Abstract
The determination of the compression characteristics of cement-stabilized dredged soil is an important issue. However, conventional method of laboratory compression test is time-consuming. Considering its robustness, random forest (RF) regression algorithm was utilized in this research to predict the compressibility of cement-stabilized dredge soil. The input variables included fundamental physical parameters of uncemented soil, water/cement ratio, and cement content. The output variables were initial void ratio e0 and compression index Cs at the pre-yield state, which can be used to describe the compression curve. Furthermore, a comparative analysis between the RF and support vector machine (SVM) algorithms was implemented, and variable importance was also evaluated according to the RF model. Results indicate that the RF model could quickly predict Cs and e0 values of cement-stabilized dredged soil without one-dimensional oedometer tests. The RF algorithm was found to exhibit better performance than the SVM algorithm, and it has a strong ability to avoid over-fitting. The determination coefficient R2 for Cs and e0 on the validation set were found to respectively reach 0.83 and 0.97. Moreover, the importance values of the initial water content, water/cement ratio, and plastic limit were found to be 0.313, 0.249, and 0.117, respectively. The secant compression modulus Es1−2 obtained from the compression curves could be applied to the calculation of settlement and stress in engineering design.
Similar content being viewed by others
References
Anysz H, Brzozowski L, Kretowicz W, Narloch P (2020) Feature importance of stabilised rammed earth components affecting the compressive strength calculated with explainable artificial intelligence tools. Materials (Basel) 13(10), DOI: https://doi.org/10.3390/ma13102317
Benzerzour M, Amar M, Abriak NE (2017) New experimental approach of the reuse of dredged sediments in a cement matrix by physical and heat treatment. Construction and Building Materials 140:432–444, DOI: https://doi.org/10.1016/j.conbuildmat.2017.02.142
Breiman L (1996) Bagging predictors. Machine Learning 24(2):123–140, DOI: https://doi.org/10.1007/BF00058655
Breiman L (2001) Random forests. Machine Learning 45(1):5–32, DOI: https://doi.org/10.1023/a:1010933404324
Bui DT, Nhu VH, Hoang ND (2018) Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network. Advanced Engineering Informatics 38:593–604, DOI: https://doi.org/10.1016/j.aei.2018.09.005
Chan CM (2016) Geo-parametric study of dredged marine clay with solidification for potential reuse as good engineering soil. Environmental Earth Sciences 75(11):941, DOI: https://doi.org/10.1007/s12665-016-5639-9
Chen YH, Zhang WL, Zhao LY, Peng ZH (2018) Field in-situ stabilization of bored pile mud: Engineering properties and application for pavement. Construction and Building Materials 165:541–547, DOI: https://doi.org/10.1016/j.conbuildmat.2018.01.006
Cheng X, Chen YH, Chen G, Li BY (2020) Characterization and prediction for the strength development of cement stabilized dredged sediment. Marine Georesources & Geotechnology 1–10, DOI: https://doi.org/10.1080/1064119x.2020.1795014
Chew SH, Kamruzzaman AHM, Lee FH (2004) Physicochemical and engineering behavior of cement treated clays. Journal of Geotechnical and Geoenvironmental Engineering 130(7):696–706, DOI: https://doi.org/10.1061/(Asce)1090-0241(2004)130:7(696)
Dehghanbanadaki A, Khari M, Arefnia A, Ahmad K, Motamedi S (2019) A study on UCS of stabilized peat with natural filler: A computational estimation approach. KSCE Journal of Civil Engineering 23(4): 1560–1572, DOI: https://doi.org/10.1007/s12205-019-0343-4
