Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

Sarat Kumar Das1, Pijush Samui2, Akshaya Kumar Sabat3
1Department of Civil Engineering, National Institute of Technology Rourkela, Rourkela, India
2Center for Disaster Mitigation and Management, VIT University, Vellore, India
3School of Civil Engineering, KIIT University, Bhubaneswar, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

Burroughs VS (2001) Quantitative criteria for the selection and stabilization of soils for rammed earth wall construction. Ph.D. Thesis submitted to University of New South Wales

Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotechnics 33(8):454–459

Das SK, Basudhar PK (2008) Prediction of residual friction angle of clays using artifical neural network. Eng Geol 100(3–4):142–145

Goh ATC, Goh SH (2007) Support vector machines: their use in geotechnical engineering as illustrated using seismic liquefaction data. Comput Geotech 34(5):410–421

Goh TC, Kulhawy FH, Chua CG (2005) Bayesian neural network analysis of undrained side resistance of drilled shafts. J Geotech Geoenviron Eng ASCE 131(1):84–93

Horpibulsuk S, Miura N, Nagaraj TS (2003) Assessment of strength development in cement admixed high water content clays with Abrams law as basis. Geotechnique 53(2):439–444

Jarrige JF (1989) Chronology of the earlier periods of the greater as seen from Mehergrah, Pakistan. In: Alichin B (ed) South Asian archaeology. Cambridge University Press, Cambridge, pp 21–28

Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. The MIT press, Cambridge

Liong SY, Lim WH, Paudyal GN (2000) River stage forecasting in Bangladesh: neural network approach. J Comput Civ Eng 14(1):1–8

Miura N, Horpibussuk S, Nagaraj TS (2001) Engineering behavior of cement stabilized clay at high water content. Soils Found 41(5):33–45

Narendra BS, Sivapullaiah PV, Suresh S, Omkar SN (2006) Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study. Comput Geotech 33(3):196–208

Park D, Rilett LR (1999) Forecasting freeway link ravel times with a multi-layer feed forward neural network. Comput Aided Civ Infa Struct Eng 14:358–367

Porbaha A (1998) State of the art in deep mixing technology: part I. Basic concepts and overview. Ground Improv 2(2):81–92

Samui P (2008) Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Goetech 35(3):419–427

Samui P, Kurup Pradeep, Sitharam TG (2008) OCR prediction using support vector machine based on piezocone data. J Geotech Geo Environ Eng 134(6):894–898

Scholkopf B (1997) Support vector learning. R. Oldenbourg, Munich

Smola AJ, Scholkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

Tan TS, Goh TL, Yong KY (2002) Properties of Singapore marine clays improved by cement mixing. Geotech Test J ASTM 25(4):422–433

Uddin K, Balasubramaniam AS, Bergado DT (1997) Engineering behavior of cement treated Bangkok soft clay. Geotech Eng J 28(1):89–119

Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

Vapnik VN (1998) Statistical learning theory. Wiley, New York

Yin JH, Lai CK (1998) Strength and stiffness of Hong Kong marine deposit mixed with cement. Geotech Eng J 29(1):29–44