Graduation Year
2005
Document Type
Thesis
Degree
M.S.C.E.
Degree Granting Department
Civil Engineering
Major Professor
A. Gray Mullins, Ph.D.
Committee Member
Rajan Sen, Ph. D.
Committee Member
Abla Zayed, Ph. D.
Keywords
Unloading point, Drilled shaft, Capacity, Side shear, Deep foundation
Abstract
In the field of civil engineering, particularly structural foundations, low-cost options and time saving construction methods are important because both can be a burden on the public. Drilled shafts have proven to both lower cost and shorten construction time for large-scale projects. However, their integrity as load-carrying foundations has been questioned. The statnamic load test was conceived in the 1980s as an alternative method of testing these larger, deeper foundation elements. Performing a load test verifies that the load carrying capacity of a foundation is agreeable with the estimated capacity during the design phase and that no significant anomalies occurred during construction. The statnamic test, however, is classified as a rapid load test and requires special data regression techniques.
The outcome of available regression techniques is directly related to the available instrumentation on the test shaft. Generally, the more instrumentation available, the more complete results the regression method will produce. This thesis will show that a proposed method requiring only basic instrumentation can produce more complete results using a predictive model for side shear development with displacement during the statnamic test. A driven pile or drilled shaft can be discretized into segments based on the load shed distribution model. Each segment can be analyzed as a rigid body. The total static capacity is then the summation of each segments’ contribution. Further, a weighted acceleration can be generated and used to perform an unloading point analysis.
Scholar Commons Citation
Lowry, Sonia L., "Analysis of Statnamic Load Test Data Using a Load Shed Distribution Model" (2005). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/750