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Non-linear dynamic analyses (NDAs) can capture the complex dynamic behavior of the soil using properly calibrated constitutive models. However, the quality of results from an NDA tudy hinges on several factors. Validation, which involves comparing numerical results to physical measurements, can assess the ability of an NDA to capture key responses through selected metrics. This study presents the application of a time history-based validation metric for evaluating the performance of numerical simulations. The centrifuge experiment conducted at UC Davis under the LEAP-2017 project, along with simulations performed using the PM4Sand constitutive model, provides the experimental and numerical data, respectively. The validation of the simulations against experimental measurements using the proposed metric is followed by a discussion on the potential experimental and numerical sources causing the quantified discrepancies. Conclusions are drawn on the effectiveness of the investigated metrics in facilitating the performance evaluation of numerical simulations and enhancing their reliability. 

Non-linear dynamic analyses (NDAs) are widely used in engineering practice to evaluate the seismic performance of geotechnical structures affected by liquefaction or cyclic softening. The quality of results from an NDA study depends on several technical and nontechnical factors. Validation, wherein a numerical prediction is compared to its physical counterpart, can assess the ability of an NDA to capture the various metrics of the response and potentially provide guidance toward improving the prediction. This study investigates select methodologies and validation metrics commonly used in signal processing problems to assess their effectiveness in capturing discrepancies between experimental and simulation results for a specific response of interest. Three simple problems are initially evaluated to analyze the metrics’ capabilities and identify necessary modifications. Then, the metrics are applied to nine sets of experimental and simulation time series, focusing on one response of interest (pore water pressure). It is found that cross-correlation successfully captures the lag in the initiation of liquefaction triggering, while Russell’s error metric captures magnitude and phase discrepancies.