Prediction of Geopolymer concrete compressive and flexural strengths from extended mix parameters using comparative machine learning models
DOI:
https://doi.org/10.66224/JCER.8.3.39Keywords:
Geopolymer concrete; Machine learning; Compressive strength; Flexural strength; Sustainable construction.Abstract
Ordinary Portland cement has high energy and CO₂ footprint, motivating the use of geopolymer concrete as a lower-carbon structural binder. However, geopolymer mix design still relies on slow trial-and-error that does not achieve target compressive and flexural strengths. Prior studies used machine learning to predict geopolymer strength, but mostly with few input variables, compressive-strength-only targets and limited algorithm comparison. Hence, there is no clear evidence on which model best predicts strengths for extended geopolymer mix designs. For this study, a database of 497 geopolymer mixes with fourteen mix and curing variables was compiled from literature, and four supervised models’ artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and classification and regression tree (CART) were trained on normalized data with tuned hyper-parameters. Model performance was evaluated using correlation coefficient, root mean square error and mean absolute error for separate compressive and flexural targets. All four models achieved high predictive accuracy on test data, with ANN giving the best compressive-strength performance. For flexural strength, SVM provided the most accurate predictions. Different mechanical responses of geopolymer concrete are best represented by different model structures and that multi-parameter machine-learning frameworks can markedly reduce empirical trial batches. Overall, developed comparative machine-learning framework directly addresses limitations of trial-and-error geopolymer mix design by providing practical tool to predict compressive and flexural strengths from mix parameters and to support performance-based, low-carbon mix design. Future work should expand the database to other mechanical and durability properties and embed tools in decision-support software.
References
[1] Abdelmonim, A., & Bompa, D. V. (2021). Mechanical and Fresh Properties of Multi-Binder Geopolymer Mortars Incorporating Recycled Rubber Particles. Infrastructures, 6(10), 146. https://doi.org/10.3390/infrastructures6100146
[2] Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727. https://doi.org/10.1016/s0731-7085(99)00272-1
[3] Ahmad, A., Ahmad, W., Aslam, F., & Joyklad, P. (2022). Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Studies in Construction Materials, 16. https://doi.org/10.1016/j.cscm.2021.e00840
[4] Ahmad, A., Ahmad, W., Chaiyasarn, K., Ostrowski, K., Aslam, F., Zajdel, P., & Joyklad, P. (2021). Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms. Polymers.
[5] Ahmed, A., Kumar, S. S., & Nanda, R. P. (2021). Development of geopolymer concrete mixes with ambient air curing. IOP Conference Series: Materials Science and Engineering, 1116(1), 12160. https://doi.org/10.1088/1757-899x/1116/1/012160
[6] Albitar, M., Mohamed Ali, M. S., Visintin, P., & Drechsler, M. (2017). Durability evaluation of geopolymer and conventional concretes. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2017.01.056
[7] Amin, M., Khan, K., Javed, M., Aslam, F., Qadir, M. G., & Faraz, M. I. (2022). Prediction of Mechanical Properties of Fly-Ash/Slag-Based Geopolymer Concrete Using Ensemble and Non-Ensemble Machine-Learning Techniques. Materials.
