Estimating static tip resistance of driven piles with bottom pile instrumentation

Author:

Tran Khiem T.1,McVay Michael1,Herrera Rodrigo2,Lai Peter2

Affiliation:

1. Department of Civil and Coastal Engineering, University of Florida, 365 Weil Hall, P.O. Box 116580, Gainesville, FL 32611, USA.

2. Florida Department of Transportation, 605 Suwannee Street, Tallahassee, FL, USA.

Abstract

A technique is presented to estimate static tip resistance of a pile during driving from embedded strain and accelerometer data located one diameter (D) from the bottom of the pile. The approach uses a nonlinear single degree of freedom system to satisfy force and energy equilibrium with a global genetic inversion approach. By balancing force and energy from inertia, damping, and stiffness against the measured tip data, the unknown parameters (mass, damping, and nonlinear stiffness) are estimated. Requiring a few seconds for analysis for each blow, the algorithm ensures a real-time assessment of static tip resistance as a function of displacement, which is important when setting pile lengths. The proposed approach was applied to four test piles at two bridge sites (Florida and Louisiana). Mobilized static tip resistances ranging from 400 to 1500 kN as a function of displacement were predicted. The predicted static resistance versus displacements compared favorably with measured values from static load tests. Interestingly, the maximum recorded increase in tip resistance in silty to clayey sands was less than 20% when piles were re-struck at times ranging from 2 to 30 days after initial drive.

Publisher

Canadian Science Publishing

Subject

Civil and Structural Engineering,Geotechnical Engineering and Engineering Geology

Reference18 articles.

1. Axelsson, G. 2000. Long-term set-up of driven piles in sands. Ph.D. dissertation, Royal Institute of Technology, Sweden.

2. Evaluation of Static Capacity of Deep Foundations from Statnamic Testing

3. Side Shear Setup. II: Results From Florida Test Piles

4. Genetic algorithms: A powerful tool for large-scale nonlinear optimization problems

5. Goldberg, D. 1989. Genetic algorithms in search, optimization and machine learning. Addison Wesley Publishing Company.

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