Civil and Structural Engineering, Building and Construction
166
Scopus Publications
Scopus Publications
Optimizing the potential use of waste palm oil clinker powder in cementitious grouts for semiflexible pavements using response surface methodology Nasir Khan, Muslich Hartadi Sutanto, Muhammad Imran Khan, Nura Shehu Aliyu Yaro, Rania Al-Nawasir, Arsalaan Khan Yousafzai, Kaffayatullah Khan, Muhammad Arsalan Khan Scientific Reports, 2026 Abstract Cement is one of the major industrial sources of global CO₂ emissions, which highlights the need for sustainable alternatives in pavement construction. In this context, the present study examines the partial substitution of cement with waste palm oil clinker powder (POCP) to develop cementitious grouts for semi-flexible pavements (SFP). In this study, POCP was used at different proportions (0–30%) across water–cement ratios (W/C) ranging from 0.40 to 0.55, in the presence of a superplasticizer to improve grout flowability. The performance of the grout was assessed by measuring flowability and compressive strength at 1, 7, and 28 days. The experiments were designed using Response Surface Methodology (RSM), which was used to analyze statistical correlations and determine the optimal POCP content and W/C. The optimized grout mixture with 20% POCP was subsequently used to produce SFP specimens (PSFP), which were assessed for marshall stability, moisture susceptibility, indirect tensile stiffness modulus, cantabro loss, and resistance to fuel spillage, and then compared with control SFP (CSFP) and conventional hot mix asphalt (HMA). Microstructural analysis using Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDS) indicated that the addition of POCP increased the porosity relative to the control sample. The marshall stability for both CSFP and PSFP was more than twice that of conventional HMA with a moisture-induced loss of about 9%, compared to about 16% in HMA. However, the rigid nature of SFP and the interface between the grout and bitumen-coated aggregates, which creates a weak zone, led to greater mass loss under impact loading than in HMA, highlighting the need for higher viscoelasticity in SFP samples.
Synergistic effects of rice husk ash and extracted microsilica on the performance of high-strength concrete Muhammad Fahad Ullah, Hesheng Tang, Arshad Ullah, Kaffayatullah Khan Scientific Reports, 2025 The construction industry's urgent need for sustainable alternatives to ordinary Portland cement (OPC) has spurred significant interest in supplementary cementitious materials (SCMs). Therefore, this study investigates the synergistic effects of rice husk ash (RHA) and extracted microsilica (EMS) as partial replacements for cement in high-strength concrete (HSC). RHA was produced by controlled combustion of rice husk (RH) at 600-700 °C, while EMS was obtained through an optimized extraction process. Along with control mix (CM), three ternary mixes incorporating 15-35% RHA and 5-15% EMS were investigated to evaluate their effects on the mechanical and durability properties, as well as microstructural characteristics. Experimental results demonstrated that samples containing 20% (5% EMS + 15% RHA) and 35% (10% EMS + 25% RHA) as cement replacement exhibited enhanced performance, increasing the 28-day compressive strength by 11.56% and 5.98%, respectively, as compared to conventional concrete. Durability assessments of the optimal mixes revealed a significant reduction in water absorption (WA) and permeability. Physiochemical characterization confirmed the increased formation of dense calcium silicate hydrate (CSH) gels and a reduction in portlandite content, indicating high pozzolanic reactivity. However, the 50% replacement mix (15% EMS + 35% RHA) showed inferior performance due to the particle agglomeration and higher porosity. This study shows that the strategically formulated combinations of EMS and RHA blends (up to 35%) can produce sustainable HSC with improved mechanical properties and durability while mitigating environmental impacts. These findings provide valuable insights for advancing eco-friendly construction materials, supporting global sustainability initiatives in infrastructure development.
Machine learning models for mechanical properties prediction of basalt fiber-reinforced concrete incorporating graphical user interface Md Ehtesamul Haque, Md Arifuzzaman, Kaffayatullah Khan, A. K. M. Azad, Ayed Eid Alluqmani, Abul Kashem Scientific Reports, 2025 One of the most significant advancements in basalt fiber (BF) technology is its application in Basalt fiber reinforced polymers (BFRP). The production of BFRP utilizes basalt rock, a naturally abundant resource, resulting in a composite that generates approximately 74% less carbon emissions compared to traditional steel, aligning with global sustainability goals. This study employs previously published experimental datasets on basalt fiber-reinforced concrete (BFRC) to statistically predict compressive strength (CS) and splitting tensile strength (STS) using advanced machine learning. The dataset includes 270 CS and 267 STS samples, split into 70% training and 30% testing, enabling accurate, data-driven predictions without the need for new laboratory experiments. In addition to the parametric analysis of Shapley additive explanation (SHAP), machine learning models were used, namely support vector regression (SVR), random forest regression (RFR), decision tree (DT), bagging regressor (BR), and gradient boosting regression (GBR) with grid search hyper-tuning. Additionally, the model generated SHAP interaction plots to show the impact of each characteristic on an individual prediction. The results found that GBR model performance is the most precise prediction of compressive strength compared to other models, achieving an R 2 of 0.99 for training phase and R 2 of 0.86 for testing phase. But SVR model outperforms the other four models in STS prediction, with the coefficient of determination (R 2 ) value of 0.99 during the training stage and R 2 of 0.97 for the testing stage. The Shapley additive explanations (SHAP) method was used to display the effect of each input parameters on model prediction. The cement and silica fume were found to have the highest positive influence on BFRC compressive and tensile strength. The basalt fiber (BF) diameter as an input parameter was found to have the highest effect on STC. Finally, the concrete designers can now easily and affordably predict CS and STS using a graphical user interface, without conducting expensive computations or experiments.
