Predicting the compressive strength of ecological concrete made with PET granules using artificial neural networks (MATLAB)

Authors

  • Houcine Bentegri
  • Mohamed Rabehi
  • Samir Kherfane

DOI:

https://doi.org/10.54021/seesv5n1-073

Keywords:

PET, correlation coefficient, ANN, prediction, environmentally friendly

Abstract

The problem of getting rid of waste, especially plastic waste, has become a problem that worries governments. From this standpoint, the method of getting rid of plastic appeared by inserting it and making it one of the components of concrete, and it became called environmentally friendly concrete.The purpose of this research is to evaluate the performance of long-lasting concrete that has partial volumetric substitution of aggregate with polyethylene terephthalate (PET) granules. mechanical characteristics, such as tensile and compressive strength, This work offers a prediction model-based method for predicting the compressive and tensile strength of environmentally friendly concrete with various kinds of plastic aggregates (PET) using artificial neural networks (ANN). Artificial neurons, which resemble brain neurons in general, are linked units or nodes that make up an ANN. These are linked together by edges that resemble brain synapses. Connected neurons send messages to an artificial neuron. Previous literature collected a data group with five affecting characteristics: water, fine aggregates, cement, coarse aggregates, PET aggregates, and model validation. Tensile and compressive strength were the outcomes. Additionally, a sensitivity analysis was done to confirm the stability and robustness of this model. The testing results demonstrated the excellent performance of the ANN model, which makes it a viable method for predicting the compressive and tensile strength of an environmentally friendly concrete. In addition, the correlation coefficient for compressive strength was found to be 0.9999, and tensile strength is 0.9999 which indicates the high reliability of the model used in the analysis and the accuracy of the results obtained.

References

Al-Jamimi .H.A, A. Bagudu, T.A. Saleh, An intelligent approach for the modeling and experimental optimization of molecular hydrodesulfurization over AlMoCoBi catalyst, J. Mol. Liq. 278 (2019) 376–384, https://doi.org/10.1016/j.molliq.2018.12.144.

Al-Jamimi .H.A, G.M. BinMakhashen, T.A. Saleh, Multiobjectives optimization in petroleum refinery catalytic desulfurization using Machine learning approach, Fuel 322 (2022), 124088, https://doi.org/10.1016/j.fuel.2022.124088.

Almeshal .Ibrahim Almeshal, Bassam A. Tayeh, Rayed Alyousef, Hisham Alabduljabbar, Abdeliazim Mustafa Mohamed,Eco-friendly concrete containing recycled plastic as partial replacement for sand,Journal of Materials Research and Technology,Volume 9, Issue 3,2020,Pages 4631-4643,ISSN 2238-7854,https://doi.org/10.1016/j.jmrt.2020.02.090.

Ammar ,Ahdal & Amrani, Mokhtar & Ghaleb, Abdulrakeeb & Abadel, Aref & Alghamdi, Hussam & Alamri, Mohammed & Wasim, Muhammad & Shameeri, Mutahar. (2022). Mechanical performance and feasibility analysis of green concrete prepared with local natural zeolite and waste PET plastic fibers as cement replacements. Case Studies in Construction Materials. 17. e01256. 10.1016/j.cscm.2022.e01256.

Aocharoen, Yanika Piya Chotickai, Compressive mechanical and durability properties of concrete with polyethylene terephthalate and high-density polyethylene aggregates,Cleaner Engineering and Technology,Volume 12,2023,100600,ISSN 26667908,https://doi.org/10.1016/j.clet.2023.100600.(https://www.sciencedirect.com/science/article/pii/S2666790823000058)

Argatov I (2019) Artificial Neural Networks (ANNs) as a Novel Modeling Technique in Tribology. Front. Mech. Eng. 5:30. doi: 10.3389/fmech.2019.00030

Asteris .P.G., F.I.M. Rizal, M. Koopialipoor, P.C. Roussis, M. Ferentinou, D.J. Armaghani, B. Gordan, Slope stability classification under seismic conditions using several tree-based intelligent techniques, Appl. Sci. (Switz.) 12 (3) (2022), https://doi.org/10.3390/app12031753.

Asteris .P.G., S. Nozhati, M. Nikoo, L. Cavaleri, M. Nikoo, Krill herd algorithm-based neural network in structural seismic reliability evaluation, Mech. Adv. Mater. Struct. 26 (13) (2019) 1146–1153, https://doi.org/10.1080/15376494.2018.1430874.

