Analyzing the compressive strength, eco-strength, and cost-strength ratio of agro-waste-derived concrete using advanced machine learning methods Muhammad Nasir Amin, Bawar Iftikhar, Kaffayatullah Khan, Muhammad Tahir Qadir Reviews on Advanced Materials Science, 2025 Agro-waste like eggshell powder (ESP) and date palm ash (DPA) are used as supplementary cementitious materials (SCMs) in concrete because of their pozzolanic and cementitious attributes as well as environmental and cost benefits. In addition, performing lab tests to optimize mixed proportions of concrete with different SCMs takes considerable time and effort. Therefore, the creation of estimation models for such purposes is vital. This study aimed to create interpretable prediction models for the compressive strength (CS), eco-strength (ECR), and cost–strength ratio (CSR) of DPA–ESP concrete. Gene expression programming (GEP) was employed for model generation via the hyperparameter optimization method. Also, the importance of input features was determined via SHapley Additive exPlanations (SHAP) analysis. The GEP models accurately matched experimental results for the CS, ECR, and CSR of DPA–ESP concrete. These models can be used for future predictions, reducing the need for additional tests and saving effort, time, and costs. The model’s accuracy was confirmed by an R 2 value of 0.94 for CS, as well as high values of 0.91 for ECR and 0.92 for CSR, as well as lower values for statistical checks. The SHAP analysis suggested that test age was the most critical factor in all outcomes.
Foamed geopolymers as low carbon materials for fire-resistant and lightweight applications in construction: A review Muhammad Nasir Amin, Bawar Iftikhar, Kaffayatullah Khan, Nashwan Adnan Othman, Muhammad Tahir Qadir Reviews on Advanced Materials Science, 2025 This study analyzed the research developments on foamed geopolymers (FGPs) in construction applications, aiming to evaluate advancements, challenges, and prospective future directions. Data for the review were collected using the Scopus database. The evaluation identified key publishing sources, keyword trends, leading authors in terms of citations and publications, most-cited papers, and regions actively involved in FGP research. Additionally, the study discussed the demand for FGP, the main challenges to its implementation, and potential solutions. A notable increase in publications on FGP was observed, indicating growing interest among researchers. Keyword trends emphasized the growing interest in FGPs for thermal insulation and fire-resistant applications, underscoring their potential to address critical sustainability challenges in the construction industry. An analysis of prominent authors and their extensively cited works showed the principal contributors driving innovation within this domain. The review highlighted current research gaps concerning the long-term performance and durability of FGPs when subjected to extreme environmental conditions. Furthermore, the necessity for advanced processing techniques to enhance material characteristics and cost-effectiveness for practical applications was discussed. This study might be valuable for both researchers and industry, providing recommendations to address existing gaps and promote the advancement and implementation of FGPs in sustainable construction.
Tensile behavior evaluation of two-stage concrete using an innovative model optimization approach Muhammad Nasir Amin, Faizullah Jan, Kaffayatullah Khan, Suleman Ayub Khan, Muhammad Tahir Qadir, et al. Reviews on Advanced Materials Science, 2025 Two-stage concrete (TSC) is a sustainable material produced by incorporating coarse aggregates into formwork and filling the voids with a specially formulated grout mix. The significance of this study is to improve the predictive accuracy of TSC’s tensile strength, which is essential for optimizing its use in construction applications. To achieve this objective, novel and reliable predictive models were developed using advanced machine learning algorithms, including random forest (RF) and gene expression programming (GEP). The performance of these models was evaluated using important evaluation metrics, including the coefficient of determination (R 2), mean absolute error (MAE), mean squared error, and root mean square error (RMSE), after they were trained on a comprehensive dataset. The results suggest that the RF model outperforms the GEP model, as evidenced by a higher R 2 value of 0.94 relative to 0.91 for GEP and reduced MAE and RMSE error values. This suggests that the RF model has a superior predictive capability. Additionally, sensitivity analyses and SHapley Additive ExPlanation analysis revealed that the water-to-binder (W/B) ratio was the most influential input parameter, accounting for 51.01% of the predictive outcomes presented in the model. This research emphasizes optimizing TSC design, enhancing material performance, and promoting sustainable, cost-effective construction.
Advanced explainable models for strength evaluation of self-compacting concrete modified with supplementary glass and marble powders Kaffayatullah Khan, Muhammad Ehsan Ullah Khan, Ahmed A. Alawi Al-Naghi, Muhammad Nasir Amin, Bawar Iftikhar, et al. Reviews on Advanced Materials Science, 2025 Self-compacting concrete (SCC) is increasingly adopted in modern construction due to its self-flowing nature, which eliminates the need for mechanical vibration and enhances construction quality. The use of industrial waste materials like marble powder (MP) and glass powder (GP) in SCC presents a sustainable alternative to conventional materials, reducing environmental impact. However, predicting the compressive strength (CS) of such mixes through traditional testing methods is time-consuming, costly, and limits rapid mix optimization. This motivates the adoption of machine learning (ML) techniques, which can efficiently analyze complex datasets and identify patterns that influence concrete performance. In this study, three ML models, gradient boosting, bagging regression, and random forest (RF), were used to predict the CS of SCC incorporating MP and GP. Among them, RF achieved the highest accuracy (R² = 0.95). Model interpretability was ensured through Shapley Additive exPlanations, partial dependence plots, and individual conditional expectation analyses, which identified curing time as the most influential feature. The Taylor plot and validation metrics confirmed RF’s superior reliability. This research highlights the potential of ML not only as a predictive tool but also as a means of understanding key factors in sustainable mix design, ultimately promoting smarter and greener construction practices.
Life-cycle assessment of using sulfur-extended asphalt (SEA) in pavements R. Yang, H. Ozer, Y. Ouyang, A. H. Alarfaj, K. Islam, et al. Airfield and Highway Pavements 2019 Innovation and Sustainability in Highway and Airfield Pavement Technology Selected Papers from the International Airfield and Highway Pavements Conference 2019, 2019
