Cohesion
is an important soil strength parameter for the overall structure and quality
of building foundations as well as slope stability. For a civil engineering
project, cohesion (c) can be determined directly from mainly unconfined
compression tests, direct shear tests, and triaxial tests of soil. However, it’s
quite challenging to collect soil samples as there are time and cost
constraints, as well as a diversity of soil deposits. Hence, this research aims
to demonstrate a simplified method in order to determine the strength parameter
of cohesive soil. Here, we propose an alternative solution adopting statistical
correlations and machine learning techniques to establish correlations between
the liquid limit, plastic limit, moisture content and %fine of soil with the
strength parameter. In laboratory testing, these parameters can be obtained
easily and these tests are relatively simple, quick to perform and also
comparatively inexpensive. Hence, several
test results were used from 100 boreholes which were soft soil or silty
clay-type soil. Using the collected in-situ and lab test results of soil samples, a Multiple Linear Regression (MLR),
Random Forest Regression (RFR) and Machine Learning (ML) model will be developed
to establish a relationship between cohesion and the available test results. In
order to assess the performances of both models, several performance indicators
like: correlation coefficient (R2), mean squared error (MSE), root
mean square error (RMSE), and mean average error (MAE) will be used. These
correlation coefficients will be used to demonstrate the prediction capacity
and accuracy of both models. It should be noted that this approach will
substitute the conventional testing required for strength parameters, which is
both expensive and time-consuming.
References
[1]
Omotoso, O.A., Ojo, O.J. and Adetolaju, E.T. (2012) Engineering Properties of in Sango Area, Ilorin, Nigeria. Earth Science Research, 1, 71-81. https://doi.org/10.5539/esr.v1n2p71
[2]
Surendra, R. and Sanjeev, K.B. (2017) Role of Geotechnical Properties of Soil on Civil Engineering Structures. Resources and Environment, 7, 103-109.
[3]
Sarker, D. and Abedin, M.Z. (2015) Applicability of Standard Penetration Test in Bangladesh and Graphical Representation of SPT-N Value. International Journal of Science and Engineering Investigations, 4, 55-59.
[4]
Temiz, C., Ari, F., Deviren Saygin, S., Arslan, S., Altay Unal, M. and Erpul, G. (2020) Soil Cohesion Development under Different Pore and Size Characteristics. 22nd EGU General Assembly, 4-8 May, 319-336. https://doi.org/10.5194/egusphere-egu2020-8325
[5]
Roy, S. and Bhalla, S.K. (2017) Role of Geotechnical Properties of Soil on Civil Engineering Structures. Resources and Environment, 7, 103-109.
[6]
Yokoi, H. (2012) Relationship between Soil Cohesion and Shear Strength. Soil Science and Plant Nutrition, 14, 89-93. https://doi.org/10.1080/00380768.1968.10432750
[7]
Suljić, P.N. (2013) Importance of Soil Cohesion on the Stability of Retaining Reinforced Concrete Wall. 17th International Research/Expert Conference: Trends in the Development of Machinery and Associated Technology, Istanbul, 10-11 September 2013. https://www.researchgate.net/publication/331453309_Importance_of_soil_cohesion_on_the_stability_of_retaining_wall
[8]
Griffiths, D.V., Fenton, G.A. and Manoharan, N. (2002) Bearing Capacity of Rough Rigid Strip Footing on Cohesive Soil: Probabilistic Study. Journal of Geotechnical and Geoenvironmental Engineering, 128, 743-755. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:9(743)
[9]
Leshchinsky, B. (2015) Bearing Capacity of Footings Placed Adjacent to c’-ϕ’ Slopes. Journal of Geotechnical and Geoenvironmental Engineering, 141, Article ID: 04015022. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001306
[10]
Torri, D., Sfalanga, M. and Del Sette, M. (1987) Splash Detachment: Runoff Depth and Soil Cohesion. Catena, 14, 149-155. https://doi.org/10.1016/S0341-8162(87)80013-9
[11]
Poodt, M.P., Koolen, A.J. and Van der Linden, J.P. (2003) FEM Analysis of Subsoil Reaction on Heavy Wheel Loads with Emphasis on Soil Preconsolidation Stress and Cohesion. Soil and Tillage Research, 73, 67-76. https://doi.org/10.1016/S0167-1987(03)00100-4