[8] Anuradha, R., Sreevidya, V., & Venkatasubramani, R. (2013). Microstructure of Geopolymer Concrete Based on Fly Ash Using M-Sand. Asian Journal of Chemistry, 25(2), 1095–1097. https://doi.org/10.14233/ajchem.2013.13522
[9] Asayesh, S., Shirzadi Javid, A. A., Ziari, H., & Mehri, B. (2021). Evaluating fresh state, hardened State, thermal expansion and bond properties of geopolymers for the repairing of concrete pavements under restrained conditions. Construction and Building Materials, 292, 123398. https://doi.org/10.1016/j.conbuildmat.2021.123398
[10] Awoyera, P. O., Kirgiz, M. S., Viloria, A., & Ovallos-Gazabon, D. (2020). Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques. Journal of Materials Research and Technology, 9(4). https://doi.org/10.1016/j.jmrt.2020.06.008
[11] Banerjee, S., Gelfand, A. E., Finley, A. O., & Sang, H. (2008). Gaussian Predictive Process Models for Large Spatial Data Sets. Journal of the Royal Statistical Society Series B: Statistical Methodology, 70(4), 825–848. https://doi.org/10.1111/j.1467-9868.2008.00663.x
[12] Ben Chaabene, W., Flah, M., & Nehdi, M. L. (2020). Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2020.119889
[13] Chen, Y.-C., Lee, W.-H., & Ding, Y.-C. (2021). The Use of Recycled Aggregate Sludge for the Preparation of GGBFS and Fly Ash Based Geopolymer. Crystals, 11(12), 1486. https://doi.org/10.3390/cryst11121486
[14] Cheng, M.-Y., Huang, C.-C., & Roy, A. F. Van. (2014). PREDICTING PROJECT SUCCESS IN CONSTRUCTION USING AN EVOLUTIONARY GAUSSIAN PROCESS INFERENCE MODEL. Journal of Civil Engineering and Management, 19(Supplement_1), S202–S211. https://doi.org/10.3846/13923730.2013.801919
[15] da Paixão, R. C. F., Penido, R. E. K., Cury, A. A., & Mendes, J. C. (2022). Comparison of machine learning techniques to predict the compressive strength of concrete and considerations on model generalization. Revista IBRACON de Estruturas e Materiais, 15(5). https://doi.org/10.1590/S1983-41952022000500003
[16] Davidovits, J. (1994). Properties of Geopolymer Cements. First International Conference on Alkaline Cements and Concretes.
[17] de Brito, J., & Kurda, R. (2021). The past and future of sustainable concrete: A critical review and new strategies on cement-based materials. Journal of Cleaner Production, 281, 123558. https://doi.org/10.1016/j.jclepro.2020.123558
[18] Deb, P. S., Nath, P., & Sarker, P. K. (2014). The effects of ground granulated blast-furnace slag blending with fly ash and activator content on the workability and strength properties of geopolymer concrete cured at ambient temperature. Materials & Design (1980-2015), 62, 32–39. https://doi.org/10.1016/j.matdes.2014.05.001
[19] Ding, Y., Shi, C.-J., & Li, N. (2018). Fracture properties of slag/fly ash-based geopolymer concrete cured in ambient temperature. Construction and Building Materials, 190, 787–795. https://doi.org/10.1016/j.conbuildmat.2018.09.138
[20] Duxson, P., Fernández-Jiménez, A., Provis, J. L., Lukey, G. C., Palomo, A., & van Deventer, J. S. J. (2006). Geopolymer technology: the current state of the art. Journal of Materials Science, 42(9), 2917–2933. https://doi.org/10.1007/s10853-006-0637-z
[21] Esparham, A., & Mohammadi, M. (2022). Effect of Alkaline Solutions on Bentonite-Based Eco-Friendly Geopolymer Composite. ALKHAS The Journal of Environment Agriculture and Biological Sciences. https://doi.org/10.47176/alkhass.4.1.5
[22] Fang, G., Ho, W. K., Tu, W., & Zhang, M. (2018). Workability and mechanical properties of alkali-activated fly ash-slag concrete cured at ambient temperature. Construction and Building Materials, 172, 476–487. https://doi.org/10.1016/j.conbuildmat.2018.04.008
[23] Faridmehr, I., Nehdi, M. L., Huseien, G. F., Baghban, M. H., Sam, A. R. M., & Algaifi, H. A. (2021). Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete. Sustainability, 13(13), 7444. https://doi.org/10.3390/su13137444