Ding J, Wu X, Li H, Bie X, Ji F (2012) Compression properties and structure yield stress for solidified soil composing of dredged clays. Journal of Engineering Geology 20(04):627–632
Feng H, Zhang L, Li S, Liu L, Yang T, Yang P, Zhao J, Arkin IT, Liu H (2021) Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints. Toxicology Letters 340:4–14, DOI: https://doi.org/10.1016/j.toxlet.2021.01.002
Han Q, Gui C, Xu J, Lacidogna G (2019) A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm. Construction and Building Materials 226:734–742, DOI: https://doi.org/10.1016/j.conbuildmat.2019.07.315
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis & Machine Intelligence 20(8):832–844, DOI: https://doi.org/10.1109/34.709601
Horpibulsuk S, Miura N, Nagaraj TS (2005) Clay-water/cement ratio identity for cement admixed soft clays. Journal of Geotechnical and Geoenvironmental Engineering 131(2):187–192, DOI: https://doi.org/10.1061/(asce)1090-0241(2005)131:2(187)
Huang YH, Dong C, Zhang CL, Xu K (2017) A dredged material solidification treatment for fill soils in East China: A case history. Marine Georesources & Geotechnology 35(6):865–872, DOI: https://doi.org/10.1080/1064119x.2016.1257669
Huang YH, Zhu W, Qian X, Zhang N, Zhou X (2011) Change of mechanical behavior between solidified and remolded solidified dredged materials. Engineering Geology 119(3–4):112–119, DOI: https://doi.org/10.1016/j.enggeo.2011.03.005
Kaloop MR, Kumar D, Samui P, Gabr AR, Hu JW, Jin X, Roy B (2019) Particle swarm optimization algorithm-extreme learning machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases. Applied Sciences 9(16):3221, DOI: https://doi.org/10.3390/app9163221
Keum HJ, Han KY, Kim HI (2020) Real-time flood disaster prediction system by applying machine learning technique. KSCE Journal of Civil Engineering 24(9):2835–2848, DOI: https://doi.org/10.1007/s12205-020-1677-7
Khalid U, Ye GL, Yadav SK, Yin ZY (2019) A simple experimental method to regain the mechanical behavior of naturally structured marine clays. Applied Ocean Research 88:275–287, DOI: https://doi.org/10.1016/j.apor.2019.04.012
Kirts PES, Panagopoulos OP, Xanthopoulos P, Nam BH (2018) Soil-compressibility prediction models using machine learning. Journal of Computing in Civil Engineering 32(1), DOI: https://doi.org/10.1061/(Asce)Cp.1943-5487.0000713
Kordnaeij A, Moayed RZ, Soleimani M (2019) Small strain shear modulus equations for zeolite — cement grouted sands. Geotechnical and Geological Engineering 37(3), DOI: https://doi.org/10.1007/s10706-019-00964-4
Lang L, Liu N, Chen B (2020) Investigation on the strength, durability and swelling of cement- solidified dredged sludge admixed fly ash and nano-SiO2. European Journal of Environmental and Civil Engineering 1–21, DOI: https://doi.org/10.1080/19648189.2020.1776160
Lemos SGFP, Almeida MDS, Consoli NC, Nascimento TZ, Polido UF (2020) Field and laboratory investigation of highly organic clay stabilized with portland cement. Journal of Materials in Civil Engineering 32(4), DOI: https://doi.org/10.1061/(Asce)Mt.1943-5533.0003111
Li Z, Liu LL, Yan SH, Zhang MK, Xia JJ, Xie YL (2019) Effect of freeze-thaw cycles on mechanical and porosity properties of recycled construction waste mixtures. Construction and Building Materials 210:347–363, DOI: https://doi.org/10.1016/j.conbuildmat.2019.03.184
Li JS, Poon CS (2017) Innovative solidification/stabilization of lead contaminated soil using incineration sewage sludge ash. Chemosphere 173:143–152, DOI: https://doi.org/10.1016/j.chemosphere.2017.01.065