Azad ,Mohammed & Muhammad, Muhammad & Mohammed, Bilal. (2023). Effect of PET waste fiber addition on flexural behavior of concrete beams reinforced with GFRP bars. Case Studies in Construction Materials. 19. e02564. 10.1016/j.cscm.2023.e02564.

Babafemi, A.J., ˇSavija, B., Paul, S.C., Anggraini, V., 2018. Engineering properties of concrete with waste recycled plastic: a review. Sustainability 10 (11), 3875.https:// doi.org/10.3390/su10113875

Bajracharya, R.M., Manalo, A.C., Karunasena, W., Lau, K.T., 2016. Characterisation of recycled mixed plastic solid wastes: coupon and full-scale investigation. Waste Manag. 48, 72–80. https://doi.org/10.1016/j.wasman.2015.11.017

Bamigboye, Gideon & Tarverdi, Karnik & Umoren, Amarachi & Bassey, Daniel & Okorie, Uchechukwu & Adediran, Joel. (2021). Evaluation of eco-friendly concrete having waste PET as fine aggregates. Cleaner Materials. 2. 100026. 10.1016/j.clema.2021.100026.

Bentegri, Houcine & Mohamed, Rabehi & Samir, Kherfane & Boukansous, Sarra. (2023). Valorization of plastic waste in concrete for sustainable development. The Journal of Engineering and Exact Sciences. 9. 16009-01e. 10.18540/jcecvl9iss5pp16009-01e.

Botha, Ayden & Walls, Richard & Flores Quiroz, Natalia & Babafemi, Adewumi John. (2023). Behaviour of concrete building units incorporating waste plastic eco-aggregate (RESIN8) subjected to fire conditions. Journal of Building Engineering. 76. 107393. 10.1016/j.jobe.2023.107393.

Cherkassky .V, Y. Ma, Practical selection of SVM parameters and noise estimation for SVM regression, Neural Netw. 17 (1) (2004) 113–126.

Danial J. Armaghani, G.D. Hatzigeorgiou, C. Karamani, A. Skentou, I. Zoumpoulaki, P.G. Asteris, Soft computing-based techniques for concrete beams shearstrength, Procedia Struct. Integr. 17 (2019) 924–933, https://doi.org/10.1016/j.prostr.2019.08.123.

Frigione, M., 2010. Recycling of PET bottles as fine aggregate in concrete. Waste Manag. 30 (6), 1101–1106. https://doi.org/10.1016/j.wasman.2010.01.030.

Hamad .B.S., A.H. Dawi, Sustainable normal and high strength recycled aggregate concretes using crushed tested cylinders as coarse aggregates, Case Stud. Constr. Mater. 7 (2017) 228239.

Ilyas, R., Azmi, A., Nurazzi, N., Atiqah, A., Atikah, M., Ibrahim, R., Norrrahim, M.N.F., Asyraf, M., Sharma, S., Punia, S., 2022. Oxygen permeability properties of nanocellulose reinforced biopolymer nanocomposites. Mater. Today: Proc. 52, 2414–2419. https://doi.org/10.1016/j.matpr.2021.10.420.

Irwan, J.M. & Sheikh Khalid, Faisal. (2013). Relationship between Compressive, Splitting Tensile and Flexural Strength of Concrete Containing Granulated Waste Polyethylene Terephthalate (PET) Bottles as Fine Aggregate. Advanced Materials Research. Volume 795. 356-359. 10.4028/www.scientific.net/AMR.795.356.

Jandré Daniel Oosthuizen, Adewumi John Babafemi, Richard Shaun Walls, 3D-printed recycled plastic eco-aggregate (Resin8) concrete,Construction and Building Materials,Volume 408,2023,133712,ISSN 0950-0618,https://doi.org/10.1016/j.conbuildmat.2023.133712.

Jase D. Sitton, Yasha Zeinali, Brett A. Story,Rapid soil classification using artificial neural networks for use in constructing compressed earth blocks,Construction and Building Materials,Volume 138,2017,Pages 214-221,ISSN 0950-0618,https://doi.org/10.1016/j.conbuildmat.2017.02.006.

Kangavar, Mohammad & Lokuge, Weena & Manalo, Allan & Karunasena, Warna & Frigione, Mariaenrica. (2022). Investigation on the properties of concrete with recycled polyethylene terephthalate (PET) granules as fine aggregate replacement. Case Studies in Construction Materials. 16. e00934. 10.1016/j.cscm.2022.e00934.

Kardani. N, A. Bardhan, P. Samui, M. Nazem, P.G. Asteris, A. Zhou, Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients, Int. J. Therm. Sci. 173 (2022), https://doi.org/10.1016/j.ijthermalsci.2021.107427.