[12]
Ly, H.B. and Pham, B.T. (2020) Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model. The Open Construction & Building Technology Journal, 14, 41-50. https://doi.org/10.2174/1874836802014010041
[13]
Motaghedi, H. and Eslami, A. (2014) Analytical Approach for Determination of Soil Shear Strength Parameters from CPT and CPTu Data. Arabian Journal for Science and Engineering, 39, 4363-4376. https://doi.org/10.1007/s13369-014-1022-x
[14]
McGann, C.R., Bradley, B.A., Taylor, M.L., Wotherspoon, L.M. and Cubrinovski, M. (2015) Development of an Empirical Correlation for Predicting Shear Wave Velocity of Christ Church Soils from Cone Penetration Test Data. Soil Dynamics and Earthquake Engineering, 75, 66-75. https://doi.org/10.1016/j.soildyn.2015.03.023
[15]
Oliveira, P.J.V., Correia, A.A. and Lemos, L.J. (2017) Numerical Prediction of the Reep Behaviour of an Unsterilized and a Chemically Stabilised Soft Soil. Computers and Geotechnics, 87, 20-31. https://doi.org/10.1016/j.compgeo.2017.02.006
[16]
Yimer, Y.M., Makesh, A.P. and Muhammed, S. (2021) Prediction of Undrained Shear Strength and Correlation in between Soil Parameters. Journal of Physics: Conference Series, 2040, Article ID: 012024. https://doi.org/10.1088/1742-6596/2040/1/012024
[17]
Roy, S. and Dass, G. (2014) Statistical Models for the Prediction of Shear Strength Parameters at Sirsa, India. International Journal of Civil & Structural Engineering, 4, 483-498.
[18]
Tchakalova, B. and Ivanov, P. (2021) Correlation between Effective Cohesion and Plasticity Index of Clay. Geologica Balcanica, 51, 45-49. https://doi.org/10.52321/GeolBalc.51.3.45
[19]
Haner Kırğıl, E.N. and Erçelebi Ayyıldız, T. (2023) Predicting Software Cohesion Metrics with Machine Learning Techniques. Applied Sciences, 13, Article 3722. https://doi.org/10.3390/app13063722
[20]
Estikhamah, F. and Solin, D.P. (2021) Correlation between Cone Resistance Values and Cohesion Values in Cohesive Soils (Case Study in Gunung Anyar District). E3S Web of Conferences, 328, Article No. 01005. https://doi.org/10.1051/e3sconf/202132801005
[21]
Hossain, M.M., Sultana, N. and Malo, R.C. (2020) Correlations between CPT, SPT and Soil Parameters for Khulna, Bangladesh. Journal of Geotechnical Studies, 5, 27-32.
[22]
Mominul, H.M., Ansary, M.A. and Abedin, M.J. (2014) Applicability of Existing CPT, SPT and Soil Parameter Correlations for Bangladesh. 3rd International Symposium on Cone Penetration Testing, Las Vegas, 13-14 May 2014, 373-382.
[23]
Akan, R., Keskin, S.N. and Uzundurukan, S. (2015) Multiple Regression Model for the Pre Diction of Unconfined Compressive Strength of Jet Grout Columns. Procedia Earth and Planetary Science, 15, 299-303. https://doi.org/10.1016/j.proeps.2015.08.072
[24]
Sharma, L.K. and Singh, T.N. (2018) Regression-Based Models for the Prediction of Uncon Fined Compressive Strength of Artificially Structured Soil. Engineering with Computers, 34, 175-186. https://doi.org/10.1007/s00366-017-0528-8
[25]
Ahmad, K.I., Abrar, M., Al Shafian, S. and Shahin, H.M. (2018) Correlation among the Soil Parameters of the Karnaphuli River Tunnel Project. International Journal of GEOMATE, 15, 86-90. https://doi.org/10.21660/2018.48.7257
[26]
Zaman, M.W., Hossain, R., Hossain, S. and Al Alam, A. (2016) A Study on Correlation between Consolidation Properties of Soil with Liquid Limit, in situ Water Content, Void Ratio and Plasticity Index. The 3rd International Conference on Geotechnics for Sustainable Infrastructure Development, Hanoi, 24-25 November 2016, 899-902.
[27]
Islam, S., Shahin, H.M., Kabir, M.U. and Islam, M. (2023) Provision of Building Codes in the Context of Seismic Site Characterization and Liquefaction Susceptibility Assessment. International Journal of GEOMATE, 25, 50-58. https://doi.org/10.21660/2023.108.3795
[28]
Shahin, H.M., Kabir, M.U. and Islam, S. (2023) Seismic Hazard Analysis at Site Specific Condition: Case Study in Araihazar, Bangladesh. IUT Journal of Engineering and Technology (JET), 15, 8-20.
