Kinnunen, J., Saunila, M., Ukko, J. & Rantanen, H. Strategic sustainability in the construction industry: impacts on sustainability performance and brand. J. Clean. Prod. 368, 133063 (2022).
Ansari, S. S., Ansari, H., Khateeb, A. & Ibrahim, S. M. Comparative study of machine learning models for predicting the compressive strength of concrete using Non-Destructive testing methods. Mater. Today Proc. https://doi.org/10.1016/j.matpr.2024.04.009 (2024).
Mohammad, T., Ibrahim, S. M., Ansari, S. S. & Rehman, A. U. Feasibility study and optimization of limestone calcined clay composites for compressive strength using multi-layered explainable artificial intelligence models. Mater. Today Commun. 40, 109676 (2024).
Google Scholar
Ng, S. & Engelsen, C. J. Construction and demolition wastes. Waste and Supplementary Cementitious Materials in Concrete: Characterisation, Properties and Applications 229–255 (2018). https://doi.org/10.1016/B978-0-08-102156-9.00008-0.
Almutairi, A. D., Elsayed, M., Abdelaziz, M. M. & Dahish, H. A. Punching shear capacity of rubberized high strength concrete slabs containing waste glass powder as cement substitute. Structures 78, 109222 (2025).
Elsayed, M., Almutairi, A. D., Hussein, M. & Dahish, H. A. Axial capacity of rubberized RC short columns comprising glass powder as a partial replacement of cement. Structures 64, 106612 (2024).
Saeed, M. K., Rahman, M. K., Alfawzan, M., Basha, S. & Dahish, H. A. Investigating the potential use of date kernel Ash (DKA) as a partial cement replacement in concrete. Materials 15, (2022).
Mahmoud, A. A. et al. Synergizing machine learning and experimental analysis to predict post-heating compressive strength in waste concrete. (2025). https://doi.org/10.1002/suco.202400211.
Abd Elmoaty, A. E. M. Mechanical properties and corrosion resistance of concrete modified with granite dust. Constr. Build. Mater. 47, 743–752 (2013).
Taji, I. et al. Application of statistical analysis to evaluate the corrosion resistance of steel rebars embedded in concrete with marble and granite waste dust. J. Clean. Prod. 210, 837–846 (2019).
Google Scholar
Ghorbani, S. et al. Mechanical and durability behaviour of concrete with granite waste dust as partial cement replacement under adverse exposure conditions. Constr. Build. Mater. 194, 143–152 (2019).
Google Scholar
Corinaldesi, V., Moriconi, G. & Naik, T. R. Characterization of marble powder for its use in mortar and concrete. Constr. Build. Mater. 24, 113–117 (2010).
Rana, A., Kalla, P. & Csetenyi, L. J. Sustainable use of marble slurry in concrete. J. Clean. Prod. 94, 304–311 (2015).
Google Scholar
Zhang, S. et al. Effect of silica fume and waste marble powder on the mechanical and durability properties of cellular concrete. Constr. Build. Mater. 241, 117980 (2020).
Google Scholar
Rashwan, M. A., Al – Basiony, T. M., Mashaly, A. O. & Khalil, M. M. Behaviour of fresh and hardened concrete incorporating marble and granite sludge as cement replacement. J. Building Eng. 32, 101697 (2020).
Talah, A., Kharchi, F. & Chaid, R. Influence of marble powder on high performance concrete behavior. Procedia Eng. 114, 685–690 (2015).
Google Scholar
Pereira, M. M. L., Capuzzo, V. M. S. & de Lameiras, R. Evaluation of use of marble and granite cutting waste to the production of self-compacting concrete. Constr. Build. Mater. 345, 128261 (2022).
Nega, D. M., Yifru, B. W., Taffese, W. Z., Ayele, Y. K. & Yehualaw, M. D. Impact of partial replacement of cement with a blend of marble and granite waste powder on mortar. Appl. Sci. 13, 8998 (2023).
Google Scholar
Mahmoud, A. A. et al. Investigating the effects of granite, marble, granodiorite, and ceramic waste powders on the physical, mechanical, and radiation shielding performance of sustainable concrete. Ann. Nucl. Energy. 216, 111274 (2025).
Google Scholar
Ansari, S. S., Azeem, A., Asad, M., Zafar, K. & Ibrahim, S. M. Comparative analysis of conventional and ensemble machine learning models for predicting split tensile strength in thermal stressed SCM-blended lightweight concrete. Mater. Today Proc. https://doi.org/10.1016/j.matpr.2024.04.081 (2024).
