A novel groutability estimation model for ground improvement projects in sandy silt soil based on Bayesian framework

Tunnelling and Underground Space Technology

Volumn 43, Issue , 2014, Pages 453-458

  Cheng, Min Yuan ^a,c   Hoang, Nhat Duc ^b  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b DUY TAN UNIVERSITY   (Viet Nam)

^c Section 4   (Taiwan)

Author keywords

Bayesian framework; Groutability prediction; K nearest neighbor; Machine learning; Microfine cement

Indexed keywords

CONCRETE CONSTRUCTION; FORECASTING; GROUTING; HIGHWAY ENGINEERING; LEARNING SYSTEMS; MORTAR; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; PROBABILITY DENSITY FUNCTION; RAPID TRANSIT; SILT;

BAYESIAN FRAMEWORKS; CONDITIONAL PROBABILITY DENSITY; CONSTRUCTION ENGINEERING; CONSTRUCTION ENGINEERS; ESTIMATION APPROACHES; GROUND IMPROVEMENT PROJECT; K-NEAREST NEIGHBORS; MICROFINE CEMENT;

SOIL CEMENT;

BAYESIAN ANALYSIS; CEMENT; CONSTRUCTION METHOD; ESTIMATION METHOD; GROUND IMPROVEMENT; GROUTING; PREDICTION; PROBABILITY; SANDY SOIL; SILTY SOIL;

EID: 84904889231     PISSN: 08867798     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tust.2014.07.001     Document Type: Article

References (28)

1. Agrawal R.K., Bala R. Incremental Bayesian classification for multivariate normal distribution data. Pattern Recogn. Lett. 2008, 29:1873-1876.
2. Akbulut S., Saglamer A. Estimating the groutability of granular soils: a new approach. Tunn. Undergr. Space Technol. 2002, 17:371-380.
3. Bao, Y.K., Tao, Z., Guo, L.L., Wang, W., 2005. Forecasting stock composite index by fuzzy Support Vector Machine regression. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005.
4. Bishop C. Pattern Recognition and Machine Learning 2006, Springer Science+Business Media, Singapore.
5. Chen Y.-L., Azzam R., Fernandez-Steeger T., Li L. Studies on construction pre-control of a connection aisle between two neighbouring tunnels in shanghai by means of 3D FEM, neural networks and fuzzy logic. Geotech. Geol. Eng. 2009, 27:155-167.
6. Cheng M.-Y., Hoang N.-D., Wu Y.-W. Hybrid intelligence approach based on LS-SVM and Differential Evolution for construction cost index estimation: a Taiwan case study. Autom. Constr. 2013, 35:306-313.
7. Clark P., Niblett T. The CN2 induction algorithm. Mach. Learn. 1989, 3:261-283.
8. Domingos P., Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learn. 1997, 29:103-130.
9. Duda R.O., Hart P.E., Stock D.G. Pattern Classification 2001, John Wiley & Sons. second ed.
10. Greco S., Bouchon-Meunier B., Coletti G., Fedrizzi M., Matarazzo B., Yager R., Liu J.K., He Y., Wang X., Hu Y. Naive Bayesian classifier based on neighborhood probability. Adv. Comput. Intell. 2012, 112-121. Springer Berlin Heidelberg.
11. Huang C.L., Fan J.C., Yang W.J. A study of applying microfine cement grout to sandy silt soil. Sino-Geotechnics 2007, 111:71-82.
12. Incecik M., Ceren I. Cement grouting model tests. Bull. Tech. Univ. Istanbul 1995, 48:305-317.
13. Kalinli A., Acar M.C., Gunduz Z. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng. Geol. 2011, 117:29-38.
14. Kiranyaz S., Ince T., Yildirim A., Gabbouj M. Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks 2009, 22:1448-1462.
15. Kononenko I. Inductive and Bayesian learning in medical diagnosis. Appl. Artif. Intell. 1993, 7:317-337.
16. Langley P., Sage S. Induction of selective Bayesian classifiers. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence 1994, 399-406. Morgan Kaufmann, San Francisco, CA. R. Lopez de Mantaras, D. Poole (Eds.).
17. Liao K.-W., Fan J.-C., Huang C.-L. An artificial neural network for groutability prediction of permeation grouting with microfine cement grouts. Comput. Geotech. 2011, 38:978-986.
18. Ozgurel H.G., Vipulanandan C. Effect of grain size and distribution on permeability and mechanical behavior of acrylamide grouted sand. J. Geotech. Geoenviron. Eng. 2005, 131:1457-1465.
19. Perret S., Khayat K.H., Gagnon E., Rhazi J. Repair of 130-year old masonry bridge using high-performance cement grout. J. Bridge Eng. 2002, 7:31-38.
20. Perret S., Palardy D., Ballivy G. Rheological behavior and setting time of microfine cement-based grouts. Mater. J. 2000, 97:472-478.
21. Russell S.J., Norvig P. Artificial Intelligence A Modern Approach 2003, Prentice Hall, Person Education, Inc. second ed.
22. Samarasinghe S. Neural Networks for Applied Sciences and Engineering 2006, Taylor and Francis.
23. Scott D.W. Multivariate Density Estimation Theory, Practice, and Visualization 1992, John Wiley & Sons, Inc.
24. Sebastiani F. Machine learning in automated text categorization. ACM Comput. Surv. 2002, 34:1-47.
25. Tekin E., Akbas S. Artificial neural networks approach for estimating the groutability of granular soils with cement-based grouts. Bull. Eng. Geol. Environ. 2011, 70:153-161.
26. Theodoridis S., Koutroumbas K. Pattern Recognition 2009, Academic Press, Elsevier Inc.
27. Zebovitz S., Krizek R.J., Atmatzidis D.K. Injection of fine sands with very fine cement grout. J. Geotech. Eng. 1989, 115:1717-1733.
28. Zhang P. Model selection via multifold cross validation. Ann. Stat. 1993, 21:299-313.