Ling.H, C. Qian, W. Kang, C. Liang, H. Chen, Combination of support vector machine and K-fold cross validation to predict compressive strength of concrete in marine environment, Constr. Build. Mater. 206 (2019) 355–363.

Lu. S, M. Koopialipoor, P.G. Asteris, M. Bahri, D.J. Armaghani, A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs, Materials 13 (17) (2020), https://doi.org/10.3390/ma13173902.

Michal Tkáč, Robert Verner, Artificial neural networks in business: Two decades of research,Applied Soft Computing,Volume 38,2016,Pages 788-804,ISSN 1568-4946,https://doi.org/10.1016/j.asoc.2015.09.040.

Mohan R, Vijayaprabha Chakrawarthi, T. Vamsi Nagaraju, Siva Avudaiappan, T.F. Awolusi, Ángel Roco-Videla, Marc Azab, Pavel Kozlov,Performance of recycled Bakelite plastic waste as eco-friendly aggregate in the concrete beams,Case Studies in Construction Materials,Volume 18,2023,e02200,ISSN 2214-5095,https://doi.org/10.1016/j.cscm.2023.e02200.

Ncube, L.K., Ude, A.U., Ogunmuyiwa, E.N., Zulkifli, R., Beas, I.N., 2021. An overview of plastic waste generation and management in food packaging industries. Recycling 6 (1). https://doi.org/10.3390/recycling6010012. , p. 12.

Nkomo, N., Masu, L., Nziu, P., 2022. Effects of polyethylene terephthalate fibre reinforcement on mechanical properties of concrete. Adv. Mater. Sci. Eng. 2022.

Park. J.Y, Y.G. Yoon, T.K. Oh, Prediction of concrete strength with P-, S-, R-wave velocities by support vector machine (SVM) and artificial neural network (ANN), Appl. Sci. 9 (19) (2019) 4053.

Shiuly, Amit & Hazra, Tumpa & Sau, Debasis & Maji, Dibyendu. (2022). Performance and optimisation study of waste plastic aggregate based sustainable concrete – A machine learning approach. Cleaner Waste Systems. 2. 100014. 10.1016/j.clwas.2022.100014.

Siorikis .V.G, Ultimate axial load of rectangular concrete-filled steel tubes using multiple ANN activation functions, Steel Compos. Struct. 42 (4) (2022) 459–475, https://doi.org/10.12989/scs.2022.42.4.459.

Skibicki .S, M. Pułtorak, M. Kaszy´nska, M. Hoffmann, E. Ekiert, D. Sibera, The effect of using recycled PET aggregates on mechanical and durability properties of 3D printed mortar, Construction and Building Materials 335 (2022), 127443.

Taffese, W.Z., Sistonen, E., & Puttonen, J. (2015). Prediction of concrete carbonation depth using decision trees. Proc. 23rd Eur. Symp. Artif. Neural Networks, Comput. Intell. Mach. Learn. ESANN, 415–420.

Umasabor, Richie & Daniel, Samuel.C.. (2020). The effect of using polyethylene terephthalate as an additive on the flexural and compressive strength of concrete. Heliyon. 6. e04700. 10.1016/j.heliyon.2020.e04700.

Wurm, F.R., Spierling, S., Endres, H.-J., Barner, L., 2020. Plastics and the environment—current status and challenges in Germany and Australia. Macromol. Rapid Commun. 41 (18), 2000351 https://doi.org/10.1002/marc.202000351.

Xu .L, L. Hou, Z. Zhu, Y. Li, J. Liu, T. Lei, X. Wu, Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm, Energy 222 (2021), 119955.

Zeng. J, P.C. Roussis, A.S. Mohammed, C. Maraveas, S.A. Fatemi, D.J. Armaghani, P.G. Asteris, Prediction of peak particle velocity caused by blasting through the combinations of boosted-chaid and svm models with various kernels, Appl. Sci. (Switz.) 11 (8) (2021), https://doi.org/10.3390/app11083705.

Ziółkowski, Patryk & Demczynski, Sebastian & Niedostatkiewicz, Maciej. (2019). Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning. Applied Sciences. 9. 3267. 10.3390/app9163267.

Downloads

Published

2024-04-29

How to Cite

Bentegri, H., Rabehi, M., & Kherfane, S. (2024). Predicting the compressive strength of ecological concrete made with PET granules using artificial neural networks (MATLAB). STUDIES IN ENGINEERING AND EXACT SCIENCES, 5(1), 1413–1435. https://doi.org/10.54021/seesv5n1-073