Prediction of Elastic Modulus for Fibre-Reinforced Soil-Cement Mixtures: A Machine Learning Approach
Source abstract
Abstract
Soil-cement mixtures reinforced with fibres are an alternative method of chemical soil stabilisation in which the inherent disadvantage of low or no tensile or flexural strength is overcome by incorporating fibres. These mixtures require a significant amount of time and resources for comprehensive laboratory characterisation, because a considerable number of parameters are involved. Therefore, the implementation of a Machine Learning (ML) approach provides an alternative way to predict the mechanical properties of soil-cement mixtures reinforced with fibres. In this study, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), and Multiple Regression (MR) algorithms were trained for predicting the elastic modulus of soil-cement mixtures reinforced with fibres. For ML algorithms training, a dataset of 121 records was used, comprising 16 properties of the composite material (soil, binder, and fibres). ANN and RF showed a promising determination coefficient (R2 ≥ 0.93) on elastic modulus prediction. Moreover, the results of the proposed models are consistent with the findings that the fibre and binder content have a significant effect on the elastic modulus.
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Research context
OpenAlex topics: Geotechnical Engineering and Soil Stabilization · Innovative concrete reinforcement materials · Geotechnical Engineering and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
ISISE, Department of Civil Engineering, University of Minho, 4800-058 Guimaraes, Portugal
CIEPQPF-Chemical Process Engineering and Forest Products Research Centre, Department of Civil Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
ISISE, Department of Civil Engineering, University of Coimbra, 3004-531 Coimbra, Portugal
Funding
FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Engineering Structures (ISISE) · R&D Unit Chemical Process Engineering and Forest Products Research Centre (CIEPQPF)
References (45)
Show source-provided references
1. Patel, A. (2019). Soil stabilisation. Geotechnical Investigations and Improvement of Ground Conditions, Woodhead Publishing.10.1016/B978-0-12-817048-9.00003-2 ↗
2. Emerging trends in expansive soil stabilisation: A review
3. Steinberg, M. (2000, January 5–8). Expansive soils, and the geomembrane remedy. Proceedings of the Geo-Denver 200, Denver, CO, USA.10.1061/40510(287)31 ↗
4. Tinoco, J., Correia, A.A.S., and Venda Oliveira, P.J. (2021). Soil-cement Mixtures Reinforced with Fibers: A Data-Driven Approach for Mechanical Properties Prediction. Appl. Sci., 11.10.3390/app11178099 ↗
5. Buhler, R.L., and Cerato, A.B. (2007, January 18–21). Stabilization of Oklahoma Expansive Soils using Lime and Class C Fly Ash. Proceedings of the Geo-Denver 2007, Denver, CO, USA.10.1061/40906(225)1 ↗
6. Hussey, N.L., Cerato, A.B., Grasmick, J.G., Holderby, E.S., Miller, G.A., and Tabet, W. (2010, January 20–24). An Assessment of Soil Parameters Governing Soil Strength Increases with Chemical Additives. Proceedings of the Geo-Florida 2010, West Palm Beach, FL, USA.10.1061/41095(365)274 ↗
7. Effect of Organic Matter Content and Binder Quantity on the Uniaxial Creep Behavior of an Artificially Stabilized Soil10.1061/(ASCE)GT.1943-5606.0001158 ↗
8. Correia, A.A.S., Venda Oliveira, P.J., and Lemos, L.J.L. (2013, January 2–6). Prediction of the unconfined compressive strength in soft soil chemically stabilized. Proceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering: Challenges and Innovation in Geotechnics, Paris, France.
9. Effect of polypropylene fibres on the compressive and tensile strength of a soft soil, artificially stabilised with binders10.1016/j.geotexmem.2014.11.008 ↗
10. Terashi, M. (2005, January 23–25). Keynote lecture: Design of deep mixing in infrastructure applications. Proceedings of the International Conference on Deep Mixing Best Practice, and Recent Advances, Stockholm, Sweden.
11. Terashi, M., and Kitazume, M. (2009, January 19–21). Keynote lecture: Current practice and future perspective of QA/QC for deep-mixed ground. Proceedings of the International Symposium of Deep Mixing and Admixture Stabilization, Okinawa, Japan.
12. Al-Tabbaa, A., Barker, P., and Evans, C.W. (2009, January 19–21). Keynote lecture: Design of deep mixing in infrastructure applications. Proceedings of the International Symposium of Deep Mixing and Admixture Stabilization, Okinawa, Japan.