[24] Ferdous, W., Manalo, A., Khennane, A., & Kayali, O. (2015). Geopolymer concrete-filled pultruded composite beams - Concrete mix design and application. Cement and Concrete Composites. https://doi.org/10.1016/j.cemconcomp.2014.12.012
[25] Garces, J. I. T., Dollente, I. J., Beltran, A. B., Tan, R. R., & Promentilla, M. A. B. (2021). Life cycle assessment of self-healing geopolymer concrete. Cleaner Engineering and Technology, 4, 100147. https://doi.org/10.1016/j.clet.2021.100147
[26] Gunasekara, C., Sandanayake, M., Zhou, Z., Law, D. W., & Setunge, S. (2020). Effect of nano-silica addition into high volume fly ash–hydrated lime blended concrete. Construction and Building Materials, 253, 119205. https://doi.org/10.1016/j.conbuildmat.2020.119205
[27] Gupta, A., Gupta, N., & Saxena, K. K. (2021). Mechanical and Durability Characteristics Assessment of Geopolymer Composite (GPC) at Varying Silica Fume Content. Journal of Composites Science, 5(9), 237. https://doi.org/10.3390/jcs5090237
[28] Hadi, M. N. S., Farhan, N. A., & Sheikh, M. N. (2017). Design of geopolymer concrete with GGBFS at ambient curing condition using Taguchi method. Construction and Building Materials, 140, 424–431. https://doi.org/10.1016/j.conbuildmat.2017.02.131
[29] Haruna H.; Umar, I.; Shao, J.; Adamu, M.; Ibrahim, Y., S. I. . Z. (2022). Gaussian process regression model for the prediction of the compressive strength of polyurethane-based polymer concrete for runway repair: A comparative approach. IOP Conference Series: Earth and Environmental Science, 1026. https://doi.org/10.1088/1755-1315/1026/1/012007
[30] John, S. K., Cascardi, A., Nadir, Y., Aiello, M. A., & Girija, K. (2021). A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash‐Slag Geopolymer Mortar. Advances in Civil Engineering, 2021(1). https://doi.org/10.1155/2021/6662347
[31] Kanagaraj, B., N, A., Alengaram, U. J., Raj R, S., B, P., & Tattukolla, K. (2022). Performance evaluation on engineering properties and sustainability analysis of high strength geopolymer concrete. Journal of Building Engineering, 60, 105147. https://doi.org/10.1016/j.jobe.2022.105147
[32] Khokhar, S. A., Ahmed, T., Khushnood, R. A., Ali, S. M., & Shahnawaz. (2021). A Predictive Mimicker of Fracture Behavior in Fiber Reinforced Concrete Using Machine Learning. Materials, 14(24), 7669. https://doi.org/10.3390/ma14247669
[33] Kishore, Y. S. N., Nadimpalli, S. G. D., Potnuru, A. K., Vemuri, J., & Khan, M. A. (2022). Statistical analysis of sustainable geopolymer concrete. Materials Today: Proceedings, 61, 212–223. https://doi.org/10.1016/j.matpr.2021.08.129
[34] Kumar, A., Arora, H. C., Kapoor, N. R., Mohammed, M. A., Kumar, K., Majumdar, A., & Thinnukool, O. (2022). Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models. Sustainability (Switzerland), 14(4). https://doi.org/10.3390/su14042404
[35] Kumar, B. S. C. (2019). Correlation between Compressive Strength and Split Tensile Strength of GGBS and MK Based Geopolymer Concrete using Regression Analysis. JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 14(1). https://doi.org/10.26782/jmcms.2019.02.00002
[36] Kurhade S., S. . P. (2024). Predicting compressive strength of geopolymer concrete using machine learning models. Innovative Infrastructure Solutions. https://doi.org/10.1007/s41062-024-01832-8
[37] Lee, W.-H., Lin, K.-L., Chang, T.-H., Ding, Y.-C., & Cheng, T.-W. (2020). Sustainable Development and Performance Evaluation of Marble-Waste-Based Geopolymer Concrete. Polymers, 12(9), 1924. https://doi.org/10.3390/polym12091924
[38] Liu, J., Shi, X., Zhang, G., & Li, L. (2023). Study the Mechanical Properties of Geopolymer under Different Curing Conditions. Minerals.
