Prediction of Settlement of Soft Clay Foundation in Highway Using Artifical Neural Networks

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Abstract:

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of Highway embankment, accurate prediction of settlement of soft clay foundation in highway is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting settlement of soft clay foundation based on the observation data of settlement. Approximately 200 data sets, obtained from the Field Tests and the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate settlement predictions for soft clay foundation in highway.

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Periodical:

Advanced Materials Research (Volumes 443-444)

Pages:

15-20

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Online since:

January 2012

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[1] J.E. Bowles, Foundation analysis and design (5th ed. ), McGraw-Hill (1996).

Google Scholar

[2] J.L. Briaud, M. Ballouz and G. Nasr, Static capacity prediction by dynamic methods for three bored piles, J Geotech Geoenviron Eng ASCE 126 (7) (2000), p.640–649.

DOI: 10.1061/(asce)1090-0241(2000)126:7(640)

Google Scholar

[3] B.B. Broms and L. Hellman, End bearing and skin friction resistance of piles, J Soil Mech Found Div, Proc ASCE 94 (SM2) (1968), p.421–429.

DOI: 10.1061/jsfeaq.0001104

Google Scholar

[4] CTCI Corporation. Bureau of Kaohsiung Mass Rapid TransitBureau of Kaohsiung Mass Rapid Transit vol. 1 (1991) p.1–202.

Google Scholar

[5] Chang KR, Lin ML. Analysis of consolidation settlement caused by withdrawing of multi-well water at Meiliao area. Master Thesis of Civil Engineering, National Taiwan University, Taiwan; (1999).

Google Scholar

[6] Duh FL, Lue CJ. A study on the problem of consolidation settlement due to withdrawing of water. Master Thesis of Civil Engineering, Chung Fua University, Taiwan; (1992).

Google Scholar

[7] G.W. Clough and L.A. Hansen, Clay anisotropy and braced wall behavior. Journal of Geotechnical Engineering, ASCE 107 7 (1981), p.893–913. View Record in Scopus | Cited By in Scopus (21).

DOI: 10.1061/ajgeb6.0001168

Google Scholar

[8] G.W. Clough and T.D. O'Rourke, Construction induced movement of in-situ walls. In: Proceeding of Design and Performance of Earth Retaining Structures, ASCE, Special Conference, Ithaca, N.Y. (1990), p.439–470.

Google Scholar

[9] A.P. Deane, Application of NATM to design of underground station in London clay. In: Proceedings of Seventh International Symposium of Tunneling, Chapman & Hall, London (1994), p.87–97.

DOI: 10.1007/978-1-4615-2646-9_6

Google Scholar

[10] I. Flood and N. Kartam, Neural networks in civil engineering: I. Principles and understanding. Journal of Computing in Civil Engineering, ASCE 8 2 (1994), p.131–148.

DOI: 10.1061/(asce)0887-3801(1994)8:2(131)

Google Scholar

[11] L. Fausett, Fundamentals of neural networks, Prentice-Hall (1994).

Google Scholar

[12] Yoh-Han Pao, Adaptive pattern recognition and neural networks, Addison-Wesley Publishing (1989).

Google Scholar

[13] Metin. Akey, Nonlinear biomedical signal processing, Fuzzy logic, neural networks, and new algorithms vol. 1, IEEE Press (2000).

Google Scholar

[14] In: L.C. Jain and V.R. Vemuri, Editors, Industrial applications of neural networks, CRC Press (1999).

Google Scholar

[15] Kang H. Fatigue analysis of spot welds subjected to combined tension and shear loading. Ph.D. Thesis, The University of Alabama; (1999).

Google Scholar

[16] Swellam MH. A fatigue design parameter for spot welds. Ph.D. Thesis, The University of Illinois at Urbana, Champaign; (1991).

Google Scholar

[17] B. Werbos and M. Hoff, Adaptive Switching and Circuits, Institute of Radio Engineers WESCON Convention Record, part 4 (1960), p.96–104.

Google Scholar

[18] Parker DB. Learning logic. technical report TR-87, Center for Computational Research in Economics and Management Science, MIT, Cambridge, MA; (1985).

Google Scholar

[19] D. Rumelhart and J. McClelland, Parallel distributed processing: explorations in the microstructure of cognition vol. 1, MIT Press, Cambridge (MA) (1986).

DOI: 10.7551/mitpress/5236.001.0001

Google Scholar

[20] Rumellhart and McClelland, 1986 D.L. Rumellhart and J. McClelland, Parallel Distributed Proceeding vol. 1, MIT Press, Cambridge, MA (1986).

Google Scholar

[21] In: L.C. Jain and V.R. Vemuri, Editors, Industrial applications of neural networks, CRC Press (1999).

Google Scholar