Elsayed, M., Almutairi, A. D., Azzam, E. O. A., Dahish, H. A. & Gomaa, M. S. Performance of rubberized reinforced concrete columns at ambient and high temperatures. Case Stud. Constr. Mater. 19, e02605 (2023).
Dahish, H. A. & Almutairi, A. D. Effect of elevated temperatures on the compressive strength of nano-silica and nano-clay modified concretes using response surface methodology. Case Stud. Constr. Mater. 18, e02032 (2023).
Arioz, O. Effects of elevated temperatures on properties of concrete. Fire Saf. J. 42, 516–522 (2007).
Google Scholar
Lublóy, É., Kopecskó, K., Balázs, G. L., Restás, Á. & Szilágyi, I. M. Improved fire resistance by using Portland-pozzolana or Portland-fly Ash cements. J. Therm. Anal. Calorim. 129, 925–936 (2017).
Malik, M., Bhattacharyya, S. K. & Barai, S. V. Thermal and mechanical properties of concrete and its constituents at elevated temperatures: A review. Constr. Build. Mater. 270, 121398 (2021).
Azfar Shaida, M. et al. Prediction of CO2 uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence. Fuel 380, 133183 (2025).
Google Scholar
Al-Saraireh, M. A. Predicting compressive strength of concrete with fly ash, Metakaolin and silica fume by using machine learning techniques. Latin Am. J. Solids Structures 19, (2022).
Mohammed, A. S. et al. Modeling the impact of liquid polymers on concrete stability in terms of a slump and compressive strength. Appl. Sci. 13, 1208 (2023).
Google Scholar
Ahmed, H. U., Mohammed, A. S., Faraj, R. H., Qaidi, S. M. A. & Mohammed, A. A. Compressive strength of geopolymer concrete modified with nano-silica: experimental and modeling investigations. Case Stud. Constr. Materials 16, (2022).
Alkharisi, M. K., Dahish, H. A. & Youssf, O. Prediction models for the hybrid effect of nano materials on radiation shielding properties of concrete exposed to elevated temperatures. Case Stud. Constr. Mater. 21, e03750 (2024).
Ashrafian, A., Shahmansouri, A. A., Bengar, A., Behnood, A. & H. & Post-fire behavior evaluation of concrete mixtures containing natural zeolite using a novel metaheuristic-based machine learning method. Archives Civil Mech. Eng. 22, 101 (2022).
Dahish, H. A. & Almutairi, A. D. Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures. Results Eng. 103975 https://doi.org/10.1016/j.rineng.2025.103975 (2025).
Wani, S. R. & Suthar, M. Using machine learning approaches for predicting the compressive strength of ultra-high-performance concrete with SHAP analysis. Asian J. Civil Eng. 26, 373–388 (2025).
Yuan, Y., Yang, M., Shang, X., Xiong, Y. & Zhang, Y. Predicting the compressive strength of UHPC with coarse aggregates in the context of machine learning. Case Stud. Constr. Mater. 19, e02627 (2023).
Sobuz, M. H. R. et al. Assessing the influence of sugarcane Bagasse Ash for the production of eco-friendly concrete: experimental and machine learning approaches. Case Stud. Constr. Mater. 20, e02839 (2024).
Ganesh, A. C. et al. Development of alkali activated paver blocks for medium traffic conditions using industrial wastes and prediction of compressive strength using random forest algorithm. Sci. Rep. 13, 15152 (2023).
Google Scholar
Thisovithan, P., Aththanayake, H., Meddage, D. P. P., Ekanayake, I. U. & Rathnayake, U. A novel explainable AI-based approach to estimate the natural period of vibration of masonry infill reinforced concrete frame structures using different machine learning techniques. Results Eng. 19, 101388 (2023).
Saleem, M., Al, Harrou, F. & Sun, Y. Explainable machine learning methods for predicting water treatment plant features under varying weather conditions. Results Eng. 21, 101930 (2024).
Google Scholar
Amin, M. N. et al. Investigating the compressive property of foamcrete and analyzing the feature interaction using modeling approaches. Results Eng. 24, 103305 (2024).
Google Scholar
Harirchian, E., Aghakouchaki Hosseini, S. E., Novelli, V., Lahmer, T. & Rasulzade, S. Utilizing advanced machine learning approaches to assess the seismic fragility of non-engineered masonry structures. Results Eng. 21, 101750 (2024).
Isleem, H. F. et al. Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading. Results Eng. 19, 101341 (2023).
Saaidi, A., Bichri, A. & Abderafi, S. Efficient machine learning model to predict dynamic viscosity in phosphoric acid production. Results Eng. 18, 101024 (2023).
Google Scholar
Alkharisi, M. K. & Dahish, H. A. The application of response surface methodology and machine learning for predicting the compressive strength of recycled aggregate concrete containing polypropylene fibers and supplementary cementitious materials. Sustainability 17, 2913 (2025).