[39] Lopes, A. V, Lopes, S. M. R., & Pinto, I. (2020). Influence of the Composition of the Activator on Mechanical Characteristics of a Geopolymer. Applied Sciences, 10(10), 3349. https://doi.org/10.3390/app10103349
[40] Ma, C.-K., Awang, A. Z., & Omar, W. (2018). Structural and material performance of geopolymer concrete: A review. Construction and Building Materials, 186, 90–102. https://doi.org/10.1016/j.conbuildmat.2018.07.111
[41] Malayali, A. B., Chokkalingam, R. B., & Muthukannan, M. (2021). Strength Properties of Geopolymer Concrete Modified with Recycled Aggregates. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(1), 77–89. https://doi.org/10.1007/s40996-021-00662-3
[42] Mineral Commodity Summaries 2019. (2019). In Mineral Commodity Summaries. US Geological Survey. https://doi.org/10.3133/70202434
[43] Mohammed Hameed, M., & Mohammed Ali, A. (2021). Using of metakaolin to produce colored geopolymer concrete. Journal of Physics: Conference Series, 2114(1), 12018. https://doi.org/10.1088/1742-6596/2114/1/012018
[44] Montes, C., & Allouche, E. N. (2013). Rheological Behavior of Fly-Ash-Based Geopolymers. In Geopolymer Binder Systems (pp. 72–84). ASTM International. https://doi.org/10.1520/stp156620120094
[45] Mustafa, M. A.-T., Hanafi, I., Mahmoud, R., & Tayeh, B. A. (2019). Effect of partial replacement of sand by plastic waste on impact resistance of concrete: experiment and simulation. Structures, 20, 519–526. https://doi.org/10.1016/j.istruc.2019.06.008
[46] N.d., H. G. G. (2022). Cement Industry Initiative Releases Technology Roadmap to Cut CO2 Emissions 24% by 2050. SDG Knowledge Hub | IISD.
[47] Naghizadeh, A., & Ekolu, S. O. (2019). Method for comprehensive mix design of fly ash geopolymer mortars. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2018.12.185
[48] Nath, P., & Sarker, P. K. (2015). Use of OPC to improve setting and early strength properties of low calcium fly ash geopolymer concrete cured at room temperature. Cement and Concrete Composites, 55, 205–214. https://doi.org/10.1016/j.cemconcomp.2014.08.008
[49] Nath, P., & Sarker, P. K. (2017). Flexural strength and elastic modulus of ambient-cured blended low-calcium fly ash geopolymer concrete. Construction and Building Materials, 130, 22–31. https://doi.org/10.1016/j.conbuildmat.2016.11.034
[50] Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565
[51] Omran, B. A., Chen, Q., & Jin, R. (2016). Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete. Journal of Computing in Civil Engineering, 30(6). https://doi.org/10.1061/(asce)cp.1943-5487.0000596
[52] Oti, J., Adeleke, B., Anowie, F. X., Kinuthia, J. M., & Ekwulo, E. (2024). Mechanical Properties of a Sustainable Low-Carbon Geopolymer Concrete Using a Pumice-Derived Sodium Silicate Solution. Materials.
[53] Padakanti, R., Sarella, V. R., Pancharathi, R. K., & Jha, A. (2022). Study on the bond behavior of high-strength geopolymer concrete. Asian Journal of Civil Engineering, 23(2), 269–288. https://doi.org/10.1007/s42107-022-00422-2
[54] Pan, Z., Sanjayan, J. G., & Rangan, B. V. (2011). Fracture properties of geopolymer paste and concrete. Magazine of Concrete Research, 63(10), 763–771. https://doi.org/10.1680/macr.2011.63.10.763
[55] Panda, B., & Tan, M. J. (2018). Experimental study on mix proportion and fresh properties of fly ash based geopolymer for 3D concrete printing. Ceramics International. https://doi.org/10.1016/j.ceramint.2018.03.031
[56] Patel, Y. J., & Shah, N. (2018). Development of self-compacting geopolymer concrete as a sustainable construction material. Sustainable Environment Research, 28(6), 412–421. https://doi.org/10.1016/j.serj.2018.08.004
[57] Pham, T. M., Liu, J., Tran, P., Pang, V.-L., Shi, F., Chen, W., Hao, H., & Tran, T. M. (2020). Dynamic compressive properties of lightweight rubberized geopolymer concrete. Construction and Building Materials, 265, 120753. https://doi.org/10.1016/j.conbuildmat.2020.120753