13. Hernandez-Martinez, F.G., Sparrevik, M., Kvalvåg, Ø., Eggen, A., Kvennås, A., and Grini, R.S. (2009, January 19–21). Stabilization/Solidification of two contaminate marine sediments. Proceedings of the International Symposium of Deep Mixing and Admixture Stabilization, Okinawa, Japan.
14. Effect of fibre type on the compressive and tensile strength of a soft soil chemically stabilized10.1680/jgein.15.00040 ↗
15. Use of steel and polypropylene fibres to improve flexural performance of deep soil-cement column10.1016/j.conbuildmat.2011.10.040 ↗
16. Effects of polypropylene fiber inclusion on the strength and volume change characteristics of cement-fly ash stabilized clay soil10.1680/gein.13.00016 ↗
17. Effect of the type of soil on the cyclic behaviour of chemically stabilised soils unreinforced and reinforced with polypropylene fibres10.1016/j.soildyn.2018.09.005 ↗
18. No AccessStrength of a stabilised soil reinforced with steel fibres10.1680/jgeen.16.00200 ↗
19. Durability and mechanistic characteristics of fiber reinforced soil-cement mixtures10.1080/10298430500489207 ↗
20. Mechanical characteristics of soft clay treated with fibre and cement10.1680/gein.12.00012 ↗
21. The influence of concrete composition on Young’s modulus10.1016/j.proeng.2015.06.181 ↗
22. Neville, A.M. (1995). Properties of Concrete, Prentice Hall/Pearson Education. [4th ed.].
23. Young’s Modulus and Poisson’s ratio of concrete at high temperatures: Experimental investigations10.1016/j.matdes.2012.07.070 ↗
24. Elastic Behavior of Soil-Cement Mixtures
25. The application of artificial neural network for the development of a nonlinear aeroelastic model10.1016/j.ast.2021.106845 ↗
26. Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy10.1016/j.geoderma.2020.114227 ↗
27. Random Forests for Big Data10.1016/j.bdr.2017.07.003 ↗
28. A neural network-based intelligent cognitive state recognizer for confidence-based e-learning system10.1007/s00521-016-2430-5 ↗
29. Prediction of throw in bench blasting using neural networks: An approach10.1007/s00521-016-2423-4 ↗
30. Extreme learning machine based surrogate model for analyzing system reliability of soil slopes10.1080/19648189.2016.1169225 ↗
31. Predictive and prescriptive analytics in transportation geotechnics: Three case studies10.1016/j.treng.2021.100074 ↗
32. Piezometric level prediction based on data mining techniques10.1007/s00521-019-04392-6 ↗
33. Stability Condition Identification of Rock and Soil Cutting Slopes Based on Soft Computing10.1061/(ASCE)CP.1943-5487.0000739 ↗
34. Data-Driven Model for Stability Condition Prediction of Soil Embankments Based on Visual Data Features10.1061/(ASCE)CP.1943-5487.0000770 ↗
35. A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures10.1007/s00521-019-04399-z ↗
36. Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns10.1016/j.compgeo.2013.08.010 ↗
37. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media.
38. Hamakareem, M.I. (2022, March 29). The Constructor, Building Ideas. Available online: https://theconstructor.org/concrete/properties/factors-affecting-modulus-elasticity-concrete/5166/.
39. Venables, W.N., Smith, D.M., and R Development Core Team (2021). An Introduction to R: Notes on R, A Programming Environment for Data Analysis and Graphics, Network Theory Ltd.
40. Cortez, P. (2010, January 12–14). Data mining with neural networks and support vector machines using the r-rminer tool, in Advances in Data Mining: Applications and Theoretical Aspects. Proceedings of the 10th Industrial Conference on Data Mining, Berlin, Germany.10.1007/978-3-642-14400-4_44 ↗
41. Bi, J., and Bennett, K.P. (2003, January 21–24). Regression error characteristic curves. Proceedings of the Twentieth International Conference on Machine Learning, Washington, DC, USA.
42. Kitazume, M., and Terashi, M. (2017). The Deep Mixing Method, CRC Press/Balkema.
43. Effect of cyclic loading frequency on the behavior of a stabilized sand reinforced with polypropylene and sisal fibers10.1061/(ASCE)MT.1943-5533.0004012 ↗
44. Effect of cement treatment on geotechnical properties of some Washington State soils10.1016/j.enggeo.2008.09.003 ↗
45. The behaviour of organic matter in the process of soft soil stabilization using cement10.1007/s10064-005-0030-1 ↗