Machine Learning and Linear Regression Approach to Model Unconfined Compressive Strength of Ceramic Waste Modified Soil as Subgrade Pavement Material

• Autorzy: Alkahtani Meshel Qablan

Identyfikatory

DOI

10.54740/ros.2024.041

• Języki publikacji: EN

Abstrakty

EN

An effective application of artificial intelligence involves artificial neural networks. Artificial neural networks and linear regression models were developed to simulate the effects of using discarded ceramic waste as a subgrade for pavement. The ceramic waste was used at 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. A sample with 0% ceramic waste was tested to serve as a reference sample. The dataset was produced from laboratory experimentation findings used to train, test, and evaluate the model. A training set, a target set, and a prediction set were created from the dataset. The artificial neural network MSE was 0.42-1.40, while the linear regression model range was 1.74 to 3.63 for ceramic modified samples. The R2 range for the ANN model was 0.85-0.92, and the linear regression model exhibited a range of 0.71-0.78. The ANN model was more accurate than the linear regression model. Future studies are required to compare different machine-learning approaches for predicting soil mechanical properties.

Słowa kluczowe

EN

ceramic waste; artificial neural network; unconfined compression strength; California bearing ratio

• Wydawca: Politechnika Koszalińska
• Czasopismo: Rocznik Ochrona Środowiska
• Rocznik: 2024
• Tom: Tom 26
• Strony: 424--431
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Alkahtani Meshel Qablan

• Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia

Bibliografia

• Abed, M.A., Taki, Z.N.M., Abed, A.H. (2021). Artificial neural network modeling of the modified hot mix asphalt stiffness using Bending Beam Rheometer. Materials Today: Proceedings, 42, 2584-2589. https://doi.org/10.1016/j.matpr.2020.12.583
• Afolagboye, L.O., Ajayi, D.E., Afolabi, I.O. (2023). Machine learning models for predicting unconfined compressive strength: A case study for Precambrian basement complex rocks from Ado-Ekiti, Southwestern Nigeria. Scientific African, 20. https://doi.org/10.1016/j.sciaf.2023.e01715
• Ahenkorah, I., Rahman, M.M., Karim, M.R., Beecham, S. (2023). Unconfined compressive strength of MICP and EICP treated sands subjected to cycles of wetting-drying, freezing-thawing and elevated temperature: Experimental and EPR modelling. Journal of Rock Mechanics and Geotechnical Engineering, 15(5), 1226-1247. https://doi.org/10.1016/j.jrmge.2022.08.007
• Ahmadi Sheshde, E., Cheshomi, A. (2015). New method for estimating unconfined compressive strength (UCS) using small rock samples. Journal of Petroleum Science and Engineering, 133, 367-375. https://doi.org/10.1016/j.petrol.2015.06.022
• Aju, D.E., Onyelowe, K.C., Alaneme, G.U. (2021). Constrained vertex optimisation and simulation of the unconfined compressive strength of geotextile reinforced soil for flexible pavement foundation construction. Cleaner Engineering and Technology, 5, 100287. https://doi.org/10.1016/j.clet.2021.100287
• Ashfaq, M., Iqbal, M., Khan, M.A., Jalal, F.E., Alzara, M., Hamad, M., Yosri, A.M. (2022). GEP tree-based computational AI approach to evaluate unconfined compression strength characteristics of Fly ash treated alkali contaminated soils. Case Studies in Construction Materials, 17(September), e01446. https://doi.org/10.1016/j.cscm.2022.e01446
• Barua, L., Zou, B. (2021). Planning maintenance and rehabilitation activities for airport pavements: A combined supervised machine learning and reinforcement learning approach. International Journal of Transportation Science and Technology, 11(2), 423-435. https://doi.org/10.1016/j.ijtst.2021.05.006
• Cabalar, A.F., Hassan, D.I., Abdulnafaa, M.D. (2017). Use of waste ceramic tiles for road pavement subgrade. Road Materials and Pavement Design, 18(4), 882-896. https://doi.org/10.1080/14680629.2016.1194884
• Cao, R., Leng, Z., Hsu, S.C., Hung, W.T. (2020). Modelling of the pavement acoustic longevity in Hong Kong through machine learning techniques. Transportation Research Part D: Transport and Environment, 83(May), 102366. https://doi.org/10.1016/j.trd.2020.102366
• Deboucha, S., Mamoune, A., Mohammed, S., Universitaire, C., Temouchent, A., Sail, Y. (2020). Effects of Ceramic Waste, Marble Dust, and Cement in Pavement Sub-base Layer. https://doi.org/10.1007/s10706-020-01211-x
• Deboucha, S., Ziani, H., Amriou, A. (2023). Effect of Ceramic Waste and Lime in Road Sub-Base Layer. Advances in Transdisciplinary Engineering, 43, 879-884. https://doi.org/10.3233/ATDE230810
• Ghafari, S., Ehsani, M., Moghadas Nejad, F. (2022). Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach. Construction and Building Materials, 314(PB), 125332. https://doi.org/10.1016/j.conbuildmat.2021.125332
• Ghorbani, B., Arulrajah, A., Narsilio, G., Horpibulsuk, S., & Bo, M. W. (2020). Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils. Soils and Foundations, 60(2), 398-412. https://doi.org/10.1016/j.sandf.2020.02.010
• Ghorbani, B., Arulrajah, A., Narsilio, G., Horpibulsuk, S., Win Bo, M. (2021). Thermal and mechanical properties of demolition wastes in geothermal pavements by experimental and machine learning techniques. Construction and Building Materials, 280, 122499. https://doi.org/10.1016/j.conbuildmat.2021.122499
• Gong, H., Sun, Y., Shu, X., Huang, B. (2018). Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890-897. https://doi.org/10.1016/j.conbuildmat.2018.09.017
• Huang, J., Shiva Kumar, G., Ren, J., Zhang, J., Sun, Y. (2021). Accurately predicting dynamic modulus of asphalt mixtures in low-temperature regions using hybrid artificial intelligence model. Construction and Building Materials, 297, 123655. https://doi.org/10.1016/j.conbuildmat.2021.123655
• Issa, A., Samaneh, H., Ghanim, M. (2021). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 13(1), 101490. https://doi.org/10.1016/j.asej.2021.04.033
• Kumar, C.V., Swaminathen, A.N., Vittalaiah, A., Kumar, C.R. (2022). Materials Today : Proceedings Pavement Subgrade Stabilized with Waste coal Ash and Geosynthetics : An Experimental Study and Multiple Regression Modelling. Materials Today: Proceedings, 52(3), 1543-1550. https://doi.org/10.1016/j.matpr.2021.11.233
• Li, Y., Chen, J., Dan, H., Wang, H. (2022). Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network. Cold Regions Science and Technology, 194(August 2021), 103434. https://doi.org/10.1016/j.coldregions.2021.103434
• Mabrouk, G.M., Elbagalati, O.S., Dessouky, S., Fuentes, L., Walubita, L.F. (2021). Using ANN modeling for pavement layer moduli backcalculation as a function of traffic speed deflections. Construction and Building Materials, 315, 125736. https://doi.org/10.1016/j.conbuildmat.2021.125736
• Mohd, M.H., Koting, S., Hashim, H., Mo, K.H., Aziz, S.A. (2024). Utilising tile waste as an additive to enhance lime-based subgrade stabilisation. Case Studies in Construction Materials, 20, e03342. https://doi.org/10.1016/j.cscm.2024.e03342