[58] Philip, S., Nidhi, M., & Ahmed, H. U. (2024). A comparative analysis of tree-based machine learning algorithms for predicting the mechanical properties of fibre-reinforced GGBS geopolymer concrete. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(3). https://doi.org/10.1007/s41939-023-00355-6
[59] Put, R., Perrin, C., Questier, F., Coomans, D., Massart, D. L., & Vander Heyden, Y. (2003). Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure–retention relationship studies. Journal of Chromatography A, 988(2), 261–276. https://doi.org/10.1016/s0021-9673(03)00004-9
[60] Rahman, S. K., & Al-Ameri, R. (2021). A newly developed self-compacting geopolymer concrete under ambient condition. Construction and Building Materials, 267, 121822. https://doi.org/10.1016/j.conbuildmat.2020.121822
[61] Rahmawati, C., Aprilia, S., Saidi, T., Aulia, T. B., & Hadi, A. E. (2021). The Effects of Nanosilica on Mechanical Properties and Fracture Toughness of Geopolymer Cement. Polymers, 13(13), 2178. https://doi.org/10.3390/polym13132178
[62] Rashiddadash, P., Ramezanianpour, A. A., & Mahdikhani, M. (2014). Experimental investigation on flexural toughness of hybrid fiber reinforced concrete (HFRC) containing metakaolin and pumice. Construction and Building Materials, 51, 313–320. https://doi.org/10.1016/j.conbuildmat.2013.10.087
[63] Reddy, M. S., Dinakar, P., & Rao, B. H. (2018). Mix design development of fly ash and ground granulated blast furnace slag based geopolymer concrete. Journal of Building Engineering, 20, 712–722. https://doi.org/10.1016/j.jobe.2018.09.010
[64] Rehman, A., & Khokhar, I. (2022). ANN based predictive mimicker for mechanical and rheological properties of eco-friendly geopolymer concrete. Case Studies in Construction Materials.
[65] Ren, Q., Ding, L., Dai, X., Jiang, Z., & De Schutter, G. (2021). Prediction of Compressive Strength of Concrete with Manufactured Sand by Ensemble Classification and Regression Tree Method. Journal of Materials in Civil Engineering, 33(7). https://doi.org/10.1061/(asce)mt.1943-5533.0003741
[66] Simatupang, P. H., Nine, V., & Sinaga, A. E. (2019). Setting time and compression strength of low-calcium fly ash geopolymer paste with non-calcined red soil substitution in ambient curing. IOP Conference Series: Materials Science and Engineering, 669(1), 12025. https://doi.org/10.1088/1757-899x/669/1/012025
[67] Sitarz, M., Castro-Gomes, J., & Hager, I. (2021). Strength and Microstructure Characteristics of Blended Fly Ash and Ground Granulated Blast Furnace Slag Geopolymer Mortars with Na and K Silicate Solution. Materials, 15(1), 211. https://doi.org/10.3390/ma15010211
[68] Sitarz, M., Hager, I., & Choińska, M. (2020). Evolution of Mechanical Properties with Time of Fly-Ash-Based Geopolymer Mortars under the Effect of Granulated Ground Blast Furnace Slag Addition. Energies, 13(5), 1135. https://doi.org/10.3390/en13051135
[69] Sivasundaram, V., & Malhotra, V. M. (1988). Fly ash in concrete compilation of abstracts of papers from recent international conferences and symposia on fly ash in concrete. Natural Resources Canada/CMSS/Information Management. https://doi.org/10.4095/305072
[70] Somna, K., Jaturapitakkul, C., Kajitvichyanukul, P., & Chindaprasirt, P. (2011). NaOH-activated ground fly ash geopolymer cured at ambient temperature. Fuel. https://doi.org/10.1016/j.fuel.2011.01.018
[71] Sravanthi, D., Himath Kumar, Y., & Sarath Chandra Kumar, B. (2020). Comparative study on flow characteristics, strength and durability of GGBS based geopolymer concrete. IOP Conference Series: Materials Science and Engineering, 912(6), 62032. https://doi.org/10.1088/1757-899x/912/6/062032
