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.
The Canterbury region experienced widespread damage due to liquefaction induced by seismic shaking during the 4 September 2010 earthquake and the large aftershocks that followed, notably those that occurred on 22 February, 13 June and 23 December 2011. Following the 2010 earthquake, the Earthquake Commission directed a thorough investigation of the ground profile in Christchurch, and to date, more than 7500 cone penetration tests (CPT) have been performed in the region. This paper presents the results of analyses which use a subset of the geotechnical database to evaluate the liquefaction process as well as the re-liquefaction that occurred following some of the major events in Christchurch. First, the applicability of existing CPT-based methods for evaluating liquefaction potential of Christchurch soils was investigated using three methods currently available. Next, the results of liquefaction potential evaluation were compared with the severity of observed damage, categorised in terms of the land damage grade developed from Tonkin & Taylor property inspections as well as from observed severity of liquefaction from aerial photography. For this purpose, the Liquefaction Potential Index (LPI) was used to represent the damage potential at each site. In addition, a comparison of the CPT-based strength profiles obtained before each of the major aftershocks was performed. The results suggest that the analysis of spatial and temporal variations of strength profiles gives a clear indication of the resulting liquefaction and re-liquefaction observed in Christchurch. The comparison of a limited number of CPT strength profiles before and after the earthquakes seems to indicate that no noticeable strengthening has occurred in Christchurch, making the area vulnerable to liquefaction induced land damage in future large-scale earthquakes.
A number of field testing techniques, such as standard penetration test (SPT), cone penetration test (CPT), and Swedish weight sounding (SWS), are popularly used for in-situ characterisation. The screw driving sounding (SDS) method, which has been recently developed in Japan, is an improved version of the SWS technique and measures more parameters, including the required torque, load, speed of penetration and rod friction; these provide more robust way of characterising soil stratigraphy. It is a cost-efficient technique which uses a machine-driven and portable device, making it ideal for testing in small-scale and confined areas. Moreover, with a testing depth of up to 10-15m, it is suitable for liquefaction assessment. Thus, the SDS method has great potential as an in-situ testing method for geotechnical site characterisation, especially for residential house construction. In this paper, the results of SDS tests performed at a variety of sites in New Zealand are presented. The soil database was employed to develop a soil classification chart based on SDS-derived parameters. Moreover, using the data obtained following the 2010-2011 Christchurch Earthquake Se-quence, a methodology was established for liquefaction potential evaluation using SDS data. http://www.isc5.com.au/wp-content/uploads/2016/09/1345-2-ORENSE.pdf
During the recent devastating earthquakes in Christchurch, many residential houses were damaged due to widespread liquefaction of the ground. In-situ testing is widely used as a convenient method for evaluating liquefaction potential of soils. Cone penetration test (CPT) and standard penetration test (SPT) are the two popular in situ tests which are widely used in New Zealand for site characterization. The Screw Driving Sounding (SDS) method is a relatively new operating system developed in Japan consisting of a machine that drills a rod into the ground by applying torque at seven steps of axial loading. This machine can continuously measure the required torque, load, speed of penetration and rod friction during the test, and therefore can give a clear overview of the soil profile along the depth of penetration. In this paper, based on a number of SDS tests conducted in Christchurch, a correlation was developed between tip resistance of CPT test and SDS parameters for layers consisting of different fines contents. Moreover, using the obtained correlation, a chart was proposed which relates the cyclic resistance ratio to the appropriate SDS parameter. Using the proposed chart, liquefaction potential of soil can be estimated directly using SDS data. As SDS method is simpler, faster and more economical test than CPT and SPT, it can be a reliable alternative in-situ test for soil characterization, especially in residential house constructions.
This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.