Lorenzo GA, Bergado DT (2006) Fundamental characteristics of cement-admixed clay in deep mixing. Journal of Materials in Civil Engineering 18(2):161–174, DOI: https://doi.org/10.1061/(Asce)0899-1561(2006)18:2(161)
Miura N, Horpibulsuk S, Nagaraj TS (2001) Engineering behavior of cement stabilized clay at high water content. Soils and Foundations 41(5):33–45, DOI: https://doi.org/10.3208/sandf.41.5_33
Moayed RZ, Kordnaeij A, Mola-Abasi H (2017) Compressibility indices of saturated clays by group method of data handling and genetic algorithms. Neural Computing & Applications 28:551–564, DOI: https://doi.org/10.1007/s00521-016-2390-9
Ni PP, Mangalathu S (2018) Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement. Tunnelling and Underground Space Technology 76:133–144, DOI: https://doi.org/10.1016/j.tust.2018.03.014
Ni PP, Mangalathu S, Liu KW (2020) Enhanced fragility analysis of buried pipelines through Lasso regression. Acta Geotechnica 15(2): 471–487, DOI: https://doi.org/10.1007/s11440-018-0719-5
Ni PP, Mangalathu S, Yi Y (2018) Fragility analysis of continuous pipelines subjected to transverse permanent ground deformation. Soils and Foundations 58(6):1400–1413, DOI: https://doi.org/10.1016/j.sandf.2018.08.002
Pu SY, Zhu ZD, Song WL, Wan Y, Wang HR, Song SG, Zhang J (2020) Mechanical and microscopic properties of cement stabilized silt. KSCE Journal of Civil Engineering 24(8):2333–2344, DOI: https://doi.org/10.1007/s12205-020-1671-0
Qi CC, Chen QS, Fourie A, Tang XL, Zhang QL, Dong XJ, Feng Y (2019) Constitutive modelling of cemented paste backfill: A datamining approach. Construction and Building Materials 197:262–270, DOI: https://doi.org/10.1016/j.conbuildmat.2018.11.142
Qiu JP, Guo ZB, Li L, Zhang SY, Zhao YL, Ma ZY (2020) A hybrid artificial intelligence model for predicting the strength of foam-cemented paste backfill. IEEE Access 8:84569–84583, DOI: https://doi.org/10.1109/Access.2020.2992595
Shi XC, Liu QX, Lv XJ (2012) Application of SVM in predicting the strength of cement stabilized soil. Advances in Intelligent Structure and Vibration Control 160:313–317, DOI: https://doi.org/10.4028/www.scientific.net/AMM.160.313
Solanki P (2013) Artificial neural network models to estimate resilient modulus of cementitiously stabilized subgrade soils. International Journal of Pavement Research and Technology 6(3):155–164, DOI: https://doi.org/10.6135/ijprt.org.tw/2013.6(3).155
Sridharan A, Nagaraj HB (2000) Compressibility behaviour of remoulded, fine-grained soils and correlation with index properties. Canadian Geotechnical Journal 37(3):712–722, DOI: https://doi.org/10.1139/cgj-37-3-712
Suganya K, Sivapullaiah PV (2020) Compressibility of remoulded and cement-treated Kuttanad soil. Soils and Foundations 60(3):697–704, DOI: https://doi.org/10.1016/j.sandf.2019.07.006
Sun YT, Li GC, Zhang JF, Qian DY (2019) Prediction of the strength of rubberized concrete by an evolved random forest model. Advances in Civil Engineering 2019:5198583, DOI: https://doi.org/10.1155/2019/5198583
Teerawattanasuk C, Voottipruex P (2014) Influence of clay and silt proportions on cement-treated fine-grained soil. Journal of Materials in Civil Engineering 26(3):420–428, DOI: https://doi.org/10.1061/(Asce)Mt.1943-5533.0000813
Tinoco J, Alberto A, da Venda P, Correia AG, Lemos L (2020) A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures. Neural Computing & Applications 32(13):8985–8991, DOI: https://doi.org/10.1007/s00521-019-04399-z
Wang D, Abriak NE, Zentar R (2013) Strength and deformation properties of Dunkirk marine sediments solidified with cement, lime and fly ash. Engineering Geology 166:90–99, DOI: https://doi.org/10.1016/j.enggeo.2013.09.007
