863
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Shear strength characteristics of binary mixture sand-carpet fibre using experimental study and machine learning

ORCID Icon, &
Pages 449-463 | Received 28 Feb 2023, Accepted 31 Jul 2023, Published online: 17 Aug 2023

References

  • Almeida, J. L. D., Y. M. Ordoñez, P. C. A. Pudell, D. L. Brandão, M. A. Orioli, and R. L. D. S. Izzo. 2023. “Improvements in the Mechanical Behavior of a Silty Soil Treated with Rice Husk Silica, Lime, and Polypropylene Fiber.” International Journal of Geotechnical Engineering 17 (3): 1–15. https://doi.org/10.1080/19386362.2023.2227503.
  • AS1289.3.5.1. 2006. “Australian Standard”. In: Methods of Testing Soil for Engineering Purposes. Sydney, NSW, Australia: Standards Australia.
  • AS1289.6.2.2. 2020. “Australian Standard”. In: Soil Strength and Consolidation Tests - Determination of Shear Strength of a Soil - Direct Shear Test Using a Shear Box. Sydney, NSW, Australia: Standards Australia.
  • AS1774.19. 2003. “Australian Standard”. In: The Determination of Sieve Analysis and Moisture Content. Sydney, NSW, Australia: Standards Australia.
  • Baghbani, A., H. Abuel-Naga, R. Shirani Faradonbeh, S. Costa, and R. Almasoudi. 2023a. “Ultrasonic Characterization of Compacted Salty Kaolin–Sand Mixtures Under Nearly Zero Vertical Stress Using Experimental Study and Machine Learning.” Geotechnical and Geological Engineering 41 (5): 1–26. https://doi.org/10.1007/s10706-023-02441-5.
  • Baghbani, A., S. Costa, T. Choundhury, and R. S. Faradonbeh. 2022. “Prediction of Parallel Desiccation Cracks of Clays Using a Classification and Regression Tree (CART) Technique”. Paper presented at the Proceedings of the 8th International Symposium on Geotechnical Safety and Risk (ISGSR), Newcastle, Australia.
  • Baghbani, A., F. Daghistani, H. A. Naga, and S. Costa. 2022. “Development of a Support Vector Machine (SVM) and a Classification and Regression Tree (CART) to Predict the Shear Strength of Sand Rubber Mixtures”. Paper presented at the Proceedings of the 8th International Symposium on Geotechnical Safety and Risk (ISGSR), Newcastle, Australia.
  • Baghbani, A., M. D. Nguyen, A. Alnedawi, N. Milne, T. Baumgartl, and H. Abuel-Naga. 2023. “Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio.” Applied Sciences 13 (8): 4934. https://doi.org/10.3390/app13084934.
  • Bardhan, A., and P. Samui. 2022. “Application of Artificial Intelligence Techniques in Slope Stability Analysis: A Short Review and Future Prospects.” International Journal of Geotechnical Earthquake Engineering (IJGEE) 13 (1): 1–22. https://doi.org/10.4018/IJGEE.298988.
  • Chou, N. N., K.-H. Yang, B. Barrett, H.-M. Wu, and T.-Y. Liu. 2020. “Sustainable Characteristics of Reinforced Soil Structures–From Ancient Great Walls to Modern GRS Walls.” Transportation Infrastructure Geotechnology 7 (3): 445–460. https://doi.org/10.1007/s40515-020-00121-y.
  • Daghistani, F, and Abuel-Naga, H. (2023). Evaluating the Influence of Sand Particle Morphology on Shear Strength: A Comparison of Experimental and Machine Learning Approaches. Applied Sciences, 13(14), 8160 10.3390/app13148160
  • Daghistani, F., A. Baghbani, H. Abuel Naga, and R. S. Faradonbeh. 2023. “Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning.” Geosciences 13 (7): 197. https://doi.org/10.3390/geosciences13070197.
  • Diambra, A., E. Ibraim, D. M. Wood, and A. Russell. 2010. “Fibre Reinforced Sands: Experiments and Modelling.” Geotextiles and Geomembranes 28 (3): 238–250. https://doi.org/10.1016/j.geotexmem.2009.09.010.
