Machine Learning Classifiers for Modeling Soil Characteristics by Geophysics Investigations: A Comparative Study
Source abstract
Abstract
To design geotechnical structures efficiently, it is important to examine soil’s physical properties. Therefore, classifying soil with respect to geophysical parameters is an advantageous and popular approach. Novel, quick, cost, and time effective machine learning techniques can facilitate this classification. This study employs three kinds of machine learning models, including the Decision Tree, Artificial Neural Networks, and Bayesian Networks. The Decision tree models included the chi-square automatic interaction detection (CHAID), classification and regression trees (CART), quick, unbiased, and efficient statistical tree (QUEST), and C5; the Artificial Neural Networks models included Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF); and BN models included the Tree Augmented Naïve (TAN) and Markov Blanket, which were employed to predict the soil classifications using geophysics investigations and laboratory tests. The performance of each model was assessed through the accuracy, stability and gains. The results showed that while the BAYESIANMARKOV model achieved the highest overall accuracy (100%) in training phase, this model achieved the lowest accuracy (34.21%) in testing phases. Thus, this model had the worst stability. The QUEST had the second highest overall training accuracy (99.12%) and had the highest overall testing accuracy (94.74%). Thus, this model was somewhat stable and had an acceptable overall training and testing accuracy to predict the soil characteristics. The future studies can use the findings of this paper as a benchmark to classify the soil characteristics and select the best machine learning technique to perform this classification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Soil and Unsaturated Flow · Grouting, Rheology, and Soil Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Geotropik—Centre of Tropical Geoengineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Department of Civil Engineering, Faculty of Engineering, Arak University, 38156-8-8349 Arak, Iran
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Geophysics Section, School of Physics, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
References (78)
Show source-provided references
1. The practical application of seismic testing in geotechnical engineering
2. Patrick, J.M. (1990). Digital Borehole Logging Instrumentation and Software, a Systems Approach to Design and Implementation. Symposium on the Application of Geophysics to Engineering and Environmental Problems 1990, European Association of Geoscientists & Engineers.10.4133/1.2921872 ↗
3. Imai, T., and Tonouchi, K. (1982, January 24–27). Correlation of N-value with S-wave velocity and shear modulus. Proceedings of the 2nd European Symposium of Penetration Testing, Amsterdam, The Netherlands.
4. Kaneko, F., Kanemori, T., and Tonouchi, K. (1990). Low-frequency shear wave logging in unconsolidated formations for geotechnical applications. Geophysical Applications for Geotechnical Investigations, ASTM International.10.1520/STP17252S ↗
5. Correlation of seismic P-wave velocities with engineering parameters (N value and rock quality) for tropical environmental study10.4236/ijg.2012.34075 ↗
6. Correlation of Schmidt rebound hardness with uniaxial compressive strength and P-wave velocity of rock materials10.1007/s13369-014-1510-z ↗
7. An empirical relationship for determining shear wave velocity in granular materials accounting for grain morphology10.1520/GTJ100796 ↗
8. Lau, K.C. (1998). A Review of Downhole Geophysical Methods for Ground Investigation.
9. Ulusay, R., and Hudson, J.A. (2007). ISRM (2007) The Complete ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 1974–2006, ISRM Turkish National Group.
