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Abstract This paper compares measured and predicted site response at the Garner Valley Downhole Array (GVDA) using a wide range of shear wave velocity (Vs) profiles developed from both borehole methods and inversion of surface wave data. Only low amplitude ground motions, resulting in approximately linear-viscoelastic site response between the downhole accelerometer and the surface accelerometers, were considered in this study. Thus, uncertainties associated with the small-strain Vs profiles used for site response predictions play a considerable role in attempting to match the recorded site response and its associated variability. Prior to our study, two borehole Vs profiles extending into rock were available for the site. However, their predicted/theoretical transfer functions (TTFs) were quite different and in poor agreement with the measured/empirical transfer functions (ETFs). These differences provided motivation to collect and interpret an extensive set of active-source and passive-wavefield surface wave measurements in an attempt to develop deep Vs profiles for the site that might be used to more accurately model the measured site response and its associated variability. Suites of non-unique Vs profiles developed from inversion of the surface wave data visually exhibited considerable differences, yet their predicted TTFs matched the measured ETFs very well. These results provide further evidence that surface wave dispersion data and horizontal-to-vertical spectral ratio curves represent an experimental “site signature” that can be used as a quantitative means of assessing whether candidate Vs profiles are appropriate for use in site response analyses. Highlights • Empirical transfer functions (ETF) computed at Garner Valley Dowhnole Array (GVDA) site. • Theoretical transfer functions (TTFs) calculated for various Vs profiles. • TTFs for Vs profiles from borehole testing and randomization poorly match ETF. • TTFs for Vs profiles from surface wave inversion are in good agreement with ETF. • Experimental dispersion and H/V data can be considered as a “site signature”, which can aid in randomization. [ABSTRACT FROM AUTHOR]

This paper discusses variability and accuracy of site response predictions performed using shear wave velocity (Vs) profiles derived from non-unique surface wave inversions and other commonly used statistical methods of accounting for epistemic uncertainty and aleatory variability in Vs. Specifically, linear and equivalent linear site response analyses were performed on the following three classes of Vs profiles: (1) 350 Vs profiles developed by performing multiple surface wave inversions, each with a unique set of layering parameters, on a common dispersion dataset, (2) two upper/lower range base-case Vs profiles developed by systematically increasing or decreasing the solution Vs profile by 20%, and (3) 100 Vs profiles developed using the Vs randomization procedure proposed by Toro (1995) [26]. Vs profiles derived from surface wave inversions generally yielded accurate site response estimates with minimal variability, so long as their theoretical dispersion data fit the experimental dispersion data well. On the other hand, the upper/lower range and randomized Vs profiles generally produced inaccurate and highly variable site response predictions, although the inclusion of site-specific parameters in the randomization model improved the results. At real sites where substantial aleatory variability is anticipated and/or the epistemic uncertainty is quite high, the site response estimates associated with the randomized and/or upper/lower range Vs profiles may be deemed acceptable. However, if the experimental dispersion data and horizontal-to-vertical spectral ratios are shown to be consistent over the footprint of a site, it may be possible to significantly reduce the uncertainty associated with the input Vs profile and the resulting uncertainty in the site response. [ABSTRACT FROM AUTHOR]

Nonlinear site response analyses are generally preferred over equivalent linear analyses for soft soil sites subjected to high-intensity input ground motions. However, both nonlinear (NL) and equivalent linear (EQL) analyses often result in large shear strain estimates (3–10%) at soft sites, and these large strains may generate unusual characteristics in the predicted surface ground motions, such as irregular time histories and atypical spectral shapes. One source of unusual ground motion predictions may be attributed to unrealistically low shear strengths implied by commonly used modulus reduction curves. Therefore, modulus reduction and damping curves can be modified at shear strains greater than approximately 0.1% to provide a more realistic soil model for site response. However, even after these modifications, nonlinear and equivalent linear site response analyses still may generate unusual surface acceleration time histories and Fourier amplitude spectra at soft soil sites when subjected to high-intensity input ground motions. In this study, we use equivalent linear and nonlinear 1D site response analyses for the well-known Treasure Island site to demonstrate challenges associated with accurately modeling large shear strains, and subsequent surface response, at soft soil sites. [ABSTRACT FROM AUTHOR]

