Application of Machine Learning to Predict the Mechanical Characteristics of Concrete Containing Recycled Plastic-Based Materials
Source abstract
Abstract
One of the practical ways to overcome the adverse environmental effects of plastic bottle waste is to implement bottles into concrete, one of the most widely used materials in the construction industry. Plastic bottles are mainly made of polyethylene terephthalate (PET) and can be used as a fiber to reinforce concrete. In recent years, PET fiber-reinforced concrete (PFRC) has attracted researcher attention, and several experimental studies have been conducted. This paper aims to present the benefits of using PET fiber as a reinforcing element in concrete using a machine learning approach. By considering the effect of PET fibers in concrete, engineers and stakeholders may be encouraged to further use these recycled materials. The proposed network was successfully able to capture the response of PFRC with high accuracy (mean squared error (MSE) of 7.11 MPa and R coefficient of 98%). The results of the proposed network show that the amount of PET fiber usage in concrete has a significant effect on the compressive strength of PFRC. Moreover, the PFRC’s response considering the variation of mechanical and geometrical properties of PET fiber mainly depends on the fiber’s shape. The most effective shapes of PET fiber are shapes with deformation, followed by embossed and irregular shapes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Innovative concrete reinforcement materials · Structural Behavior of Reinforced Concrete · Innovations in Concrete and Construction Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Razi University, Kermanshah 67144-14971, Iran
Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
Department of Earth Sciences, Sapienza University of Rome & CERI Research Center, P.le Aldo Moro 5, 00185 Roma, Italy
Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USA
Department of Civil Engineering, Construction Management, and Environmental Engineering, Northern Arizona University, Flagstaff, AZ 86011, USA
References (64)
Show source-provided references
1. Foti, D. (2019). Use of Recycled Plastics in Eco-Efficient Concrete, Elsevier.
2. Sadeghian, V., Tanyous, M., and Mirshekar, S. (2020, January 27–29). Modelling FRP-Strengthened Beam-Column Joints in Performance Assessment of RC Frames. Proceedings of the 6th International Conference on Construction Material (ConMat 20), Fukuoka, Japan.
3. Theoretical analysis on the lateral drift of precast concrete frame with replaceable artificial controllable plastic hinges10.1016/j.jobe.2022.105386 ↗
4. Experimental Research on the Seismic Performance of Precast Concrete Frame with Replaceable Artificial Controllable Plastic Hinges10.1061/JSENDH.STENG-11648 ↗
5. Khan, R.A., and Sharma, R. (2018). Strength and durability characteristics of rice husk ash concrete reinforced with polypropylene fibres. Jordan J. Civ. Eng., 12.
6. Experimental study on flexural behavior of UHPC wet joint in prefabricated multi-girder bridge10.1016/j.engstruct.2022.115314 ↗
7. Effect of different supplementary cementitious materials on mechanical and durability properties of concrete
8. Use of selected waste materials in concrete mixes10.1016/j.wasman.2006.07.026 ↗
9. Use of recycled plastic in concrete: A review10.1016/j.wasman.2007.09.011 ↗
10. Use of plastic waste (poly-ethylene terephthalate) in asphalt concrete mixture as aggregate replacement10.1177/0734242X05056739 ↗
11. Yarn on yarn abrasion failure mechanism of ultrahigh molecular weight polyethylene fiber
12. Experimental behavior and analysis of high strength concrete beams reinforced with PET waste fiber10.1016/j.conbuildmat.2020.118350 ↗
13. Recycling plastics in polymer concrete for construction applications10.1061/(ASCE)0899-1561(1993)5:2(237) ↗
14. Recycling woven plastic sack waste and PET bottle waste as fiber in recycled aggregate concrete: An experimental study10.1016/j.wasman.2018.05.035 ↗
15. Recent studies on recycled PET fibers: Production and applications: A review10.1007/s42824-020-00014-y ↗
16. Fu, Q., Gu, M., Yuan, J., and Lin, Y. (2022). Experimental study on vibration velocity of piled raft supported embankment and foundation for ballastless high speed railway. Buildings, 12.10.3390/buildings12111982 ↗
17. Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector10.1016/j.measurement.2014.01.030 ↗
18. Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using radial basis function10.1016/j.flowmeasinst.2016.10.001 ↗
19. Moradi, M.J., Roshani, M.M., Shabani, A., and Kioumarsi, M. (2020). Prediction of the Load-Bearing Behavior of SPSW with Rectangular Opening by RBF Network. Appl. Sci., 10.10.3390/app10031185 ↗
20. Density and velocity determination for single-phase flow based on radiotracer technique and neural networks10.1016/j.flowmeasinst.2018.03.006 ↗
21. Liao, L., Du, L., and Guo, Y. (2021). Semi-supervised SAR target detection based on an improved faster R-CNN. Remote Sens., 14.10.3390/rs14010143 ↗
22. Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique10.1016/j.ijhydene.2015.12.098 ↗
23. An overview of neural networks for control10.1109/37.103352 ↗
24. The basic ideas in neural networks10.1145/175247.175256 ↗
25. Hosseini, S., Taylan, O., Abusurrah, M., Akilan, T., Nazemi, E., Eftekhari-Zadeh, E., Bano, F., and Roshani, G.H. (2021). Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries. Polymers, 13.10.3390/polym13213647 ↗