10. Harlianto, P.A., Adji, T.B., and Setiawan, N.A. (2017, January 11–12). Comparison of machine learning algorithms for soil type classification. Proceedings of the 2017 3rd International Conference on Science and Technology-Computer (ICST), Yogyakarta, Indonesia.10.1109/ICSTC.2017.8011843 ↗
11. Neural networks in geophysical applications10.1190/1.1444797 ↗
12. Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques10.1016/j.tra.2020.04.013 ↗
13. Azizi, A., and Seifipour, N. (2009, January 17–20). Modeling of dermal wound healing-remodeling phase by Neural Networks. Proceedings of the Computer Science and Information Technology-Spring Conference, Singapore.10.1109/IACSIT-SC.2009.121 ↗
14. Azizi, A. (2019). Applications of Artificial Intelligence Techniques in Industry 4.0, Springer.10.1007/978-981-13-2640-0 ↗
15. Forecasting gold price changes: Rolling and recursive neural network models10.1016/j.mulfin.2007.12.002 ↗
16. Hajihassani, M., Abdullah, S.S., Asteris, P.G., and Armaghani, D.J. (2019). A Gene Expression Programming Model for Predicting Tunnel Convergence. Appl. Sci., 9.10.3390/app9214650 ↗
17. Chen, H., Asteris, P.G., Jahed Armaghani, D., Gordan, B., and Pham, B.T. (2019). Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models. Appl. Sci., 9.10.3390/app9061042 ↗
18. Asteris, P.G., Moropoulou, A., Skentou, A.D., Apostolopoulou, M., Mohebkhah, A., Cavaleri, L., Rodrigues, H., and Varum, H. (2019). Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects. Appl. Sci., 9.10.3390/app9020243 ↗
19. Huang, L., Asteris, P.G., Koopialipoor, M., Armaghani, D.J., and Tahir, M.M. (2019). Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength. Appl. Sci., 9.10.3390/app9245372 ↗
20. Armaghani, D.J., Asteris, P.G., Fatemi, S.A., Hasanipanah, M., Tarinejad, R., Rashid, A.S.A., and Huynh, V. (2020). Van On the Use of Neuro-Swarm System to Forecast the Pile Settlement. Appl. Sci., 10.10.3390/app10061904 ↗
21. Mapping and holistic design of natural hydraulic lime mortars10.1016/j.cemconres.2020.106167 ↗
22. Sarir, P., Chen, J., Asteris, P.G., Armaghani, D.J., and Tahir, M.M. (2019). Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns. Eng. Comput.10.1007/s00366-019-00808-y ↗
23. Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks
24. Anisotropic masonry failure criterion using artificial neural networks10.1007/s00521-016-2181-3 ↗
25. Asteris, P.G., and Nikoo, M. (2019). Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput. Appl.10.1007/s00521-018-03965-1 ↗
26. Soft computing-based techniques for concrete beams shear strength10.1016/j.prostr.2019.08.123 ↗
27. Apostolopoulour, M., Douvika, M.G., Kanellopoulos, I.N., Moropoulou, A., and Asteris, P.G. (2018, January 10–13). Prediction of Compressive Strength of Mortars using Artificial Neural Networks. Proceedings of the 1st International Conference TMM_CH, Transdisciplinary Multispectral Modelling and Cooperation for the Preservation of Cultural Heritage, Athens, Greece.
28. Effect of crack angles on energy characteristics of sandstones under a complex stress path10.1016/j.engfracmech.2019.106577 ↗
29. Effect of loading frequency on the deformation behaviours of sandstones subjected to cyclic loads and its underlying mechanism10.1016/j.ijfatigue.2019.105349 ↗
30. The mechanical and microstructural properties of refuse mudstone-GGBS-red mud based geopolymer composites made with sand10.1016/j.conbuildmat.2020.119193 ↗
31. Deformation characteristics of sandstones during cyclic loading and unloading with varying lower limits of stress under different confining pressures10.1016/j.ijfatigue.2019.06.007 ↗
32. Cemented paste backfill for mineral tailings management: Review and future perspectives10.1016/j.mineng.2019.106025 ↗
33. Integrated and intelligent design framework for cemented paste backfill: A combination of robust machine learning modelling and multi-objective optimization10.1016/j.mineng.2020.106422 ↗
34. Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill10.1016/j.conbuildmat.2017.11.006 ↗
35. Kordjazi, A., Pooya Nejad, F., and Jaksa, M.B. (2015, January 22–25). Prediction of load-carrying capacity of piles using a support vector machine and improved data collection. Proceedings of the 12th Australia New Zealand Conference on Geomechanics, Wellington, New Zealand.
36. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition10.1016/j.tust.2016.12.009 ↗
37. Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction10.1007/s11069-015-1842-3 ↗
38. Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories10.1016/j.ssci.2019.05.046 ↗
39. Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining10.1177/1077546314568172 ↗
40. Evaluation method of rockburst: State-of-the-art literature review10.1016/j.tust.2018.08.029 ↗
41. Model test on the entrainment phenomenon and energy conversion mechanism of flow-like landslides10.1016/j.enggeo.2018.03.023 ↗
42. Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass10.1016/j.tust.2018.07.023 ↗
43. Micromechanical modeling of dynamic compressive responses of mesoscopic heterogenous brittle rock10.1016/j.tafmec.2007.04.008 ↗
44. Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading10.1016/j.ijrmms.2014.03.003 ↗
45. A quasi-three-dimensional spring-deformable-block model for runout analysis of rapid landslide motion10.1016/j.enggeo.2014.11.016 ↗
46. Classification of rockburst in underground projects: Comparison of ten supervised learning methods10.1061/(ASCE)CP.1943-5487.0000553 ↗
47. Armaghani, D.J., Koopialipoor, M., Marto, A., and Yagiz, S. (2019). Application of several optimization techniques for estimating TBM advance rate in granitic rocks. J. Rock Mech. Geotech. Eng.10.1016/j.jrmge.2019.01.002 ↗
48. Prediction of bearing capacity of thin-walled foundation: A simulation approach10.1007/s00366-017-0542-x ↗
49. Momeni, E., Dowlatshahi, M.B., Omidinasab, F., Maizir, H., and Armaghani, D.J. (2020). Gaussian Process Regression Technique to Estimate the Pile Bearing Capacity. Arab. J. Sci. Eng.10.1007/s13369-020-04683-4 ↗
50. Soil texture classification using multi class support vector machine
51. Machine learning in soil classification10.1016/j.neunet.2006.01.005 ↗
52. Machine learning for predicting soil classes in three semi-arid landscapes10.1016/j.geoderma.2014.09.019 ↗
53. Rock and soil classification using PLS-DA and SVM combined with a laser-induced breakdown spectroscopy Library10.1088/1009-0630/17/8/08 ↗
54. Abraham, S., Huynh, C., and Vu, H. (2020). Classification of soils into hydrologic groups using machine learning. Data, 5.10.3390/data5010002 ↗
55. Rahman, S.A.Z., Mitra, K.C., and Islam, S.M.M. (2018, January 21–23). Soil classification using machine learning methods and crop suggestion based on soil series. Proceedings of the 2018 21st International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh.10.1109/ICCITECHN.2018.8631943 ↗
56. Soil type characterization for moisture estimation using machine learning and UWB-Time of Flight measurements10.1016/j.measurement.2019.06.042 ↗
57. Hengl, T., Mendes de Jesus, J., Heuvelink, G.B.M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.10.1371/journal.pone.0169748 ↗
58. Mapping numerically classified soil taxa in Kilombero Valley, Tanzania using machine learning10.1016/j.geoderma.2016.11.020 ↗
59. V An exploratory technique for investigating large quantities of categorical data
60. Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, CERN Library. Wadsworth & Brooks.
61. Split selection methods for classification trees
62. Kaufmann, M. (1993). C4. 5: Programs for Machine Learning, Elsevier.
63. A survey on image classification approaches and techniques
64. Forecast of water demand in Weinan City in China using WDF-ANN model10.1016/S1474-7065(03)00026-3 ↗
65. Prediction of flyrock in boulder blasting using artificial neural network
66. Simulation of blasting-induced air overpressure by means of Artificial Neural Networks
67. Asteris, P.G., Argyropoulos, I., Cavaleri, L., Rodrigues, H., Varum, H., Thomas, J., and Lourenço, P.B. (2018, January 10–13). Masonry compressive strength prediction using artificial neural networks. Proceedings of the International Conference on Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage, Athens, Greece.10.1007/978-3-030-12960-6_14 ↗
68. Asteris, P., Roussis, P., and Douvika, M. (2017). Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors, 17.10.3390/s17061344 ↗
69. Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique10.1109/ACCESS.2019.2927632 ↗
70. Kennedy, J. (2011). Particle swarm optimization. Encyclopedia of Machine Learning, Springer.10.1007/978-0-387-30164-8_630 ↗
71. Madden, M.G. (2002). Evaluation of the performance of the markov blanket bayesian classifier algorithm. arXiv.
72. Bayesian network classifiers10.1023/A:1007465528199 ↗
73. Zheng, F., and Webb, G.I. (2010). Tree Augmented Naive Bayes, Springer.10.1007/978-0-387-30164-8_850 ↗
74. Pearl, J. (2014). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Elsevier.
75. Richter, C.F. (1958). Elementaty Seismology, Freeman.
76. Evaluation of various downhole data reduction methods for obtaining reliable VS profiles10.1520/GTJ11811 ↗
77. Lambe, T.W., and Whitman, R.V. (1991). Soil Mechanics, John Wiley & Sons.
78. Guo, H., Zhou, J., Koopialipoor, M., Armaghani, D.J., and Tahir, M.M. (2019). Deep neural network and whale optimization algorithm to assess flyrock induced by blasting. Eng. Comput.10.1007/s00366-019-00816-y ↗