Accounting for uncertainty in shear wave velocity (Vs) profiles has been recognized to be critical to seismic ground response analysis (GRA) and probabilistic seismic hazard analysis (PSHA). Vs randomization models are among the most widely used approaches to account for this uncertainty, often referred to as aleatory variability within the context of PSHA. Researchers have investigated alternate ways of more realistically accounting for Vs uncertainty, such as randomizing the cumulative shear wave travel time (tts) instead of Vs directly. While some preliminary assessments suggest that tts randomization allows for better management of Vs uncertainty, more validations are needed to guide its use in practice. This study aims to fill a knowledge gap by performing a thorough review of the tts randomization framework and suggesting changes that will lead to improved implementation. Specifically, through the use of real experimental data and Monte Carlo simulations, we identify several issues which, if left unimproved, could lead to bias and unrealistic results. These issues particularly pertain to: (1) methods used for bounding simulated layers, (2) approaches for merging column and bedrock models, and (3) appropriate values of the lognormal standard deviation on tts (σ lntts) and inter-layer correlation (ρ) for randomizing tts. For each issue, we devise recommendations and demonstrate that they provide a clear improvement on the current tts randomization framework. The revised steps put forward in this article do not require any additional model parameters, but rather, simply improve upon the existing methodology. The findings of this study enhance our understanding of the tts randomization framework, and the recommendations we provide will enable researchers and engineers to adopt it more robustly and confidently in engineering practice. ● A procedure for incorporating Vs uncertainty in seismic ground response is revisited and improved ● The procedure is based on randomizing the cumulative shear wave travel time (tts), rather than absolute Vs ● Real experimental data and Monte Carlo simulations are used to identify several issues with the current procedure which, if left unimproved, could lead to bias and unrealistic results ● For each issue, we devise recommendations and demonstrate that they provide a clear improvement on the current tts randomization framework ● The recommendations we provide will enable researchers and engineers to adopt tts randomization more robustly and confidently in engineering practice [ABSTRACT FROM AUTHOR]

Non-invasive surface wave methods have become a popular alternative to traditional invasive forms of site-characterization for inferring a site's subsurface shear wave velocity (Vs) structure. The advantage of surface wave methods over traditional forms of site characterization is that measurements made solely at the ground surface can be used routinely and economically to infer the subsurface structure of a site to depths of engineering interest (20–50 m), and much greater depths (>1 km) in some special cases. However, the quantification and propagation of uncertainties from surface wave measurements into the Vs profiles used in subsequent engineering analyses remains challenging. While this has been the focus of much work in recent years, and while considerable progress has been made, no approach for doing so has been widely accepted, leading analysts to either address the propagation of uncertainties in their own specialized manner or, worse, to ignore these uncertainties entirely. In response, this paper presents a new, effective, and verifiable method for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data. We begin by examining four approaches presented in the literature for developing suites of Vs profiles meant to account for uncertainty present in the measured dispersion data. These methods are shown to be deficient in three specific ways. First, all approaches are shown to be highly sensitive to their many user-defined inversion input parameters, making it difficult/impossible for them to be performed repeatedly by different analysts. Second, the suites of inverted Vs profiles, when viewed in terms of their implied theoretical dispersion curves, are shown to significantly underestimate the uncertainty present in the experimental dispersion data, though some may appear satisfactory when viewed purely qualitatively. Third, if the uncertainties in the implied theoretical dispersion curves were to be examined quantitatively, which has not been done previously, there is no obvious remedy available for the analyst to resolve any inconsistency between the measured and inverted dispersion uncertainty. Therefore, a new approach is proposed that seeks to remedy these shortcomings. First, beyond appropriate considerations that must be given to all inversions, the new approach is governed by only one user-defined input parameter, to which it is not overly sensitive. Second, the new approach is shown to produce suites of Vs profiles whose theoretical dispersion curves quantitatively reproduce the uncertainties in the experimental dispersion data. Third, the final step of the procedure requires the analyst to compare the measured and inverted dispersion uncertainties quantitatively, and should the analyst find the results of the new approach to be lacking, clear guidance is provided on the additional actions necessary to produce Vs profiles whose theoretical dispersion curves better account for the experimental uncertainty. Using two synthetic tests and a real-world example, the procedure is shown to produce suites of Vs profiles that accurately capture the site's Vs structure, while rigorously propagating the dispersion data's uncertainty through the inversion process. • A procedure is developed to quantify Vs uncertainty from surface wave inversion. • The procedure results in dispersion uncertainty-consistent Vs profiles. • The procedure is new, effective, and verifiable. • The procedure propagates experimental dispersion uncertainties into the inverted Vs profiles. • The procedure is demonstrated using synthetic and real-world examples. [ABSTRACT FROM AUTHOR]