Google Scholar
Safhi, A., Dabiri, M., Soliman, H. & Khayat, K. H. el, A. Prediction of self-consolidating concrete properties using XGBoost machine learning algorithm: Part 1–Workability. Constr Build Mater 408, 133560 (2023).
Alyami, M. et al. Estimating compressive strength of concrete containing rice husk Ash using interpretable machine learning-based models. Case Stud. Constr. Mater. 20, e02901 (2024).
Zhang, J., Wang, R., Lu, Y. & Huang, J. Prediction of compressive strength of geopolymer concrete landscape design: application of the novel hybrid RF–GWO–XGBoost algorithm. Buildings 14, 591 (2024).
Sun, C., Wang, K., Liu, Q., Wang, P. & Pan, F. Machine-Learning-Based comprehensive properties prediction and mixture design optimization of Ultra-High-Performance concrete. Sustainability 15, 15338 (2023).
Google Scholar
Kurniati, E. O., Zeng, H., Latypov, M. I. & Kim, H. J. Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials. Case Stud. Constr. Mater. 21, e03373 (2024).
Chen, Q., Hu, G. & Wu, J. Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models. Case Stud. Constr. Mater. 21, e03439 (2024).
Das, P., Kashem, A., Hasan, I. & Islam, M. A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis. Asian J. Civil Eng. 25, 3301–3316 (2024).
Ansari, S. S., Ibrahim, S. M. & Hasan, S. D. Interpretable Machine-Learning models to predict the flexural strength of Fiber-Reinforced SCM-Blended concrete composites. J. Struct. Des. Constr. Pract. 30, (2025).
Singh, B., Sihag, P., Tomar, A. & Sehgad, A. Estimation of compressive strength of High-Strength concrete by random forest and M5P model tree approaches. J. Mater. Eng. Struct. 6 (2019).
Zhang, J. et al. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Constr. Build. Mater. 210, 713–719 (2019).
Nguyen-Sy, T. et al. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Constr. Build. Mater. 260, 119757 (2020).
Kocamaz, A. F., Ayaz, Y., Karakoç, M. B., Türkmen, İ. & Demirboğa, R. Prediction of compressive strength and ultrasonic pulse velocity of admixtured concrete using tree model M5P. Structural Concrete 22, (2021).
Emad, W. et al. Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures. Constr. Build. Mater. 349, 128737 (2022).
Li, H., Lin, J., Lei, X. & Wei, T. Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm. Mater. Today Commun. 30, 103117 (2022).
Google Scholar
Ahmed, H. U., Abdalla, A. A., Mohammed, A. S. & Mohammed, A. A. Mathematical modeling techniques to predict the compressive strength of high-strength concrete incorporated Metakaolin with multiple mix proportions. Clean. Mater. 5, 100132 (2022).
Google Scholar
Chen, G., Suhail, S. A., Bahrami, A., Sufian, M. & Azab, M. Machine learning-based evaluation of parameters of high-strength concrete and Raw material interaction at elevated temperatures. Front Mater 10, (2023).
Ibrahim, S. M., Ansari, S. S. & Hasan, S. D. Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI). Appl. Soft Comput. 149, 110997 (2023).
Gad, M. A., Nikbakht, E. & Ragab, M. G. Predicting the compressive strength of engineered geopolymer composites using automated machine learning. Constr. Build. Mater. 442, 137509 (2024).
Google Scholar
Harith, I. K., Abdulhadi, A. M. & Hussien, M. L. Harnessing machine learning for accurate Estimation of compressive strength of high-performance self-compacting concrete from non-destructive tests: A comparative study. Constr. Build. Mater. 451, 138779 (2024).
Wani, S. R. & Suthar, M. A. Comparative analysis of the predictive performance of Tree-Based and artificial neural network approaches for compressive strength of concrete utilising waste. Int. J. Pavement Res. Technol. https://doi.org/10.1007/s42947-024-00454-8 (2024).
Wani, S. R. & Suthar, M. Using soft computing to forecast the strength of concrete utilized with sustainable natural fiber reinforced polymer composites. Asian J. Civil Eng. 25, 5847–5863 (2024).
Ansari, M. A. et al. Incorporating non-destructive UPV into machine learning models for predicting compressive strength in SCM concrete. Mater. Today Proc. https://doi.org/10.1016/J.MATPR.2024.04.059 (2024).
Ansari, S. S., Ansari, M. A., Saqib, M., Ghazi, M. S. & Ibrahim, S. M. Impact of thermal loads on silica fume-modified lightweight concrete: machine learning approach to assess compressive strength evolution. Mater. Today Proc. https://doi.org/10.1016/J.MATPR.2024.04.054 (2024).