Wang DX, Abriak NE, Zentar R, Xu WY (2012) Solidification/stabilization of dredged marine sediments for road construction. Environmental Technology 33(1):95–101, DOI: https://doi.org/10.1080/09593330.2011.551840
Wang DX, Xiao J, Gao XY (2019) Strength gain and microstructure of carbonated reactive MgO-fly ash solidified sludge from East Lake, China. Engineering Geology 251:37–47, DOI: https://doi.org/10.1016/j.enggeo.2019.02.012
Wang DX, Zentar R, Abriak NE (2018) Durability and swelling of solidified/stabilized dredged marine soils with class-F fly ash, cement, and lime. Journal of Materials in Civil Engineering 30(3):04018013, DOI: https://doi.org/10.1061/(Asce)Mt.1943-5533.0002187
Xiao HW, Lee FH (2014) An energy-based isotropic compression relation for cement-admixed soft clay. Geotechnique 64(5):412–418, DOI: https://doi.org/10.1680/geot.13.T.019
Yilmaz E, Belem T, Bussiere B, Mbonimpa M, Benzaazoua M (2015) Curing time effect on consolidation behaviour of cemented paste backfill containing different cement types and contents. Construction and Building Materials 75:99–111, DOI: https://doi.org/10.1016/j.conbuildmat.2014.11.008
Yin ZY, Jin YF, Liu ZQ (2020) Practice of artificial intelligence in geotechnical engineering. Journal of Zhejiang University-Science A 21(6):407–411, DOI: https://doi.org/10.1631/jzus.A20AIGE1
Yoobanpot N, Jamsawang P, Simarat P, Jongpradist P, Likitlersuang S (2020) Sustainable reuse of dredged sediments as pavement materials by cement and fly ash stabilization. Journal of Soils and Sediments 20(10):3807–3823, DOI: https://doi.org/10.1007/s11368-020-02635-x
Yuan FF (2017) Study on engineering properties of solidified tidal silt with high water content. MSc Thesis, Zhejiang University, Zhejiang, China (in Chinese)
Zhang J, Ma G, Huang Y, Sun J, Aslani F, Nener B (2019) Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Construction and Building Materials 210:713–719, DOI: https://doi.org/10.1016/j.conbuildmat.2019.03.189
Zhang DM, Zhang JZ, Huang HW, Qi CC, Chang CY (2020a) Machine learning-based prediction of soil compression modulus with application of 1D settlement. Journal of Zhejiang University-Science A 21(6):430–444, DOI: https://doi.org/10.1631/jzus.A1900515
Zhang WL, Zhao LY, McCabe BA, Chen YH, Morrison L (2020b) Dredged marine sediments stabilized/solidified with cement and GGBS: Factors affecting mechanical behaviour and leachability. Science of the Total Environment 733:138551, DOI: https://doi.org/10.1016/j.scitotenv.2020.138551
Zhou Y, Li SQ, Zhou C, Luo HB (2019) Intelligent approach based on random forest for safety risk prediction of deep foundation pit in subway stations. Journal of Computing in Civil Engineering 33(1): 05018004, DOI: https://doi.org/10.1061/(Asce)Cp.1943-5487.0000796
Zouch A, Mamindy-Pajany Y, Ennahal I, Abriak NE, Ksibi M (2020) An eco-friendly epoxy polymer binder for the treatment of Tunisian Harbor sediments: Laboratory investigations for beneficial reuse. Waste Management & Research 38(8):876–885, DOI: https://doi.org/10.1177/0734242x20910234
Acknowledgments
This research is financially supported by the Fundamental Research Funds for the Central Universities (No.B200203083; No.2019B05114), the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX20_0438), and the Zhejiang (China) Province Department of Transportation Science and Technology Project (No. 2014H28).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Guo, Q., Li, B., Chen, Y. et al. Intelligent Model for the Compressibility Prediction of Cement-Stabilized Dredged Soil Based on Random Forest Regression Algorithm. KSCE J Civ Eng 25, 3727–3736 (2021). https://doi.org/10.1007/s12205-021-2202-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-021-2202-3