Semi-empirical models based on in-situ geotechnical tests have become the standard of practice for predicting soil liquefaction. Since the inception of the “simplified” cyclic-stress model in 1971, variants based on various in-situ tests have been developed, including the Cone Penetration Test (CPT). More recently, prediction models based soley on remotely-sensed data were developed. Similar to systems that provide automated content on earthquake impacts, these “geospatial” models aim to predict liquefaction for rapid response and loss estimation using readily-available data. This data includes (i) common ground-motion intensity measures (e.g., PGA), which can either be provided in near-real-time following an earthquake, or predicted for a future event; and (ii) geospatial parameters derived from digital elevation models, which are used to infer characteristics of the subsurface relevent to liquefaction. However, the predictive capabilities of geospatial and geotechnical models have not been directly compared, which could elucidate techniques for improving the geospatial models, and which would provide a baseline for measuring improvements. Accordingly, this study assesses the realtive efficacy of liquefaction models based on geospatial vs. CPT data using 9,908 case-studies from the 2010-2016 Canterbury earthquakes. While the top-performing models are CPT-based, the geospatial models perform relatively well given their simplicity and low cost. Although further research is needed (e.g., to improve upon the performance of current models), the findings of this study suggest that geospatial models have the potential to provide valuable first-order predictions of liquefaction occurence and consequence. Towards this end, performance assessments of geospatial vs. geotechnical models are ongoing for more than 20 additional global earthquakes.
The Screw Driving Sounding (SDS) method developed in Japan is a relatively new insitu testing technique to characterise soft shallow sites, typically those required for residential house construction. An SDS machine drills a rod into the ground in several loading steps while the rod is continuously rotated. Several parameters, such as torque, load and speed of penetration, are recorded at every rotation of the rod. The SDS method has been introduced in New Zealand, and the results of its application for characterising local sites are discussed in this study. A total of 164 SDS tests were conducted in Christchurch, Wellington and Auckland to validate/adjust the methodologies originally developed based on the Japanese practice. Most of the tests were conducted at sites where cone penetration tests (CPT), standard penetration tests (SPT) and borehole logs were available; the comparison of SDS results with existing information showed that the SDS method has great potential as an in-situ testing method for classifying the soils. By compiling the SDS data from 3 different cities and comparing them with the borehole logs, a soil classification chart was generated for identifying the soil type based on SDS parameters. Also, a correlation between fines content and SDS parameters was developed and a procedure for estimating angle of internal friction of sand using SDS parameters was investigated. Furthermore, a correlation was made between the tip resistance of the CPT and the SDS data for different percentages of fines content. The relationship between the SPT N value and a SDS parameter was also proposed. This thesis also presents a methodology for identifying the liquefiable layers of soil using SDS data. SDS tests were performed in both liquefied and non-liquefied areas in Christchurch to find a representative parameter and relationship for predicting the liquefaction potential of soil. Plots were drawn of the cyclic shear stress ratios (CSR) induced by the earthquakes and the corresponding energy of penetration during SDS tests. By identifying liquefied or unliquefied layers using three different popular CPT-based methods, boundary lines corresponding to the various probabilities of liquefaction happening were developed for different ranges of fines contents using logistic regression analysis, these could then be used for estimating the liquefaction potential of soil directly from the SDS data. Finally, the drilling process involved in screw driving sounding was simulated using Abaqus software. Analysis results proved that the model successfully captured the drilling process of the SDS machine in sand. In addition, a chart to predict peak friction angles of sandy sites based on measured SDS parameters for various vertical effective stresses was formulated. As a simple, fast and economical test, the SDS method can be a reliable alternative insitu test for soil and site characterisation, especially for residential house construction
Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.
Damage distribution maps from strong earthquakes and recorded data from field experiments have repeatedly shown that the ground surface topography and subsurface stratigraphy play a decisive role in shaping the ground motion characteristics at a site. Published theoretical studies qualitatively agree with observations from past seismic events and experiments; quantitatively, however, they systematically underestimate the absolute level of topographic amplification up to an order of magnitude or more in some cases. We have hypothesized in previous work that this discrepancy stems from idealizations of the geometry, material properties, and incident motion characteristics that most theoretical studies make. In this study, we perform numerical simulations of seismic wave propagation in heterogeneous media with arbitrary ground surface geometry, and compare results with high quality field recordings from a site with strong surface topography. Our goal is to explore whether high-fidelity simulations and realistic numerical models can – contrary to theoretical models – capture quantitatively the frequency and amplitude characteristics of topographic effects. For validation, we use field data from a linear array of nine portable seismometers that we deployed on Mount Pleasant and Heathcote Valley, Christchurch, New Zealand, and we compute empirical standard spectral ratios (SSR) and single-station horizontal-to-vertical spectral ratios (HVSR). The instruments recorded ambient vibrations and remote earthquakes for a period of two months (March-April 2017). We next perform two-dimensional wave propagation simulations using the explicit finite difference code FLAC. We construct our numerical model using a high-resolution (8m) Digital Elevation Map (DEM) available for the site, an estimated subsurface stratigraphy consistent with the geomorphology of the site, and soil properties estimated from in-situ and non-destructive tests. We subject the model to in-plane and out-of-plane incident motions that span a broadband frequency range (0.1-20Hz). Numerical and empirical spectral ratios from our blind prediction are found in very good quantitative agreement for stations on the slope of Mount Pleasant and on the surface of Heathcote Valley, across a wide range of frequencies that reveal the role of topography, soil amplification and basin edge focusing on the distribution of ground surface motion.