Yu, X. et al. Data-driven prediction of compressive strength for ultra-high performance concrete exposed to elevated temperatures. Mater. Today Commun. 42, 111518 (2025).
Google Scholar
ASTM International. Standard Specification for Portland cement (ASTM C150 / C150M – 20). ASTM International: West Conshohocken, PA, USA. (2020).
ASTM International. Standard Specification for Concrete Aggregates (ASTM C33/C33M-18). ASTM International: West Conshohocken, PA, USA. (2018).
Fathy, I. N., Elfakharany, M. E. & El-Sayed, A. A. Recycling of waste granodiorite powder as a partial cement replacement material in ordinary concrete. Adv. Mater. Sci. 24, 56–88 (2024).
Google Scholar
Sciarretta, F., Eslami, J., Beaucour, A. L. & Noumowé, A. State-of-the-art of construction stones for masonry exposed to high temperatures. Constr. Build. Mater. 304, 124536 (2021).
Abouelnour, M. A. et al. Recycling of marble and granite waste in concrete by incorporating nano alumina. Constr. Build. Mater. 411, 134456 (2024).
Google Scholar
Mohammed Haneefa, K., Santhanam, M. & Parida, F. C. Review of concrete performance at elevated temperature and hot sodium exposure applications in nuclear industry. Nucl. Eng. Des. 258, 76–88 (2013).
Google Scholar
Wani, S. R. & Suthar, M. Utilizing machine learning approaches within concrete technology offers an intelligent perspective towards sustainability in the construction industry: a comprehensive review. Multiscale Multidisciplinary Model. Experiments Des. 8, 1 (2025).
Khan, M. A. et al. Simulation of depth of wear of Eco-Friendly concrete using machine learning based computational approaches. Materials 15, 58 (2021).
Google Scholar
Dahish, H. A. & Alkharisi, M. K. Hybrid Fiber reinforcement in HDPE-Concrete: predictive analysis of fresh and hardened properties using response surface methodology. (2024). https://doi.org/10.3390/buildings14113479.
Kutner, M. H., Nachtsheim, C., Neter, J. & Li, W. Applied Linear Statistical Models (McGraw-Hill Irwin, 2005).
Sun, Y., Li, G., Zhang, J. & Qian, D. Prediction of the strength of rubberized concrete by an evolved random forest model. Adv. Civil Eng. 2019, 1–7 (2019).
Song, H. et al. Predicting the compressive strength of concrete with fly Ash admixture using machine learning algorithms. Constr. Build. Mater. 308, 125021 (2021).
Google Scholar
Wang, Y. & Witten, I. H. Induction of Model Trees for Predicting Continuous Classes. Computer Science Working Papers (1996). https://hdl.handle.net/10289/1183
Quinlan, J. R. Learning with Continuous Classes. Proceedings of Australian Joint Conference on Artificial Intelligence. Hobart 16–18 343–348 (1992).
Li, Z., Lu, T., He, X., Montillet, J. P. & Tao, R. An improved Cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series. Adv. Space Res. 71, 912–935 (2023).
Google Scholar
Wu, J., Ma, D. & Wang, W. Leakage identification in water distribution networks based on XGBoost algorithm. J Water Resour. Plan. Manag 148, (2022).
Wang, T., Bian, Y., Zhang, Y. & Hou, X. Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm. Comput. Geosci. 170, 105242 (2023).
Chakraborty, D., Awolusi, I. & Gutierrez, L. An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete. Results Eng. 11, 100245 (2021).
Elsayed, M., Almutairi, A. D. & Dahish, H. A. Effect of elevated temperatures on the residual capacity of rubberized RC columns containing waste glass powder. Case Stud. Constr. Mater. 20, e02944 (2024).
Dahish, H. A., Bakri, M. & Alfawzan, M. S. Predicting the strength of cement mortars containing natural Pozzolan and silica fume using multivariate regression analysis. Int. J. GEOMATE. 20, 68–76 (2021).
Dahish, H. A. Predicting the compressive strength of concrete containing crumb rubber and recycled aggregate using response surface methodology. International J. GEOMATE 24, (2023).
Dahish, H. A., Alfawzan, M. S., Tayeh, B. A., Abusogi, M. A. & Bakri, M. Effect of inclusion of natural Pozzolan and silica fume in cement – based mortars on the compressive strength utilizing artificial neural networks and support vector machine. Case Stud. Constr. Mater. 18, e02153 (2023).
Quan Tran, V. Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials. Constr. Build. Mater. 328, 127103 (2022).
Google Scholar
