Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine

Wu J, Liu Q, Deng Y, Yu X, Feng Q, Yan C (2019) Expansive soil modified by waste steel slag and its application in subbase layer of highways. Soils Found 59:955–965

Ikeagwuani CC, Obeta IN, Agunwamba JC (2019) stabilisation of black cotton soil subgrade using sawdust ash and lime. Soils Found 59(1):162–175

Ikeagwuani CC, Nwonu DC (2019) Emerging trends in expansive soil stabilisation. J Rock Mech Geotech Eng 11:423–440

Tiwari N, Satyam N, Patva J (2020) Engineering characterisitcs and performance of polypropylene fibre and silican fume treated expansive soil subgrade. Int J Geosynth Ground Eng 6(18):1–11

Bhuvaneshwari S, Robinson RG, Gandhi SR (2020) Effect of functional group of the inorganic additives on index and microstructural properties of expansive soil. Int J Geosynth Ground Eng. https://doi.org/10.1007/s40891-020-00235-w

Jain AK, Jha AK (2020) Geotechnical behaviour and micro-analyses of expansive soil amended with marble dust,. Soils Found 60:737–751

Ikeagwuani CC, Nwonu DC, Onah HN (2020) Min-max fuzzy goal programming - Taguchi model for multiple additives optimization in expansive soil improvement. Int J Numer Anal Methods Geomech. https://doi.org/10.1002/nag.3163

Ikeagwuani CC (2019) Comparative assessment of the stabilization of lime-stabilized lateritic soil as subbase material using coconut shell ash and coconut husk ash. Geotech Geol Eng 37(4):3065–3076

Onyelowe KC (2019) Nanosized palm bunch ash (NPBA) stabilisation of lateritic soil for construction purposes. Int J Geotech Eng 13(1):83–91

Onyelowe KC, Duc BV (2018) Durability of nanostructured biomassess ash (NBA) stabilized expansive soils for pavement foundation. Int J Geotech Eng. https://doi.org/10.1080/19386362.2017.1422909

Onyelowe KC, Vsn DB, Ubachukwu O, Ezugwu C, Salahudeen B, Van MV, Ikeagwuani CC, Ahmadi T, Sosa F, Wu W, Duc TT, Eberemu A, Ducc TP, Barah O, Ikpa C, Orji F, Alaneme G, Amanamba E, Ugwuanyi H, Sai V, Kadurumba C, Subburaj S, Ugorji B (2019) Recycling and reuse of solid wastes: a hub for ecofriendly, ecoefficient and sustainable soil, concrete, wastewater and pavement reengineering. Int J Low-Carbon Technol 14(3):440–451

Ikeagwuani CC, Nwonu DC (2021) Integration of data envelopment analysis and AL-Rafaie and Al-Tahat model in Taguchi method for the optimization of additives in expansive soil treatment. Geomech Geoeng. https://doi.org/10.1080/17486025.2021.1912402

Soltani A, Deng A, Taheri A, Mirzababaei M (2018) Rubber powder-polymer combined stabilization of South Australian expansive soils. Geosynth Int 25(3):304–321

Estabragh AR, Rafatjo H, Javadi AA (2014) Treatment of an expansive soil by mechanical and chemical techniques. Geosynth Int 21(3):233–243

Etim RK, Eberemu OA, Osinubi KJ (2017) Stabilization of black cotton soil with lime and iron ore tailings admixture. Transport Geotechnics 10:85–95

Olgun M (2013) The effects and optimization of additives for expansive clays under freeze-thaw conditions. Cold Reg Sci Technol 93:36–46

Ikeagwuani CC, Nwonu DC (2020) Application of fuzzy logic and grey based Taguchi approach for additives optimization in expansive soil treatment. Road Mater Pavement Design. https://doi.org/10.1080/14680629.2020.1847726

Nwonu DC, Ikeagwuani CC (2021) Microdust effect on the physical condition and microstructure of tropical black clay. Int J Pavement Res Technol 14(1):73–84

Ikeagwuani CC, Agunwamba JC, Nwankwo CM, Eneh M (2020) Additives optimization for expansive soil subgrade modification based on Taguchi grey relational analysis,". Int J Pavement Res Technol. https://doi.org/10.1007/s42947-020-1119-4

Duque J, Fuentes W, Rey S, Molina E (2020) Effect of grain size distributionon Californai bearing ratio (CBR) and modified proctor parameters for granular materials. Arab J Sci Eng 45:8231–8238

