Predicting the strength of recycled glass powder-based geopolymers for improving mechanical behavior of clay soils using artificial intelligence
Abolfazl Baghbani, Firas Daghistani, Hasan Baghbani, Katayoon Kianydownload PDF
Abstract. The paper investigates the use of artificial intelligence (AI) methods to predict the strength of recycled glass powder (RGP) and soil mixtures based on different input parameters. The study utilized a database of 57 sets with 5 inputs, including RGP percentage, ordinary Portland cement (OPC) percentage, molar concentration, curing temperature and time, and one output, mixed UCS. There were two artificial intelligence models used in this study, a support vector machines (SVM) and classification and regression random forest (CRRF). The results demonstrate the potential of RGP-based geopolymers to improve the mechanical behavior of clay soils, and the use of AI methods to predict the strength of RGP and soil mixtures with high accuracy. Using SVM model, the testing dataset had a mean absolute error (MAE) and R2 of 0.072 and 0.978, respectively. Also, CRRF had an accurate performance with a MAE of 0.075 and the R2 of 0.979. These results suggest that the AI models fits well with the data. Also, by analyzing the results of the SVM and CRRF models, it is found that curing time is the most important input parameter, while RGP and OPC are the least significant.
Recycled Glass Powder, Ordinary Portland Cement, Clayey Soil, Artificial intelligence, Soil Stabilizer, CRRF, SVM
Published online 8/10/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Abolfazl Baghbani, Firas Daghistani, Hasan Baghbani, Katayoon Kiany, Predicting the strength of recycled glass powder-based geopolymers for improving mechanical behavior of clay soils using artificial intelligence, Materials Research Proceedings, Vol. 31, pp 646-655, 2023
The article was published as article 66 of the book Advanced Topics in Mechanics of Materials, Structures and Construction
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
 AlBiajawi, M.I., Embong, R. and Muthusamy, K., 2022. An overview of the utilization and method for improving pozzolanic performance of agricultural and industrial wastes in concrete. Materials Today: Proceedings, 48, pp.778-783. https://doi.org/10.1016/j.matpr.2021.02.260
 Nguyen MD, Baghbani A, Alnedawi A, Ullah S, Kafle B, Thomas M, Moon EM, Milne NA. Investigation on the suitability of aluminium-based water treatment sludge as a sustainable soil replacement for road construction. Transportation Engineering. 2023 Mar 28:100175. https://doi.org/10.1016/j.treng.2023.100175
 Sahebzadeh S, Heidari A, Kamelnia H, Baghbani A. Sustainability features of Iran’s vernacular architecture: A comparative study between the architecture of hot-arid and hot-arid-windy regions. Sustainability. 2017 May 4;9(5):749. https://doi.org/10.3390/su9050749
 Baghbani A, Costa S, O’Kelly BC, Soltani A, Barzegar M. Experimental study on cyclic simple shear behaviour of predominantly dilative silica sand. International Journal of Geotechnical Engineering. 2022 Oct 23:1-5. https://doi.org/10.1080/19386362.2022.2135226
 Bilondi, M.P., Toufigh, M.M. and Toufigh, V., 2018. Experimental investigation of using a recycled glass powder-based geopolymer to improve the mechanical behavior of clay soils. Construction and building Materials, 170, pp.302-313. https://doi.org/10.1016/j.conbuildmat.2018.03.049
 Baghbani A, Baumgartl T, Filipovic V. Effects of Wetting and Drying Cycles on Strength of Latrobe Valley Brown Coal. Copernicus Meetings; 2023 Feb 22. https://doi.org/10.5194/egusphere-egu23-4804
 Nguyen MD, Baghbani A, Alnedawi A, Ullah S, Kafle B, Thomas M, Moon EM, Milne NA. Experimental Study on the Suitability of Aluminium-Based Water Treatment Sludge as a Next Generation Sustainable Soil Replacement for Road Construction. Available at SSRN 4331275.
 Komnitsas, K., Zaharaki, D. and Perdikatsis, V., 2009. Effect of synthesis parameters on the compressive strength of low-calcium ferronickel slag inorganic polymers. Journal of Hazardous Materials, 161(2-3), pp.760-768. https://doi.org/10.1016/j.jhazmat.2008.04.055
 Abdullah, M.M.A., Hussin, K., Bnhussain, M., Ismail, K.N. and Ibrahim, W.M.W., 2011. Mechanism and chemical reaction of fly ash geopolymer cement-a review. Int. J. Pure Appl. Sci. Technol, 6(1), pp.35-44.
