Robust identification and characterization of thin soil layers in cone penetration data by piecewise layer optimization

Abstract

Cone penetration testing (CPT) is a preferred method for characterizing soil profiles for evaluating seismic liquefaction triggering potential. However, CPT has limitations in characterizing highly stratified profiles because the measured tip resistance (qc) of the cone penetrometer is influenced by the properties of the soils above and below the tip. This results in measured qc values that appear “blurred” at sediment layer boundaries, inhibiting our ability to characterize thinly layered strata that are potentially liquefiable. Removing this “blur” has been previously posed as a continuous optimization problem, but in some cases this methodology has been less efficacious than desired. Thus, we propose a new approach to determine the corrected qc values (i.e. values that would be measured in a stratum absent of thin-layer effects) from measured values. This new numerical optimization algorithm searches for soil profiles with a finite number of layers which can automatically be added or removed as needed. This algorithm is provided as open-source MATLAB software. It yields corrected qc values when applied to computer-simulated and calibration chamber CPT data. We compare two versions of the new algorithm that numerically optimize different functions, one of which uses a logarithm to refine fine-scale details, but which requires longer calculation times to yield improved corrected qc profiles.