Taha S, Gabr A, El-Badawy S (2019) Regression and neural network models for California bearing ratio prediction of typical granular materials in Egypt. Arab J Sci Eng 44:8691–8705

Bristish Standard Institute (1990) Methods of testing soils for civil engineering purposes, London: BS 1377, Part 4

Sreelekshmypillai G, Vinod P (2019) Prediction of CBR of fine grained soils at any rational compactive effort. Int J Geotech Eng 13(6):560–565

Black WPM (1962) A method of estimating the CBR of cohesive soils from plasticity data. Geotechnique 12:271–282

Yildirim B, Gunaydin O (2011) Estimation of California bearing ratio using soft computing systems. Experts Syst Appl 38:6381–6391

Bassey OB, Attach IC, Ambrose EE, Etim RK (2017) Correlation between CBR values and index properties of soils? A case study of Ibiono, Oron and Onna in Akwa Ibom State. Resour Environ 7(4):94–102

Singh D, Reddy KS, Yadu L (2011) Moisture and compaction baseed statisitcal model for estimating CBR of fine grained subgrade soils. Int J Earth Sci Eng 4(6):100–1034

Ramasubbarao GV, Sankar GS (2013) Predicting soaked CBR value of fine grained soils using index and compaction characterisitcs. Jordan J Civil Eng 7(3):354–360

Aderinola OS, Oguntoyinbo E, Quadri AI (2017) Correlation of California bearing ratio value of clays with soil index and compaction characterisitics. Int J Sci Resour Innov Technol 4(4):12–22

Aderinola OS (2017) Prediciting the Californai bearing ratio value of low compressible clays with its index compaction characterisitics. Int J Sci Eng Resour 8(5):1460–1472

Taskiran T (2010) Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Adv Eng Softw 41:886–892

NCHRP (2021) "National Cooperative Highway Research Program. Guide for mechanisitic and empiricla - design for new and rehabilitaed pavement structures, final document," Appendix CC-1: Correlation of CBR values with soil index properties: West Univsersity Avenue Champaign, ILLinois: Ara, Inc.,

I. G. Farias, W. Araujo and G. Ruiz, "Prediction of California bearing ratio from index properties of soils using parametric and non-parametric properties models," Geotechnical and geological engineering, vol. 36, no. https://doi.org/10.1007/s10706-018-0548-1, pp. 3485–3498, 2018.

Tenpe AR, Patel A (2020) Utilization of support vector models and gene expression programming for soil strength modeling. Arab J Sci Eng 45:4301–4319

Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–141

Goh ATC, Zhang W, Zhang Y, Xiao Y (2018) Determination of earthe pressure balance tunnel-related maximum surface settlement: a multivariate adaptive regression splines approach. Bull Eng Geol Env 77:489–500

Zhang W, Goh AT, Zhang Y (2016) Multivariate adaptive regression splines application for multivariate geotechnical problems with big data. Geotech Geol Eng 34(1):193–204

Acciani C, Fucilli V, Sardaro R (2011) Data mining in real estate appraisal a model tree and multivariate adaptive regression spline approach. AESTIMUM 58:27–45

Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput 9(7):1545–1588

Breiman L (2001) Random forests. Mach Learn 45:5–32

Ho T (1995) Random decision forest," in Proceedings of the 3rd International conference on document analysis and recognition, Montreal, QC, 14–16: 278–282

Ho T (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844

Breiman L (1996) Bagging predictors. Mach Learn 26(2):123–140

Breiman L (1996) Heuristics of instability and stabilization in model selection. Ann Stat 24(6):2350–2383

Buhlmann P, Yu B (2002) Analyzing bagging. Ann Stat 30(4):927–961

Chen X, Ishwaran H (2012) Random forests for genomic data analysis. Genomics 99:323–329

Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, London

Ishwaran H, Kogalur U, Blackstone E, Lauer M (2008) Random survival forest. Annals Appl Stat 2(3):841–860

Segal M, Xiao Y (2011) Multivariate random forests. WIRESs Data Min Knowl Discov 1:80–87

Amaratunga D, Cabrera J, Lee Y-S (2008) Enriched random forests. Bioinformatics 24(18):2010–2014

Meinshausen N (2006) Quantile regression forests. J Mach Learn Res 7:983–999

Xu R (2013) "Improvement to random forest methodology," PhD thesis, Iowa State University, Iowa

Yesilkanat CM (2020) Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solut Fractals 140(110210):1–8

Yao H, Li X, Pang H, Sheng L, Wang W (2020) Application of random forest algorithm in hail forecasting over Shandong Peninsula. Atmos Res. https://doi.org/10.1016/j.atmosres.2020.105093

