Browse > Article
http://dx.doi.org/10.12989/gae.2022.28.4.385

A computational estimation model for the subgrade reaction modulus of soil improved with DCM columns  

Dehghanbanadaki, Ali (Department of Civil Engineering, Damavand Branch, Islamic Azad University)
Rashid, Ahmad Safuan A. (Department of Geotechnics & Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia)
Ahmad, Kamarudin (Department of Geotechnics & Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia)
Yunus, Nor Zurairahetty Mohd (Department of Geotechnics & Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia)
Said, Khairun Nissa Mat (Department of Geotechnics & Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia)
Publication Information
Geomechanics and Engineering / v.28, no.4, 2022 , pp. 385-396 More about this Journal
Abstract
The accurate determination of the subgrade reaction modulus (Ks) of soil is an important factor for geotechnical engineers. This study estimated the Ks of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of Ks. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate Ks. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of Ks. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict Ks. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m2/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m2/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of Ks.
Keywords
DCM columns; grey wolf optimization; soft soil; subgrade reaction modulus;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Zhang, Y., Jin, Z. and Chen, Y. (2020), "Hybridizing grey wolf optimization with neural network algorithm for global numerical optimization problems", Neural Comput. Appl., 32(14), 10451-10470. https://doi.org/10.1007/s00521-019-04580-4.   DOI
2 Amiri, S.T., Dehghanbanadaki, A., Nazir, R. and Motamedi, S. (2020), "Unit composite friction coefficient of model pile floated in kaolin clay reinforced by recycled crushed glass under uplift loading", Transport. Geotech., 22, 100313. https://doi.org/10.1016/j.trgeo.2019.100313.   DOI
3 Arulrajah, A., Yaghoubi, M., Disfani, M.M., Horpibulsuk, S., Bo, M.W. and Leong, M. (2018), "Evaluation of fly ash-and slag-based geopolymers for the improvement of a soft marine clay by deep soil mixing", Soil Found., 58(6), 1358-1370. https://doi.org/10.1016/j.sandf.2018.07.005.   DOI
4 MATLAB R2018b. The MathWorks: Natick, MA, USA.
5 Rashid, A. (2011), "Behaviour of weak soil reinforced with soil columns formed by deep mixing method", PhD Thesis. University of Sheffield.
6 Mat, Said K.N., Rashid, A.A., Osouli, A., Latifi, N., Yunus, N.Z.M. and Ganiyu, A.A. (2019), "Settlement evaluation of soft soil improved by floating soil cement column", Int. J. Geomech., 19(1), 0401818. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001323.   DOI
7 Topolnicki, M. (2016), "General overview and advances in deep soil mixing. In: XXIV geotechnical conference of torino design,construction and controls of soil improvement systems", 25-26 February, Torino, 1-30.
8 Bergado, D.T., Ruenkrairergsa, T., Taesiri, Y. and Balasubramaniam, A.S. (1999), "Deep soil mixing used to reduce embankment settlement", Ground Improvement, 3, 145-162. https://doi.org/10.1680/gi.1999.030402.   DOI
9 Bouassida, M., Fattah, M.Y. and Mezni, N. (2020), "Bearing capacity of foundation on soil reinforced by deep mixing columns", Geomech. Geoeng., 1-12. https://doi.org/10.1080/17486025.2020.1755458.   DOI
10 Bruce, M., Berg, R., Collin, J., Filz, G., Terashi, M. and Yang, D. (2013), "Federal highway administration design manual: Deep mixing for embankment and foundation support", (No. FHWA-HRT-13-046). Federal Highway Administation, U.S. Department of Transportation.
11 Porbaha, A. (2000), "State of the art in deep mixing technology: part IV. Design considerations", Ground Improv., 3, 111-125. https://doi.org/10.1680/grim.2000.4.3.111.   DOI
12 Inagaki, M., Abe, T., Yamamoto, M., Nozu, M., Yanagawa, Y. and Li, L. (2002), "Behavior of cement deep mixing columns under road embankment", Proceedings of the 5th international conference on physical modelling in geotechnics: ICPMG.
