A probabilistic Bayesian methodology for the strain-rate correction of dynamic CPTu data

Dynamic Cone Penetration Test (CPTu) profile offshore sediments by impact penetration. To exploit their results in full, the measured data are converted to obtain a quasi-static equivalent profile. CPTu conversion requires calibrated correction models. Calibration is currently done using paired (i.e., very close) and dynamic tests. It shown here that test data, which may be inconvenient acquire offshore, not strictly necessary convert data. A new probabilistic methodology proposed call upon from much wider area procedure. Those feed prior distribution of profile, within Bayesian updating scheme where strain-rate coefficient model error also described updated stochastic variables. The solved numerically Transitional Markov Chain Monte Carlo sampling algorithm. avoid undue influence local heterogeneity, statistic treatment takes place frequency domain, discrete cosine transform. procedure applied campaign Nice (France): tests with equal precision acquired at distances orders magnitude larger than what was previously employed.