Chun P, Ujike I, Mishima K, Kusumoto M, Okazaki S (2020) Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results. Constr Build Mater 253(119238):1–11

Khalilia M, Chakraborty S, Popescu M (2011) Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Mak 11(51):1–13

Yesilkanat CM (2020) Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitions Fractals 140(110210):1–8

Goldstein B, Polley E, Briggs F (2011) Random forests for genetic association studies. Stat Appl Genet Mole Biol 10(1):1–34

Jiang P, Wu H, Wang W, Ma W, Sun X, Lu Z (2007) Mipred: classification of real and pseudo microrna precursors using random forest prediction model with combined features. Nucleic Acids Res 35(2):339–344

Ward M, Pajevic S, Dreyfuss J, Malley J (2006) Short-term prediction of mortality in patients with systemic lupus erythematosus: Classification of outcomes using random forests. Arthritis Rheum 55:74–80

Gong H, Sun Y, Hu W, Polaczyk P, Huang B (2019) Investigating impacts of asphalt mixture properties on pavement performance using LTTP data through random forests. Constr Build Mater 204:203–212

Zhang J, Ma G, Huang Y, Sun J, Asiani F, Nener B (2019) Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Constr Build Mater 210:713–719

Gong M, Bai Y, Qin J, Wang J, Yang P, Wang S (2020) Gradient boosting machine for predicting return temperature of district heating system: a case study for residential buildings in Tianjin. J Build Eng 27:1–9

Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(3):1189–1232

Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

Freund Y, Freund Y, Shapire RE (1996) "Experiements with a new boosting algorithm," in Machine learning: proceedings of the thirteenth international conference, San Francisco: Morgan Kaufmann Publishers

Zhang M, Gong H, Jia X, Xiao R, Jiang X, Ma Y, Huang B (2020) Analysis of critical factors to asphalt overlay performance using gradient boosted models. Constr Build Mater 263(120083):1–9

De Clereq D, Wen Z, Fei F (2019) Determinants of efficiency in anaerobic bio-waste co-digestion facilities: a data envelopment analysis and gradient boosting appraoch. Appl Energy 253(113570):1–11

Persson C, Bacher P, Shiga T, Madsen H (2017) Multi-site solar power forecasting using gradient boosted regression. Sol Energy 150:423–430

Kaloop MR, Kumar D, Sammui P, Hu JW, Kim D (2020) Compressive strength prediction of high-performance concrete using gradient tree boosting machine. Constr Build Mater 264:1–11

Barua L, Zou B, Noruzoliaee M, Derrible S (2020) A gradient boosting appraoch to understanding airport runway and taxiway pavement deterioration,". Int J Pavment Eng. https://doi.org/10.1080/10298436.2020.1714616

Thai DK, Tu TM, Bui TQ, Bui TT (2019) Gradient tree boosting machine learning on predicting the failure modes of the RC panels under loads. Eng Comput. https://doi.org/10.1007/s00366-019-00842-w

Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7(21):1–21

Hastie T, Tibshirani R, Friedman J (2009) The elements of statisitical learning, New York. Springer, NY

Ikeagwuani CC, Nwonu DC, Nweke CC (2021) Resilient modulus descriptive analysis and estimation for fine-grained soils using multivariate and machine learning methods. Int J Pavement Eng. https://doi.org/10.1080/10298436.2021.1895993

Kor K, Altun G (2020) Is support vector regression method suitabe for predicting rate of penetration? J Petrol Sci Eng 194:1–18. https://doi.org/10.1016/j.petrol.2020.107542

Dietterich T (1995) Overfitting and undercomputing in machine learning. ACM Comput Surv 27(3):326–327

Flach P (2012) Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press, UK

Saud S, Jamil B, Upadhyay Y, Irshad K (2020) Performance improvement of empirical models for estimation of global solar radiation in india: a k-fold cross-validation approach. Sustain Energy Technol Assess. https://doi.org/10.1016/j.seta.2020.100768

Xiong Z, Cui Y, Liu Z, Zhao Y, Hu M, Hu J (2020) Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Comput Mater Sci 171:1–12. https://doi.org/10.1016/j.commatsci.2019.109203

Ikeagwuani CC (2019) Optimisation of additives for expansive soil reinforcement," Unpublished PhD thesis, 2019

Jekabsons G (2010) Areslab: Adaptive regression splines toolbox for matlab/octave

Liaw A, Wiener M (2002) Classification and regression by Random forest. R News 2:18–22