  • Fashandi, H., H. R. Pakravan, and M. Latifi. 2019. “Application of Modified Carpet Waste Cuttings for Production of Eco-Efficient Lightweight Concrete.” Construction and Building Materials 198:629–637. https://doi.org/10.1016/j.conbuildmat.2018.11.163.
  • Gatto, M. P. A., and L. Montrasio. 2023. “Artificial Neural Network Model to Predict the Dynamic Properties of Sand-Polyurethane Composite Materials for GSI Applications.” Soil Dynamics and Earthquake Engineering 172:108032. https://doi.org/10.1016/j.soildyn.2023.108032.
  • Ghiassian, H., G. Poorebrahim, and D. H. Gray. 2004. “Soil Reinforcement with Recycled Carpet Wastes.” Waste Management & Research 22 (2): 108–114. https://doi.org/10.1177/0734242X04043938.
  • Gowthaman, S., K. Nakashima, and S. Kawasaki. 2018. “A State-Of-The-Art Review on Soil Reinforcement Technology Using Natural Plant Fiber Materials: Past Findings, Present Trends and Future Directions.” Materials 11 (4): 553. https://doi.org/10.3390/ma11040553.
  • Gray, D. H., and H. Ohashi. 1983. “Mechanics of Fiber Reinforcement in Sand.” Journal of Geotechnical Engineering 109 (3): 335–353. https://doi.org/10.1061/(ASCE)0733-9410(1983)109:3(335).
  • Harrison, A. T. 2006. A Performance Framework for the Soil Strengthening Properties of Fibre-Reinforced Sand. Bristol, United Kingdom: University of Bristol.
  • Hejazi, S. M., M. Sheikhzadeh, S. M. Abtahi, and A. Zadhoush. 2012. “A Simple Review of Soil Reinforcement by Using Natural and Synthetic Fibers.” Construction and Building Materials 30:100–116. https://doi.org/10.1016/j.conbuildmat.2011.11.045.
  • Inazumi, S., S. Intui, A. Jotisankasa, S. Chaiprakaikeow, and K. Kojima. 2020. “Artificial Intelligence System for Supporting Soil Classification.” Results in Engineering 8:100188. https://doi.org/10.1016/j.rineng.2020.100188.
  • Kazemian, S., B. B. Huat, P. Arun, and M. Barghchi. 2010. “A Review of Stabilization of Soft Soils by Injection of Chemical Grouting.” Australian Journal of Basic and Applied Sciences 4 (12): 5862–5868.
  • Kumar, S., and L. B. Roy. 2022. “Rainfall Induced Geotextile Reinforced Model Slope Embankment Subjected to Surcharge Loading: A Review Study.” Archives of Computational Methods in Engineering 29 (5): 1–19. https://doi.org/10.1007/s11831-021-09688-2.
  • Lee, S. J., S. R. Lee, and Y. S. Kim. 2003. “An Approach to Estimate Unsaturated Shear Strength Using Artificial Neural Network and Hyperbolic Formulation.” Computers and Geotechnics 30 (6): 489–503. https://doi.org/10.1016/S0266-352X(03)00058-2.
  • Liu, T. 2020. “Establishment of sustainability key indicators for civil engineering and their applications in green infrastructure projects”. Doctoral Dissertation, Department of Civil Engineering, College of Engineering, National Taiwan University
  • Liu, T.-Y., P.-H. Chen, and N. N. Chou. 2019. “Comparison of Assessment Systems for Green Building and Green Civil Infrastructure.” Sustainability 11 (7): 2117. https://doi.org/10.3390/su11072117.
  • Lotfi, E., and M. R. Akbarzadeh-T. 2014. “Practical Emotional Neural Networks.” Neural Networks 59:61–72. https://doi.org/10.1016/j.neunet.2014.06.012.