13 Dehghanbanadaki, A. (2020), "Intelligent modelling and design of soft soil improved with floating column-like elements as a road subgrade", Transport. Geotech., 100428. https://doi.org/10.1016/j.trgeo.2020.100428.   DOI
14 Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv. Eng. Softw., 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007.   DOI
15 CDIT (Coastal Development Institute of Technology) (2002), "The Deep Mixing Method - Principle, Design and Construction, Balkelma Rotterdam, the Netherlands".
16 Dehghanbanadaki, A., Motamedi, S. and Ahmad, K. (2020), "Fem-based modelling of stabilized fibrous peat by end-bearing cement deep mixing columns", Geomech. Eng., 20(1), 75-86. https://doi.org/10.12989/gae.2019.20.1.075.   DOI
17 Muttuvel, T., Iyathurai, S. and Ameratunga, J. (2021), "Deep soil mixing", In Soft Clay Engineering and Ground Improvement (pp. 335-358). CRC Press.
18 Park, J.M., Jo, Y.S. and Jang, Y.S. (2019), "Reliability assessment of geotechnical structures on soil improved by deep mixing method I: Data collection and problem setting", KSCE J. Civil Eng., 23(1), 63-73. https://doi.org/10.1007/s12205-018-1135-y.   DOI
19 Rashid, A.S.A., Black, J.A. and Noor, N.M. (2015a), "Behaviour of weak soil reinforced with soil cement columns formed by the deep mixing method: Rigid and flexible footings", Measurement: J. Int. Measurement Confederation, 68, 262-279. https://doi.org/10.1016/j.measurement.2015.02.039.   DOI
20 Rashid, A.S.A., Black, J.A., Mohamad, H. and Noor, N.M. (2015b), "Behavior of weak soil reinforced with end-bearing soil-cement columns formed by the deep mixing method", 33(6), 473-486. https://doi.org/10.1080/1064119X.2014.954174.   DOI
21 Javadi, A.A. and Rezania, M. (2009), "Applications of artificial intelligence and data mining techniques in soil modeling", Geomech. Eng., 1(1), 53-74. https:// doi.org/10.12989/gae.2009.1.1.053.   DOI
22 Keshtkarbanaeemoghadam, A., Dehghanbanadaki, A. and Kaboli, M.H. (2018), "Estimation and optimization of heating energy demand of a mountain shelter by soft computing techniques", Sustain. Cities Soc., 41, 728-748. https://doi.org/10.1016/j.scs.2018.06.008.   DOI
23 Liu, J., Jiang, Y., Zhang, Y. and Sakaguchi, O. (2021), "Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns", Geomech. Eng., 25(6), 439-453. https://doi.org/10.12989/gae.2021.25.6.439.   DOI
24 Kitazume, M., Okano, K. and Miyajima S. (2000), "Centrifuge model tests on failure envelope of column type deep mixing method improved ground", Soil Found, 40, 43-55. https://doi.org/10.3208/sandf.40.4_43.   DOI
25 Kitazume, M. (2020), "Keynote Lecture: Recent development of quality control and assurance of deep mixing method", In: Duc Long P., Dung N. (eds) Geotechnics for Sustainable Infrastructure Development. Lecture Notes in Civil Engineering, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-15-2184-3_69.   DOI
26 Le, Kouby A., Guimond-Barrett, A., Reiffsteck, P., Pantet, A., Mosser, J.F. and Calon, N. (2020), "Improvement of existing railway subgrade by deep mixing", Eur. J. Environ. Civil Eng., 24(8), 1229-1244. https://doi.org/10.1080/19648189.2018.1456977.   DOI
27 Dehghanbanadaki, A., Ahmad, K. and Ali, N. (2016), "Experimental investigations on ultimate bearing capacity of peat stabilized by a group of soil-cement column: a comparative study", Acta Geotechnica, 11(2) 295-307. https://doi.org/10.1007/s11440-014-0328-x.   DOI
28 EuroSoilStab. (2002), "Development of Design and Construction Methods to Stabilise Soft Organic Soil Design Guide Soft Soil Stabilisation; CT97-0351", Project No. BE 96-3177, Industrial & Materials.