The horizontal-to-vertical spectral ratio (HVSR) has seen widespread use as a tool for measuring a site's resonant behavior. However, the processing of HVSR with traditional methods can be tedious and time-consuming when the HVSR is complex and exhibits multiple resonances. This work proposes the AutoHVSR algorithm that allows for fully-automated processing of HVSR measurements, including those with zero, one, or multiple clear resonances. The AutoHVSR algorithm accepts microtremor or earthquake recordings and user-defined HVSR processing parameters as input and returns the computed HVSR from each time window or earthquake record, the mean/median HVSR curve, and statistics on each automatically identified HVSR resonance in terms of frequency and amplitude. The AutoHVSR algorithm integrates robust signal processing and computational methods with state-of-the-art machine-learning models trained using a diverse dataset of 1108 HVSR measurements. The AutoHVSR algorithm demonstrates excellent performance by correctly determining the number of HVSR resonances for 1098 of the 1108 HVSR measurements (>99%) and predicting the mean resonant frequency of the correctly identified resonances with a root mean square error (RMSE) of 0.05 Hz. Furthermore, the AutoHVSR algorithm was able to produce these predictions in 13 min (including HVSR processing time) compared to the 30 h required for traditional processing (a speed up of 138). The AutoHVSR algorithm is further demonstrated on a challenging dataset from Canterbury, New Zealand that included many HVSR curves with multiple and/or ambiguous resonances. Despite the challenging nature of the Canterbury dataset, the AutoHVSR algorithm was capable of correctly determining the number of HVSR resonances for 113 of the 129 HVSR measurements (>87%) and predicting the mean resonant frequency of the correctly identified resonances with a RMSE of 0.10 Hz. The AutoHVSR algorithm produced these predictions under 2 min (including HVSR processing time) compared to the 4 h required for traditional processing (a speed up of 120). Finally, while the AutoHVSR algorithm was developed using microtremor measurements where the horizontal components were combined using the geometric mean, it is shown to extend without modification to microtremor HVSR measurements where the two horizontal components are rotated azimuthally and to HVSR measurements from earthquake recordings. The AutoHVSR algorithm has been made publicly available in v0.3.0 of HVSRweb, it can be accessed at https://hvsrweb.designsafe-ci.org/. • The AutoHVSR algorithm is proposed for fully-automating HVSR processing. • AutoHVSR can process HVSR with zero, one, or multiple clear resonances. • The predictions of AutoHVSR are fast, reliable, and reproducible. • AutoHVSR can be applied broadly including azimuthally-rotated and earthquake HVSR. • AutoHVSR has been made publicly accessible through HVSRweb. [ABSTRACT FROM AUTHOR]

The objective of the study presented herein is to assess three commonly used CPT-based liquefaction evaluation procedures and three liquefaction severity index frameworks using data from the 2010–2011 Canterbury earthquake sequence. Specifically, post-event field observations, ground motion recordings, and results from a recently completed extensive geotechnical site investigation programme at selected strong motion stations (SMSs) in the city of Christchurch and surrounding towns are used herein. Unlike similar studies that used data from free-field sites, accelerogram characteristics at the SMS locations can be used to assess the performance of liquefaction evaluation procedures prior to their use in the computation of surficial manifestation severity indices. Results from this study indicate that for cases with evidence of liquefaction triggering in the accelerograms, the majority of liquefaction evaluation procedures yielded correct predictions, regardless of whether surficial manifestation of liquefaction was evident or not. For cases with no evidence of liquefaction in the accelerograms (and no observed surficial evidence of liquefaction triggering), the majority of liquefaction evaluation procedures predicted liquefaction was triggered. When all cases are used to assess the performance of liquefaction severity index frameworks, a poor correlation is shown between the observed severity of liquefaction surface manifestation and the calculated severity indices. However, only using those cases where the liquefaction evaluation procedures yielded correct predictions, there is an improvement in the correlation, with the Liquefaction Severity Number (LSN) being the best performing of the frameworks investigated herein. However scatter in the relationship between the observed and calculated surficial manifestation still remains for all liquefaction severity index frameworks. [ABSTRACT FROM AUTHOR]

A dynamic full scale testing program was performed to quantitatively assess the effectiveness of prefabricated vertical drains as a liquefaction countermeasure. The testing program involved a new in situ liquefaction testing technique, which uses a large hydraulic vibrator to generate waves propagating through an embedded instrumentation area to measure the coupled soil-pore water response. The effectiveness of prefabricated vertical drains is assessed experimentally by comparing the pore pressure generation, pore pressure dissipation, and settlement from two reconstituted soil specimens; one without a drain in place and the other with a single drain installed. Because the prefabricated drain was installed during the specimen preparation process, no accompanying densification during installation occurred. Therefore, the effect of drainage alone was evaluated. The testing results show that the drainage provided by prefabricated drains can significantly reduce pore pressure generation, accelerate post-shaking pore pressure dissipation, and limit associated settlement. The outcome also shows that the new developed in situ liquefaction testing technique can be an alternative to quantitatively evaluate the effects of various liquefaction remediation techniques. [Copyright &y& Elsevier]

This paper presents the development of depth- and V s 30 -dependent parametric velocity and spatial correlation models to characterise shear-wave velocities within the geologic layers of the Canterbury New Zealand sedimentary basin. The models utilise data from 22 shear-wave velocity profiles of up to 2.5 k m depth, derived from surface wave analysis, juxtaposed with models which detail the three-dimensional structure of the geologic formations in the Canterbury sedimentary basin. Parametric velocity equations are presented for Fine Grained Sediments, Gravels, and Tertiary layer groupings. Spatial correlations were developed and applied to generate three-dimensional stochastic velocity perturbations. Dispersion curves for the stochastic models and observed velocity profiles show good agreement over a wide frequency range with the dispersion data underlying the velocity profiles used as input data, indicating that the parametric perturbed velocity profiles replicate the dispersion characteristics of the observed velocity profiles. Collectively, these models enable seismic velocities to be realistically represented for applications such as 3D ground motion and site response simulations. • Parametric functions to prescribe velocities in Canterbury sedimentary deposits. • Geostatistics are used to develop spatial correlations to add velocity perturbations to parametric models. • Velocity modelling for use in ground motion and site response simulations. [ABSTRACT FROM AUTHOR]