 Wu, Y., Lu, B., Bai, T., Wang, H., Du, F., Zhang, Y., Cai, L., Jiang, C. and Wang, W., 2019. Geopolymer, green alkali activated cementitious material: Synthesis, applications and challenges. Construction and Building Materials, 224, pp.930-949. https://doi.org/10.1016/j.conbuildmat.2019.07.112
 Koushkbaghi, M., Alipour, P., Tahmouresi, B., Mohseni, E., Saradar, A. and Sarker, P.K., 2019. Influence of different monomer ratios and recycled concrete aggregate on mechanical properties and durability of geopolymer concretes. Construction and Building Materials, 205, pp.519-528. https://doi.org/10.1016/j.conbuildmat.2019.01.174
 Tatlisoz, N., Benson, C.H. and Edil, T.B., 1997. Effect of fines on mechanical properties of soil-tire chip mixtures. ASTM Special Technical Publication, 1275, pp.93-124. https://doi.org/10.1520/STP15645S
 Ashiq, S.Z., Akbar, A., Farooq, K. and Mujtaba, H., 2022. Sustainable improvement in engineering behavior of Siwalik Clay using industrial waste glass powder as additive. Case Studies in Construction Materials, 16, p.e00883. https://doi.org/10.1016/j.cscm.2022.e00883
 Baghbani A, Choudhury T, Costa S, Reiner J. Application of artificial intelligence in geotechnical engineering: A state-of-the-art review. Earth-Science Reviews. 2022 May 1;228:103991. https://doi.org/10.1016/j.earscirev.2022.103991
 Bardhan A, Samui P. Application of artificial intelligence techniques in slope stability analysis: a short review and future prospects. International Journal of Geotechnical Earthquake Engineering (IJGEE). 2022 Jan 1;13(1):1-22. https://doi.org/10.4018/IJGEE.298988
 Baghbani A, Daghistani F, Naga HA, Costa S. Development of a Support Vector Machine (SVM) and a Classification and Regression Tree (CART) to Predict the Shear Strength of Sand Rubber Mixtures. InProceedings of the 8th International Symposium on Geotechnical Safety and Risk (ISGSR), Newcastle, Australia 2022. https://doi.org/10.3850/978-981-18-5182-7_00-08-004.xml
 Baghbani A, Costa S, Choudhury T. Developing Mathematical Models for Predicting Cracks and Shrinkage Intensity Factor During Clay Soil Desiccation. Available at SSRN 4408164.
 Zhang X, Wu X, Park Y, Zhang T, Huang X, Xiao F, Usmani A. Perspectives of big experimental database and artificial intelligence in tunnel fire research. Tunnelling and Underground Space Technology. 2021 Feb 1;108:103691. https://doi.org/10.1016/j.tust.2020.103691
 Baghbani A, Baghbani H, Shalchiyan MM, Kiany K. Utilizing artificial intelligence and finite element method to simulate the effects of new tunnels on existing tunnel deformation. Journal of Computational and Cognitive Engineering. 2022 Aug 15.
 Sharma S, Ahmed S, Naseem M, Alnumay WS, Singh S, Cho GH. A survey on applications of artificial intelligence for pre-parametric project cost and soil shear-strength estimation in construction and geotechnical engineering. Sensors. 2021 Jan 11;21(2):463. https://doi.org/10.3390/s21020463
 Baghbani A, Costa S, Lu Y, Soltani A, Abuel-Naga H and Samui P. Effects of Particle Shape on Shear Modulus of Sand Using Dynamic Simple Shear Testing. Arabian journal of geosciences. 2023.
 Xu JJ, Zhang H, Tang CS, Cheng Q, Tian BG, Liu B, Shi B. Automatic soil crack recognition under uneven illumination condition with the application of artificial intelligence. Engineering Geology. 2022 Jan 1;296:106495. https://doi.org/10.1016/j.enggeo.2021.106495
 Baghbani A, Costa S, Choundhury T, Faradonbeh RS. Prediction of Parallel Desiccation Cracks of Clays Using a Classification and Regression Tree (CART) Technique. InProceedings of the 8th International Symposium on Geotechnical Safety and Risk (ISGSR), Newcastle, Australia 2022. https://doi.org/10.3850/978-981-18-5182-7_00-08-003.xml
 Njock PG, Shen SL, Zhou A, Lyu HM. Evaluation of soil liquefaction using AI technology incorporating a coupled ENN/t-SNE model. Soil Dynamics and Earthquake Engineering. 2020 Mar 1;130:105988. https://doi.org/10.1016/j.soildyn.2019.105988
 Baghbani A, Choudhury T, Samui P, Costa S. Prediction of secant shear modulus and damping ratio for an extremely dilative silica sand based on machine learning techniques. Soil Dynamics and Earthquake Engineering. 2023 Feb 1;165:107708. https://doi.org/10.1016/j.soildyn.2022.107708
 Baghbani A, Costa S, Faradonbeh RS, Soltani A, Baghbani H. Experimental-AI Investigation of the Effect of Particle Shape on the Damping Ratio of Dry Sand under Simple Shear Test Loading, 2023, preprint. https://doi.org/10.20944/preprints202303.0021.v1
 Baghbani A, Daghistani F, Baghbani H, Kiany K, Bazaz JB. Artificial Intelligence-Based Prediction of Geotechnical Impacts of Polyethylene Bottles and Polypropylene on Clayey Soil. EasyChair; 2023 Feb 19.
 Baghbani A, Costa S, Faradonbeh RS, Soltani A, Baghbani H. Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence. Applied Sciences. 2023 Mar 29;13(7):4363 https://doi.org/10.3390/app13074363
 Baghbani A, Daghistani F, Kiany K, Shalchiyan MM. AI-Based Prediction of Strength and Tensile Properties of Expansive Soil Stabilized with Recycled Ash and Natural Fibers. EasyChair; 2023 Feb 19.
 Baghbani A.; Abuel-Naga H.; Shirani Faradonbeh R.; Costa S.; Almasoudi R. Ultrasonic Characterization of Compacted Salty Kaolin-Sand Mixtures Under Nearly Zero Vertical Stress Using Experimental Study and Machine Learning, Geotechnical and geological engineering, 2023. https://doi.org/10.1007/s10706-023-02441-5
 Baghbani, A.; Nguyen, M.D.; Alnedawi, A.; Milne, N.; Baumgartl, T.; Abuel-Naga, H. Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio. Preprints 2023, 2023030197. https://doi.org/10.20944/preprints202303.0197.v1
 Vapnik V. The nature of statistical learning theory. Springer science & business media; 1999 Nov 19. https://doi.org/10.1007/978-1-4757-3264-1