Modeling and comparative analysis of sustainable cotton rope confinement: Full vs. STrip wrapping for enhanced concrete strength and ductility Panumas Saingam, Chaitanya Krishna Gadagamma, Qudeer Hussain, Hnin Hnin Hlaing, Rawirot Suwannatrai, Muhammad Irshad Qureshi, Kaffayatullah Khan, Ali Ejaz Case Studies in Construction Materials, 2025 The rise of natural FRPs as ecological alternatives to synthetic ones has highlighted the need for studies on partial confinement using cotton ropes, which offer cost-effective, low-carbon solutions with high rupture strain. Unlike full confinement, partial confinement through cotton rope strips can efficiently strengthen deteriorated concrete sections with reduced material usage. Despite possessing several advantages, the partial confinement by cotton on concrete has not been investigated. This study addresses the gap by investigating the performance of cotton rope strips and developing analytical models to predict their structural impact. This study tested cylindrical concrete specimens of two strengths, strengthened with cotton rope in either complete wrapping (Group 1) or strip wrapping (Group 2). Each group was further divided by concrete strength and included one unstrengthened specimen, and three strengthened with one, two, or three layers of cotton rope. Experimental results revealed that cotton rope wraps effectively confined the concrete, enhancing load-bearing capacity and improving ultimate compressive strength by 9.97–152.10 % and ultimate strain by 188.00 % to 1488.89 %. The compressive stress vs. strain behavior exhibited an initial stiff elastic ascent followed by a parabolic transition. The second branch of the response, either ascending or descending, was significantly improved with an increased confinement ratio. Type-I failure was characterized by an ascending second branch in the compressive stress vs. strain curve, while Type-II failure exhibited a descending second branch. Four specimens demonstrated Type-I failure (L-2F, L-3F, H-3F, and L-3S), predominantly in full configurations and with lower unconfined compressive strength. The modulus of the second branch improved with an increased confinement ratio, transitioning from Type-II to Type-I failure near a ratio of approximately 0.50. Regression analysis provided equations of various key points along the compressive response with R² values greater than 0.90, highlighting a strong dependence on the confinement ratio. The Popovics model effectively predicted the first part of the compressive response, with predicted curves closely matching experimental results.
Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete Muhammad Nasir Amin, Ayaz Ahmad, Kaffayatullah Khan, Muhammad Tahir Qadir Journal of Environmental Management, 2025 Foamed concrete (FC) is increasingly used in modern construction due to its lightweight nature, superior thermal insulation , and sustainable properties. However, accurately predicting its compressive strength remains a challenge due to the complex interactions of its components. This study addresses this gap by employing advanced machine learning tools, including decision tree (DT), bagging, and AdaBoost, to develop predictive models for FC strength . The results provide a significant improvement in prediction accuracy, offering a reliable tool for optimizing FC design in construction applications. This research aims to streamline the sample creation process in the laboratory, minimize the waiting time for sample testing, and reduce the project's overall cost for researchers. A total of 149 data points were used from the literature to prepare a proper data set for modelling purposes. The modelling procedure used Python code via the Anaconda Navigator software. The statistical evaluation of the metrics, such as R 2 , MAE , and RMSE , along with the sensitivity analysis to check the impact of inputs and the 10-fold cross-validation method to validate the performance, were part of the presented research. Compared to the DT and bagging models, the results demonstrate that the AdaBoost model forecasts FC's compressive strength (CS) more accurately. The AdaBoost model gives the R 2 value equal to 0.97, while DT and bagging show 0.86 and 0.94, respectively. The lower error result for the AdaBoost model and higher for both DT and bagging indicates the superior precision level of the AdaBoost approach. Finally, the graphical user interface (GUI) was designed utilizing the implemented models, which indicates the additional positive aspect of the presented study.
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, Marcin Kujawa 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, Muhammad Tahir Qadir 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.
Explicable AI-based modeling for the compressive strength of metakaolin-derived geopolymers Ling Liu, Yan Tao Du, Muhammad Nasir Amin, Sohaib Nazar, Kaffayatullah Khan, Muhammad Tahir Qadir Case Studies in Construction Materials, 2024 Geopolymers (GPs) are produced using a variety of alumina and silica-rich ingredients, including natural sources, such as calcined clays, as well as waste by-products, like fly ash and slag. It is difficult to measure the effect of each parameter on the strength of GP composites via experimental research. In this regard, this research aimed to build artificial intelligence (AI)-aided estimation models to quantify the impact of mix proportions on the compressive strength (CS) of MK-based GP mortar. Gene expression programming (GEP) and multi-expression programming (MEP) tools were used for models' development due to their advantage of yielding model equations for future predictions. Hyperparameters in GEP and MEP were systematically adjusted to enhance the models' optimal predictability. The regression and error analysis proved the superiority of MEP models over GEP models. In comparison to the GEP model, which had R 2 , MAE, MSE, RMSE, and objective function values of 0.90, 4.02, 25.3, 5.03, and 0.08, respectively, the MEP model demonstrated greater accuracy with values of 0.96, 3.24, 16.4, 4.05, and 0.02 respectively. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to determine the effect of various parameters on the CS of MK-based GP mortar. The established models' equations may be exploited to assess the CS of MK-based GP composites with various input parameters, hence reducing the need for further laboratory testing. Implementing this approach not only improves the efficiency of material design but also encourages the sustainable utilization of locally accessible resources in the manufacturing of geopolymers.
Life-cycle assessment of using sulfur-extended asphalt (SEA) in pavements R. Yang, H. Ozer, Y. Ouyang, A. H. Alarfaj, K. Islam, M. I. Khan, K. M. Khan, F. I. Shalabi 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
