Efficient prediction of compressive strength in geotechnical engineering using artificial neural networks

Abstract

In recent years, artificial neural networks (ANNs) have emerged as highly effective tools for addressing the intricate challenges encountered in geotechnical engineering. ANNs find application in a variety of geotechnical problems, showcasing promising outcomes. This study aims to improve the efficiency of predicting intermediate values from unconfined compressive strength (UCS) data obtained from laboratory tests through the use of ANNs. The modelling of artificial neural networks was carried out using the Regression Learner program, integrated with the Matlab 2023a software package, offering a user-friendly graphical interface for AI model development without the need for coding. The ANNs' validation and training were based on UCS test data obtained from the Geotechnical Laboratory of Iowa State University, USA. These laboratory tests focused on engineering properties, specifically the UCS of soils treated with biofuel co-products (BCPs). The dataset, organized in a matrix of size 216 × 5, features columns providing information on soil type (Soil 1; Soil 2; Soil 3; Soil 4), sample type (pure soil-untreated; 12% BCP- treated soil; 3% cement; 6% cement; 12% cement treated soil), time (1, 7, and 28 days), moisture content (OMC-4%, OMC%, and OMC+4%), and corresponding UCS peak stress (psi) values. The AI predictions for the test data output achieved an outstanding R2 score of 0.93, showcasing the potential of employing ANNs to efficiently acquire a substantial amount of data with fewer experiments and in less time. This approach holds promise for applications in geotechnical engineering.

Keywords

• Artificial intelligence
• Regression learner
• UCS
• ANN

References

1. Ren, J., & Sun, X. (2023). Prediction of ultimate bearing capacity of pile foundation based on two optimization algorithm models. Buildings, 13(5), 1242. https://doi.org/10.3390/buildings13051242
2. Jaksa, M. B. (1995). The influence of spatial variability on the geotechnical design properties of a stiff, overconsolidated clay [Doctoral dissertation, University of Adelaide].
3. Hubick, K. T. (1992). Artificial neural networks in Australia, Technology and Commerce; Commonwealth of Australia: Canberra, Australia.
4. Chao, Z., Ma, G., Zhang, Y., Zhu, Y., & Hu, H. (2018, November). The application of artificial neural network in geotechnical engineering. In IOP conference series: Earth and environmental science, 189, 022054. https://doi.org/10.1088/1755-1315/189/2/022054
5. Kesikoğlu, M. H., Cicekli, S. Y., & Kaynak, T. (2020). The identification of seasonal coastline changes from landsat 8 satellite data using artificial neural networks and k-nearest neighbor. Turkish Journal of Engineering, 4(1), 47-56. https://doi.org/10.31127/tuje.599359
6. Shahin, M. A., Jaksa, M. B., & Maier, H. R. (2001). Artificial neural network applications in geotechnical engineering. Australian Geomechanics, 36(1), 49-62.
7. Das, S. K., & Basudhar, P. K. (2006). Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics, 33(8), 454-459. https://doi.org/10.1016/j.compgeo.2006.08.006
8. GOH, A. C. (1994). Nonlinear modelling in geotechnical engineering using neural networks. Transactions of the Institution of Engineers, Australia. Civil engineering, 36(4), 293-297.

9. Ghaboussi, J., & Sidarta, D. E. (1998). New nested adaptive neural networks (NANN) for constitutive modeling. Computers and Geotechnics, 22(1), 29-52. https://doi.org/10.1016/S0266-352X(97)00034-7
10. Das, S. K., & Basudhar, P. K. (2005). Prediction of coefficient of lateral earth pressure using artificial neural networks. Electronic Journal of Geotechnical Engineering, 10.
11. Shahin, M., Jaksa, M., & Maier, H. (2009). Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Advances in Artificial Neural Systems.
12. Yang, X. S., Gandomi, A. H., Talatahari, S., & Alavi, A. H. (Eds.). (2012). Metaheuristics in water, geotechnical and transport engineering. Newnes.
13. Grima, M. A., & Babuška, R. (1999). Fuzzy model for the prediction of unconfined compressive strength of rock samples. International Journal of Rock Mechanics and Mining Sciences, 36(3), 339-349. https://doi.org/10.1016/S0148-9062(99)00007-8
14. Ceylan, H., Gopalakrishnan, K., & Kim, S. (2010). Soil stabilization with bioenergy coproduct. Transportation Research Record, 2186(1), 130-137. https://doi.org/10.3141/2186-14
15. Vardhan, H., Bordoloi, S., Garg, A., & Garg, A. (2017). Compressive strength analysis of soil reinforced with fiber extracted from water hyacinth. Engineering Computations, 34(2), 330-342. https://doi.org/10.1108/EC-09-2015-0267
16. Öztürk, O., & Türköz, M. (2022). Effect of silica fume on the undrained strength parameters of dispersive. Turkish Journal of Engineering, 6(4), 293-299. https://doi.org/10.31127/tuje.1001413
17. Yang, B. (2015). Performance of bio-based soil stabilizers in transportation earthworks-laboratory investigations. [Master's Thesis, Iowa State University].
18. Uzer, A. U. (2015). Use of biofuel co-product for pavement geo-materials stabilization. Procedia Engineering, 125, 685-691. https://doi.org/10.1016/j.proeng.2015.11.106
19. ASTM D2166. (2006). Standard test method for unconfined compressive strength of cohesive soil. In Annual Book of ASTM standards. West Conshohocken: ASTM International.
20. Zhang, T., Cai, G., Liu, S., & Puppala, A. J. (2014). Stabilization of silt using a lignin-based bioenergy coproduct. Transportation Research Board 93rd Annual MeetingTransportation Research Board.
21. Zhang, T., Liu, S., Cai, G., & Puppala, A. J. (2015). Experimental investigation of thermal and mechanical properties of lignin treated silt. Engineering Geology, 196, 1-11. https://doi.org/10.1016/j.enggeo.2015.07.003
22. Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models. 4th Edition, WCB McGraw-Hill, New York.
23. Seber, G. A., & Lee, A. J. (2012). Linear regression analysis. John Wiley & Sons.
24. Freedman, D. A. (2009). Statistical models: theory and practice. Cambridge University Press.
25. Muggeo, V. M. (2003). Estimating regression models with unknown break‐points. Statistics in Medicine, 22(19), 3055-3071. https://doi.org/10.1002/sim.1545
26. Hemanth, D. J., Gupta, D., & Balas, V. E. (Eds.). (2019). Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions. Academic Press.
27. https://dirask.com/posts/JavaScriptartificial-neuron-model-paoM31
28. Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 326). John Wiley & Sons.
29. Glantz, S. A., Slinker, B. K., & Neilands, T. B. (2001). Primer of applied regression & analysis of variance, ed (Vol. 654). McGraw-Hill, Inc., New York.
30. Demir, V., & Doğu, R. (2024). Prediction of elevation points using three different heuristic regression techniques. Turkish Journal of Engineering, 8(1), 56-64. https://doi.org/10.31127/tuje.1257847