29 Dehghanbanadaki, A., Mahdy, K., Arefnia, A., Ahmad, K. and Motamedi, S. (2019), "A study on UCS of stabilized peat with natural filler: a computational estimation approach", KSCE J. Civil Eng., 23(4), 1560-1572. https://doi.org/10.1007/s12205-019-0343-4.   DOI
30 Dehghanbanadaki, A., Khari, M., Amiri, S.T. and Armaghani, D.J. (2021), "Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study", Soft Comput., 25(5), 4103-4119. https://doi.org/10.1007/s00500-020-05435-0.   DOI
31 Fang, X., Cao, C., Chen, Z., Chen, W., Ni, L., Ji, Z. and Gan, J. (2020), "Using mixed methods to design service quality evaluation indicator system of railway container multimodal transport", Sci. Progress, 103(1), 0036850419890491. https://doi.org/10.1177/0036850419890491.   DOI
32 Faris, H., Mirjalili, S. and Aljarah, I. (2019), "Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme", Int. J. Machine Learn. Cy., 10(10), 2901-2920. https://doi.org/10.1007/s13042-018-00913-2.   DOI
33 Garson, G.D. (1991), "Interpreting connection neural-network weights", Artif. Intell. Exp., 6(7), 47-5.
34 Himanshu, N., Kumar, V., Burman, A., Maity, D. and Gordan, B. (2020), "Grey wolf optimization approach for searching critical failure surface in soil slopes", Eng. with Comput., 1-14. https://doi.org/10.1007/s00366-019-00927-6.   DOI
35 SCDOT. (2010), "South Carolina Department of Transportation", Geotechnical design manual. Chapter 19; Ground improvement.
36 Frikha, W., Zargayouna, H., Boussetta, S. and Bouassida, M. (2017), "Experimental study of Tunis soft soil improved by deep mixing column", Geotech. Geol. Eng., 35(3), 931-947. https://doi.org/10.1007/s10706-016-0151-2.   DOI
37 Lin, K.Q. and Wong, I.H. (1999), "Use of deep cement mixing to reduce settlements at bridge approaches", ASCE J. Geotech. Geoenviron. Eng., 125(4), 309-320. https://doi.org/10.1061/(ASCE)1090 0241(1999)125:4(309).   DOI
38 Yin, J.H. and Fang, Z. (2010), "Physical modeling of a footing on soft soil ground with deep cement mixed soil columns under vertical loading", Mar. Georesour. Geotech.; 28(2), 173-188. https://doi.org/10.1080/10641191003780872.   DOI
39 Bellato, D., Marzano, I.P. and Simonini, P. (2020), "Microstructural analyses of a stabilized sand by a deep-mixing method", J. Geotech. Geoenviron. Eng., 146(6), 04020032. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002254.   DOI
40 Rashid, A.S.A., Bunawan, A.R. and Said, K.N.M. (2017), "The deep mixing method: bearing capacity studies", Geotech. Geol. Eng., 35(4), 1271-1298. https://doi.org/10.1007/s10706-017-0196-x.   DOI
41 Shen, S.L, Han, J. and Du, Y.J. (2008), "Deep mixing induced property changes in surrounding sensitive marine clays", J. Geotech. Geoenviron. Eng., 134(6), 845-54. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:6(845).   DOI
42 Terzaghi, K. (1955), "Evaluation of coefficients of subgrade reaction", Geotechnique, 5(4), 297-326.   DOI
43 Winkler, E. (1867), "Die Lehre Von Elasticitaet Und Festigkeit 1st edition Prague: H. Dominicus".
44 Waichita, S., Jongpradist, P. and Jamsawang, P. (2019), "Characterization of deep cement mixing wall behavior using wall-to-excavation shape factor", Tunn. Undergr. Sp. Tech., 83, 243-253. https://doi.org/10.1016/j.tust.2018.09.033.   DOI
45 Xiang, G., Yin, D., Cao, C. and Yuan, L. (2021), "Application of artificial neural network for prediction of flow ability of soft soil subjected to vibrations", Geomech. Eng., 25(5), 395-403. https://doi.org/10.12989/gae.2021.25.5.395.   DOI