American Society of Civil Engineers


Database Assessment of CPT-Based Design Methods for Axial Capacity of Driven Piles in Siliceous Sands


by James A. Schneider, Ph.D., (corresponding author), (Student, School of Civ. and Resource Engrg., The Univ. of Western Australia, Crawley, Perth WA 6009, Australia E-mail: schneider@civil.uwa.edu.au), Xiangtao Xu, Ph.D., (Student, School of Civ. and Resource Engrg., The Univ. of Western Australia, Crawley, Perth WA 6009, Australia), and Barry M. Lehane, (Prof., School of Civ. and Resource Engrg., The Univ. of Western Australia, Crawley, Perth WA 6009, Australia)

Journal of Geotechnical and Geoenvironmental Engineering, Vol. 134, No. 9, September 2008, pp. 1227-1244, (doi:  http://dx.doi.org/10.1061/(ASCE)1090-0241(2008)134:9(1227))

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Document type: Journal Paper
Abstract: Numerous cone penetration test (CPT)-based methods exist for calculation of the axial pile capacity in sands, but no clear guidance is presently available to assist designers in the selection of the most appropriate method. To assist in this regard, this paper examines the predictive performance of a range of pile design methods against a newly compiled database of static load tests on driven piles in siliceous sands with adjacent CPT profiles. Seven driven pile design methods are considered, including the conventional American Petroleum Institute (API) approach, simplified CPT alpha methods, and four new CPT-based methods, which are now presented in the commentary of the 22nd edition of the API recommendations. Mean and standard deviation database statistics for the design methods are presented for the entire 77 pile database, as well as for smaller subset databases separated by pile material (steel and concrete), end condition (open versus closed), and direction of loading (tension versus compression). Certain methods are seen to exhibit bias toward length, relative density, cone tip resistance, and pile end condition. Other methods do not exhibit any apparent bias (even though their formulations differ significantly) due to the limited size of the database subsets and the large number of factors known to influence pile capacity in sand. The database statistics for the best performing methods are substantially better than those for the API approach and the simplified alpha methods. Improved predictive reliability will emerge with an extension of the database and the inclusion of additional important controlling factors affecting capacity.


ASCE Subject Headings:
Compression
Cone penetration tests
Databases
Design
Driven piles
Foundations
Pile load tests
Sand (soil type)