26. Dabiri, H., Farhangi, V., Moradi, M.J., Zadehmohamad, M., and Karakouzian, M. (2022). Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars. Appl. Sci., 12.10.3390/app12104851 ↗
27. Application of artificial neural network to predict load bearing capacity and stiffness of perforated masonry walls10.3390/civileng2010004 ↗
28. Response of low-percentage FRC slabs under impact loading: Experimental, numerical, and soft computing methods10.1016/j.istruc.2020.06.005 ↗
29. Chen, J., Tong, H., Yuan, J., Fang, Y., and Gu, R. (2022). Permeability prediction model modified on kozeny-carman for building foundation of clay soil. Buildings, 12.10.3390/buildings12111798 ↗
30. Gu, M., Cai, X., Fu, Q., Li, H., Wang, X., and Mao, B. (2022). Numerical Analysis of Passive Piles under Surcharge Load in Extensively Deep Soft Soil. Buildings, 12.10.3390/buildings12111988 ↗
31. The advancement of seismic isolation and energy dissipation mechanisms based on friction10.1016/j.soildyn.2021.106746 ↗
32. Training feedforward networks with the Marquardt algorithm10.1109/72.329697 ↗
33. Online measuring density of oil products in annular regime of gas-liquid two phase flows10.1016/j.measurement.2018.07.026 ↗
34. Evaluating the behaviour of centrally perforated unreinforced masonry walls: Applications of numerical analysis, machine learning, and stochastic methods10.1016/j.asej.2021.10.026 ↗
35. A Wiener degradation process with drift-based approach of determining target reliability index of concrete structures10.1002/qre.3168 ↗
36. Interpreting neural network connection weights
37. The prediction of fire performance of concrete-filled steel tubes (CFST) using artificial neural network10.1016/j.tws.2021.107499 ↗
38. Moradi, N., Tavana, M.H., Habibi, M.R., Amiri, M., Moradi, M.J., and Farhangi, V. (2022). Predicting the Compressive Strength of Concrete Containing Binary Supplementary Cementitious Material Using Machine Learning Approach. Materials, 15.10.3390/ma15155336 ↗
39. Fiber–matrix interaction at early ages of concrete with short fibers10.1016/j.cemconres.2009.11.014 ↗
40. Early age bond stress-slip behaviour of macro-synthetic fibre reinforced concrete10.1016/j.conbuildmat.2021.124097 ↗
41. Preparation and performance analysis of recycled PET fiber reinforced recycled foamed concrete10.1016/j.jobe.2022.104948 ↗
42. Development of recycled PET fiber and its application as concrete-reinforcing fiber10.1016/j.cemconcomp.2007.02.002 ↗
43. The pull-out behavior of chemically treated lignocellulosic fibers/polymeric matrix interface (LF/PM): A review10.1016/j.compositesb.2019.107059 ↗
44. Early age performance and mechanical characteristics of recycled PET fibre reinforced concrete10.1016/j.conbuildmat.2016.01.029 ↗
45. Effect of Fiber Parameters on the Strength Properties of Concrete Reinforced with PET Waste Fibers10.1007/s40996-021-00663-2 ↗
46. Effects of PET fibers on pumpability, shootability, and mechanical properties of wet-mix shotcrete
47. Sharma, R., Kaushik, R., and Sharma, T. (August, January 31). Effect of PET fibres different aspect ratio on fresh and mechanical properties of cement concrete. Proceedings of the Asia-Pacific Young Researchers and Graduates Symposium (YRGS 2014), Bangkok, Thailand.
48. A model for compressive strength of PET fiber reinforced concrete
49. Influence of PET fiber geometry on the mechanical properties of concrete: An experimental investigation10.1080/19648189.2015.1072112 ↗
50. The mechanical properties of PET fiber reinforced concrete from recycled bottle wastes10.4028/www.scientific.net/AMR.795.347 ↗
51. The possibility of enhancing some properties of self-compacting concrete by adding waste plastic fibers10.1016/j.jobe.2016.06.011 ↗
52. Experimental study on the mechanical properties of concrete incorporating PET fibers
53. Use of recycled plastics in concrete: A critical review10.1016/j.wasman.2016.03.005 ↗
54. Strength and workability characteristics of waste plastic fibre reinforced concrete produced from recycled aggregates
55. Strengths prediction of plastic fiber reinforced concrete (M30)
56. Comparative flexural behavior of four fiber reinforced cementitious composites10.1016/j.cemconcomp.2008.08.002 ↗
57. Material and structural performance evaluation of recycled PET fiber reinforced concrete10.1016/j.cemconcomp.2009.11.002 ↗
58. Assessment of diagonal macrocrack-induced debonding mechanisms in FRP-strengthened RC beams10.1061/(ASCE)CC.1943-5614.0001255 ↗
59. Effects of the geometry of recycled PET fiber reinforcement on shrinkage cracking of cement-based composites10.1016/j.compositesb.2007.05.001 ↗
60. Schembri, J. (2011). Recycled Polyethylene Terephthalate Fibres in Concrete, University of Malta.
61. Studying the effect of low reactivity metakaolin on free and restrained shrinkage of high performance concrete10.1016/j.jobe.2019.101053 ↗
62. Effect of the addition of synthetic fibers to concrete thin slabs on plastic shrinkage cracking10.1016/j.conbuildmat.2010.04.041 ↗
63. Choi, W.C., and Yun, H.D. (2013). Effect of expansive admixtures on the shrinkage and mechanical properties of high-performance fiber-reinforced cement composites. Sci. World J., 2013.10.1155/2013/418734 ↗
64. Jang, S.J., Kim, S.W., Park, W.S., Kobayashi, K., and Yun, H.D. (2018). Effects of shrinkage-compensation on mechanical properties and repair performance of strain-hardening cement composite materials. Adv. Civ. Eng., 2018.10.1155/2018/6727516 ↗