF model, achieving superior performance with R² values of 0.9689 and 0.9632 for the training and testing datasets and lower RMSE and MAE values. To interpret the model decisions and uncover black-box insights. SHapley additive explanations (SHAP) analysis was employed, quantifying the global and local importance of each input variable on tensile strength prediction, revealing complex non-linear relationships and interactions. The findings highlight XGBoost as a robust tool for optimizing the mix design of complex sustainable concrete, while SHAP analysis revealed that curing period has the highest positive impact on predicting Ft, and W/C and RA adversely impact Ft, bridging the gap between data-driven predictions and practical engineering applications. The developed XGBoost model outperformed DNN, OGPR, and GEP in predicting. A graphical user interface (GUI) was developed to be used as a tool for predicting Ft of RA concrete containing SCMs and PPF. This approach facilitates the efficient development of high-performance, eco-friendly concrete with reduced experimental effort.

The increasing global production of plastic (P) waste presents a critical environmental challenge, while the construction industry’s demand for sustainable materials continues to grow. The building industry’s reliance on natural aggregates, a contributor to environmental degradation, requires sustainable alternatives. Utilizing plastic waste as a partial aggregate substitute in concrete offers dual advantages: preserving limited resources and redirecting waste from landfills. This research uses advanced machine learning (ML) to forecast the mechanical properties of P waste concrete. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with particle swarm optimization (PSO) were developed to predict compressive and tensile strengths of P waste concrete. A comprehensive dataset comprising 196 datapoints for compressive strength (CS) and 100 datapoints for tensile strength (TS) of P waste concrete was collected from the literature. The input parameters encompassed the plastic (P), cement (C), water-to-cement ratio (W/C), coarse aggregate (CA), fine aggregate (FA), and curing age (Age), while the outputs were CS and TS of P waste concrete. The constructed models were assessed utilizing various statistical metrics. The findings indicate that coefficient of determination of both XGBoost (CS, R² = 0.9911, and TS, R² = 0.9947) and RF (CS, R² = 0.9757, and TS, R² = 0.9737) models performed well, with XGBoost indicating better performance with fewer prediction errors. SHAP analysis emphasizes the substantial effect of P waste on concrete strength properties followed by C and Age. Furthermore, GUIs for predicting TS and CS of concrete containing P waste utilizing both RF and XGBoost models were developed. Overall, this study not only achieves superior accuracy through hybrid PSO-ML models but also contributes to sustainable construction materials and computational material science, offering a data-driven framework for optimizing mix designs that incorporate plastic waste, which can accelerate its adoption in eco-friendly engineering applications.

The rapid development in building construction has stimulated the replacement of cement in concrete with construction waste materials such as marble waste powder (MWP) and granite waste powder (GWP) to reduce the negative impact of cement production and to save natural resources. Therefore, the inclusion of these materials in concrete contributes to environmental sustainability by reducing cement consumption and promoting the reuse of industrial waste. The present study employs Response Surface Methodology (RSM) and, for the first time in a comparable context, the Neural Network Algorithm (NNA) as an advanced optimization and predictive tool to evaluate the synergistic effect of using GWP and MWP as partial cement replacements in concrete exposed to elevated temperatures. The study involved four independent variables: replacement level of GWP up to 9%, replacement level of MWP up to 9%, the degree of temperature (T) up to 800 °C, and the exposure duration (D) up to 2 h, while the dependent variable (output) was the compressive strength (CS). The ANOVA results revealed that the quadratic model outperformed the linear model in predicting the CS of concrete. The Quadratic model, derived from RSM, demonstrated superior performance in predicting CS values. However, the NNA model also showed high predictive accuracy (R² = 0.949; RMSE = 1.5297 MPa), effectively capturing the complex and nonlinear relationships among temperature, duration, and the cement replacement levels with GWP and MWP. The optimization results revealed that the maximum compressive strength of 39.4 MPa can be achieved at 8.92% GWP, 1.89% MWP, T of 247 °C, and D of 0.64 h with a desirability of 1. The proposed models provided valuable insights into the synergistic effects of granite and marble waste powders, supporting the design of sustainable, high-performance concrete with reduced environmental footprint and improved resource efficiency.

The use of recycled coarse aggregate (RCA) concrete and polypropylene fibers (PPFs) presents a sustainable alternative in concrete production. However, the non-linear and interactive effects of RCA and PPF on both fresh and hardened properties are not yet fully quantified. This study employs Response Surface Methodology (RSM) and the Random Forest (RF) algorithm with K-fold cross-validation to predict the combined effect of using recycled coarse aggregate (RCA) as a partial replacement for natural coarse aggregate and polypropylene fiber (PPF) on the engineering properties of RCA-PPF concrete, addressing the critical need for a robust, data-driven modeling framework. A dataset of 144 tested samples obtained from literature was utilized to develop and validate the prediction models. Three input variables were considered in developing the proposed prediction models, namely, RCA, PPF, and curing age (Age). The examined responses were compressive strength (CS), tensile strength (TS), ultrasonic pulse velocity (UPV), and water absorption (WA). To assess the developed models, statistical metrics were calculated, and analysis of variance (ANOVA) was employed. Afterwards, the responses were optimized using optimization in RSM. The optimal results of responses by maximizing TS, CS, and UPV and minimizing WA were achieved at a PPF of 3% by volume of concrete and an RCA of approximately 100% replacing natural coarse aggregate, highlighting optimal reuse of recycled aggregate, with an AGE of 83.6 days. The RF model demonstrated superior performance, significantly outperforming the RSM model. Feature importance analysis via SHAP values was employed to identify the most effective parameters on the predictions. The results confirm that ML techniques provide a powerful and accurate tool for optimizing sustainable concrete mixes.