DFI Journal - The Journal of the Deep Foundations Institute
Volume 8, Issue 1, May 2014
DOI: 10.1179/TBC14Z.0000000001
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Cross-Hole Sonic Logging and Frequency Tomography Analysis of Drilled Shaft Foundations to Better Evaluate Anomalies Locations
Article Type: Research Paper
Hajali, M. & Abishdid, C.
Abstract
Cross-hole sonic logging (CSL) has over recent years become the standard method for evaluating the integrity of bridge drilled shafts. The CSL method is based on measuring the speed of ultrasonic waves traveling between probes in parallel tubes placed inside the drilled shaft. Several existing studies have proposed methods that rely on the arrival time and wave speed to evaluate concrete integrity of drilled shaft foundations such as cross-hole tomography. In this study, a processing method for a three component wide band CSL data is presented. This method named frequency tomography analysis (FTA) is based on the change of the frequency amplitude of the signal recorded by the receiver probe at the location of anomalies. The signal’s time domain data are converted into frequency domain data using fast Fourier transform (FFT); the distribution of the FTA is then evaluated. This method is employed after a CSL test has determined a high probability of an anomaly in a given area and is applied to improve location accuracy and to further characterize the features of the anomaly. Two drilled shaft samples were built in Florida International University (FIU)’s Titan America Structures and Construction Testing (TASCT) Laboratory. Cubic foam pieces were placed inside the rebar cage before casting of concrete and throughout the length of the shaft. FTA was then utilized after the CSL tests to detect their location. The technique proved to have a very high resolution and was able to clarify the location of any artificial or planed discontinuities through the length of the drilled shaft.
Keywords:
drilled shaft, cross-hole sonic logging (CSL), frequency tomography analysis (FTA), time domain, frequency domain, fast fourier transform (FFT), anomalies