[72] Subash, N., Avudaiappan, S., Adish Kumar, S., Amran, M., Vatin, N., Fediuk, R., & Aepuru, R. (2021). Experimental Investigation on Geopolymer Concrete with Various Sustainable Mineral Ashes. Materials, 14(24), 7596. https://doi.org/10.3390/ma14247596
[73] Topçu, İ. B., & Sarıdemir, M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41(3), 305–311. https://doi.org/10.1016/j.commatsci.2007.04.009
[74] Van Dao, D., Ly, H. B., Trinh, S. H., Le, T. T., & Pham, B. T. (2019). Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials. https://doi.org/10.3390/ma12060983
[75] Venkatesh, C., Rao, M. V. S., Praveen Kumar, M., & Sonali Sri Durga, C. (2024). Effect of mineral admixtures and curing regimes on propertieof self-compacting concrete. Journal of Sustainable Construction Materials and Technologies, 9(1). https://doi.org/10.47481/jscmt.1383493
[76] Verma, M., Upreti, K., Vats, P., Singh, S., Singh, P., Dev, N., Kumar Mishra, D., & Tiwari, B. (2022). Experimental Analysis of Geopolymer Concrete: A Sustainable and Economic Concrete Using the Cost Estimation Model. Advances in Materials Science and Engineering, 2022, 1–16. https://doi.org/10.1155/2022/7488254
[77] Wang, Q., Bian, H., Li, M., Dai, M., Chen, Y., Jiang, H., Zhang, Q., Dong, F., Huang, J., & Ding, Z. (2022). Effects of a Water-Glass Module on Compressive Strength, Size Effect and Stress–Strain Behavior of Geopolymer Recycled Aggregate Concrete. Crystals, 12(2), 218. https://doi.org/10.3390/cryst12020218
[78] Wang, W.-C., Wang, H.-Y., & Lo, M.-H. (2015). The fresh and engineering properties of alkali activated slag as a function of fly ash replacement and alkali concentration. Construction and Building Materials, 84, 224–229. https://doi.org/10.1016/j.conbuildmat.2014.09.059
[79] Waqas, R. M., Butt, F., Danish, A., Alqurashi, M., Mosaberpanah, M. A., Masood, B., & Hussein, E. E. (2021). Influence of Bentonite on Mechanical and Durability Properties of High-Calcium Fly Ash Geopolymer Concrete with Natural and Recycled Aggregates. Materials, 14(24), 7790. https://doi.org/10.3390/ma14247790
[80] Waqas, R. M., Butt, F., Zhu, X., Jiang, T., & Tufail, R. F. (2021). A Comprehensive Study on the Factors Affecting the Workability and Mechanical Properties of Ambient Cured Fly Ash and Slag Based Geopolymer Concrete. Applied Sciences, 11(18), 8722. https://doi.org/10.3390/app11188722
[81] Yilmaz, Y., Cakmak, T., Kurt, Z., & Ustabas, I. (2024). Predicting Mechanical Properties in Geopolymer Mortars, Including Novel Precursor Combinations, Through XGBoost Method. Arabian Journal for Science and Engineering.
[82] Zaidahtulakmal, M. Z., Kartini, K., & Hamidah, M. S. (2019). Rice husk ash (RHA) based geopolymer mortar incorporating sewage sludge ash (SSA). Journal of Physics: Conference Series, 1349(1), 12022. https://doi.org/10.1088/1742-6596/1349/1/012022
[83] Zhang, H., Li, L., Yuan, C., Wang, Q., Sarker, P. K., & Shi, X. (2020). Deterioration of ambient-cured and heat-cured fly ash geopolymer concrete by high temperature exposure and prediction of its residual compressive strength. Construction and Building Materials, 262, 120924. https://doi.org/10.1016/j.conbuildmat.2020.120924
[84] Zhang, P., Sun, X., Wang, F., & Wang, J. (2023). Mechanical Properties and Durability of Geopolymer Recycled Aggregate Concrete: A Review. Polymers.
[85] Zhao, J., Wang, K., Wang, S., Wang, Z., Yang, Z., Shumuye, E. D., & Gong, X. (2021). Effect of Elevated Temperature on Mechanical Properties of High-Volume Fly Ash-Based Geopolymer Concrete, Mortar and Paste Cured at Room Temperature. Polymers, 13(9), 1473. https://doi.org/10.3390/polym13091473
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Civil Engineering Researchers
This work is licensed under a Creative Commons Attribution 4.0 International License.