[29]
Kabir, M.U., Islam, M.S., Nazrul, F.B. and Shahin, H.M. (2023) Comparative Stability and Behavior Assessment of a Hill Slope on Clayey Sand Hill Tracts. International Journal of Engineering Trends and Technology, 71, 11-24. https://doi.org/10.14445/22315381/IJETT-V71I1P202
[30]
Zaman, M.W., Mita, K.S., Al Azad, A., Hossain, R. and Ul, M. (2019) A Numerical Study on Slope Stability Analysis by Finite Element Method Using Femtij-2D Application. International Conference on Disaster Risk Management (ICDRM 2019), Dhaka, 12-14 January 2019, page.
[31]
Al Shafian, S., Talha, S.A., Hossain, M. and Shahin, H.M. (2021) Understanding the Effect of Degree of Saturation on Compressive Strength Behaviour for Reconstituted Clayey Soil of Dhaka. The 11th International Conference on Geotechnique, Construction Materials and Environment, Kyoto, 3-5 November 2021, 242-246.
[32]
Al Shafian, S., Imtiyaz, M.N. and Emtiaz, M. (2023) Deformation Behaviour Analysis Using Finite Element Method during Deep Excavation in Dhaka City. Proceedings of the 8th World Congress on Civil, Structural, and Environmental Engineering (CSEE’23), Lisbon, 29-31 March 2023. https://doi.org/10.11159/icgre23.139
[33]
Al Shafian, S., Imtiyaz, M.N. and Emtiaz, M. (2023) A Finite Element Approach to Investigate the Deformation Behaviour in Deep Excavation for TBM Launching Shaft. International Journal of Civil Infrastructure, 6, 36-40. https://doi.org/10.11159/ijci.2023.005
[34]
Kabir, M.U., Hossain, S.A., Alam, M.D. and Azim, M.D. (2017) Numerical Analyses of the Karnaphuli River Tunnel. Master’s Thesis, Islamic University of Technology (IUT), Gazipur.
[35]
Tabarsa, A., Latifi, N., Osouli, A. and Bagheri, Y. (2021) Unconfined Compressive Strength Prediction of Soils Stabilized Using Artificial Neural Networks and Support Vector Machines. Frontiers of Structural and Civil Engineering, 15, 520-536. https://doi.org/10.1007/s11709-021-0689-9
[36]
Imtiyaz, M.N. (2018) Estimating Future Crash Rates on Roads in Louisiana. Master’s Thesis, Louisiana State University and Agricultural & Mechanical College, Baton Rouge. https://doi.org/10.31390/gradschool_theses.5493
[37]
Wu, Z. and Emtiaz, M. (2022) Quality Management of Cracking Distress Survey in Flexible Pavements Using LTRC Digital Highway Data Vehicle. Louisiana State University, Louisiana Transportation Research Center, No. FHWA/LA. 21/659.
[38]
Emtiaz, M. (2020) Quality Assessment of Flexible Pavement Automated Cracking Data in Louisiana. Master’s Thesis, Louisiana State University and Agricultural & Mechanical College, Baton Rouge.
[39]
Majumder, M. (2020) An Approach to Counting Vehicles from Pre-Recorded Video Using Computer Algorithms. Master’s Thesis, Louisiana State University and Agricultural & Mechanical College, Baton Rouge. https://doi.org/10.31390/gradschool_theses.5231
[40]
Majumder, M. and Wilmot, C. (2023) Automated Vehicle Counting from Pre-Recorded Video Using You Only Look Once (YOLO) Object Detection Model. Journal of Imaging, 9, Article 131. https://doi.org/10.3390/jimaging9070131
[41]
Kabir, M.U., Sakib, S.S., Rahman, I. and Shahin, H.M. (2019) Performance of ANN Model in Predicting the Bearing Capacity of Shallow Foundations. In: Sundaram, R., Shahu, J. and Havanagi, V., Eds., Geotechnics for Transportation Infrastructure, Springer, Singapore, 695-703. https://doi.org/10.1007/978-981-13-6713-7_55
[42]
Md Ghania, I.M. and Ahmadb, S. (2010) Stepwise Multiple Regression Method to Forecast Fish Landing. Procedia—Social and Behavioral Sciences, 8, 549-554.
[43]
Guan, R.D. (2023) Predicting Forest Fire with Linear Regression and Random Forest. Highlights in Science, Engineering and Technology, 44, 1-7. https://doi.org/10.54097/hset.v44i.7159