  • Nguyen, S. H. 2022. Experimental Study on Behaviour of Clayey Sand Reinforced by Polypropylene Fibre. Paper presented at the CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure: Proceedings of the 6th International Conference on Geotechnics, Civil Engineering and Structures. Ha Long, Vietnam.
  • Nguyen, M. D., A. Baghbani, A. Alnedawi, S. Ullah, B. Kafle, M. Thomas, and N. A. Milne. 2023. “Investigation on the Suitability of Aluminium-Based Water Treatment Sludge as a Sustainable Soil Replacement for Road Construction.” Transportation Engineering 12:100175. https://doi.org/10.1016/j.treng.2023.100175.
  • Oksman, K. 2000. “Mechanical Properties of Natural Fibre Mat Reinforced Thermoplastic.” Applied Composite Materials 7 (5/6): 403–414. https://doi.org/10.1023/A:1026546426764.
  • Rahman, S. S., S. Siddiqua, and C. Cherian. 2022. “Sustainable Applications of Textile Waste Fiber in the Construction and Geotechnical Industries: A Retrospect.” Cleaner Engineering and Technology 6:100420. https://doi.org/10.1016/j.clet.2022.100420.
  • Realff, M., P. Lemieux, S. Lucero, J. Mulholland, and P. Smith. 2005. “Characterization of Transient Puff Emissions from the Burning of Carpet Waste Charges in a Rotary Kiln Combustor”. Paper presented at the Conference Record Cement Industry Technical Conference, 2005. Kansas City, MO, USA.
  • Saad, A. H., H. Nahazanan, B. Yusuf, S. F. Toha, A. Alnuaim, A. El-Mouchi, and A. A. Mohammed. 2023. “A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials.” Sustainability 15 (12): 9738. https://doi.org/10.3390/su15129738.
  • Sadek, S., S. S. Najjar, and F. Freiha. 2010. “Shear Strength of Fiber-Reinforced Sands.” Journal of Geotechnical and Geoenvironmental Engineering 136 (3): 490–499. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000235.
  • Singh, B., P. Sihag, S. M. Pandhiani, S. Debnath, and S. Gautam. 2021. “Estimation of Permeability of Soil Using Easy Measured Soil Parameters: Assessing the Artificial Intelligence-Based Models.” ISH Journal of Hydraulic Engineering 27 (sup1): 38–48. https://doi.org/10.1080/09715010.2019.1574615.
  • Sriram, M., and K. A. Sidhaarth. 2022. “Various Properties of Natural and Artificial Fibers with Cementitious Composites in Hybrid Form–A Review.” Materials Today: Proceedings 60:2018–2025. https://doi.org/10.1016/j.matpr.2022.01.266.
  • Wang, Y. 2006. Recycling in Textiles. Cambridge, United Kingdom: Woodhead publishing.
  • Wang, Y. 2007. “Carpet Fiber Recycling Technologies.” Ecotextiles. 26–32. https://doi.org/10.1533/9781845693039.1.26
  • Xu, J. J., H. Zhang, C. S. Tang, Q. Cheng, B. G. Tian, B. Liu, and B. Shi. 2022. “Automatic Soil Crack Recognition Under Uneven Illumination Condition with the Application of Artificial Intelligence.” Engineering Geology 296:106495. https://doi.org/10.1016/j.enggeo.2021.106495.
  • Zhang, F., Z. Hu, Y. Liang, and Q. Li. 2023. “Evaluation of Surface Crack Development and Soil Damage Based on UAV Images of Coal Mining Areas.” Land 12 (4): 774. https://doi.org/10.3390/land12040774.
  • Zhang, W., H. Li, Y. Li, H. Liu, Y. Chen, and X. Ding. 2021. “CovidSens: A Vision on Reliable Social Sensing for COVID-19.” Artificial Intelligence Review 54 (1): 1–41. https://doi.org/10.1007/s10462-020-09852-3.
  • Zhang, W., C. Wu, H. Zhong, Y. Li, and L. Wang. 2021. “Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest Based on Bayesian Optimization.” Geoscience Frontiers 12 (1): 469–477. https://doi.org/10.1016/j.gsf.2020.03.007.