Background and methodology The Mw 7.8, 14th November 2016 earthquake centred (item b, figure 1) in the Hurunui District of the South Island, New Zealand, damaged critical infrastructure across North Canterbury and Marlborough. We investigate the impacts to infrastructure and adaptations to the resulting service disruption in four small rural towns (figure 1): Culverden (a), Waiau (c), Ward (d) and Seddon (e). This is accomplished though literary research, interviews and geospatial analysis. Illustrating our methods, we have displayed here a Hurunui District hazard map (figure 2b) and select infrastructure inventories (figures 2a, 3).
This poster aims to present fragility functions for pipelines buried in liquefaction-prone soils. Existing fragility models used to quantify losses can be based on old data or use complex metrics. Addressing these issues, the proposed functions are based on the Christchurch network and soil and utilizes the Canterbury earthquake sequence (CES) data, partially represented in Figure 1. Figure 1 (a) presents the pipe failure dataset, which describes the date, location and pipe on which failures occurred. Figure 1 (b) shows the simulated ground motion intensity median of the 22nd February 2011 earthquake. To develop the model, the network and soil characteristics have also been utilized
Motivation This poster aims to present fragility functions for pipelines buried in liquefaction-prone soils. Existing fragility models used to quantify losses can be based on old data or use complex metrics. Addressing these issues, the proposed functions are based on the Christchurch network and soil and utilizes the Canterbury earthquake sequence (CES) data, partially represented in Figure 1. Figure 1 (a) presents the pipe failure dataset, which describes the date, location and pipe on which failures occurred. Figure 1 (b) shows the simulated ground motion intensity median of the 22nd February 2011 earthquake. To develop the model, the network and soil characteristics have also been utilized.
his poster presents the ongoing development of a 3D Canterbury seismic velocity model which will be used in physics-based hybrid broadband ground motion simulation of the 2010-2011 Canterbury earthquakes. Velocity models must sufficiently represent critical aspects of the crustal structure over multiple length scales which will influence the results of the simulations. As a result, numerous sources of data are utilized in order to provide adequate resolution where necessary. Figure 2: (a) Seismic reflection line showing P-wave velocities and significant geologic horizons (Barnes et al. 2011), and (b) Shear wave profiles at 10 locations (Stokoe et al. 2013). Figure 4: Cross sections of the current version of the Canterbury velocity model to depths of 10km as shown in Figure 1: (a) at a constant latitude value of -43.6˚, and (b) at a constant longitude value of 172.64˚. 3. Ground Surface and Geologic Horizon Models Figure 3: (a) Ground surface model derived from numerous available digital elevation models, and (b) Base of the Quaternary sediments derived from structural contours and seismic reflection line elevations. The Canterbury region has a unique and complex geology which likely has a significant impact on strong ground motions, in particular the deep and loose deposits of the Canterbury basin. The Canterbury basin has several implications on seismic wave phenomena such as long period ground motion amplification and wave guide effects. Using a realistic 3D seismic velocity model in physics-based ground motion simulation will implicitly account for such effects and the resultant simulated ground motions can be studied to gain a fundamental understanding of the salient ground motion phenomena which occurred during the Canterbury earthquakes, and the potential for repeat occurrences in the Canterbury region. Figure 1 shows the current model domain as a rectangular area between Lat=[-43.2˚,-44.0˚], and Lon=[171.5˚,173.0˚]. This essentially spans the area between the foot of the Southern Alps in the North West to Banks Peninsula in the East. Currently the model extends to a depth of 50km below sea level.
This poster presents work to date on ground motion simulation validation and inversion for the Canterbury, New Zealand region. Recent developments have focused on the collection of different earthquake sources and the verification of the SPECFEM3D software package in forward and inverse simulations. SPECFEM3D is an open source software package which simulates seismic wave propagation and performs adjoint tomography based upon the spectral-element method. Figure 2: Fence diagrams of shear wave velocities highlighting the salient features of the (a) 1D Canterbury velocity model, and (b) 3D Canterbury velocity model. Figure 5: Seismic sources and strong motion stations in the South Island of New Zealand, and corresponding ray paths of observed ground motions. Figure 3: Domain used for the 19th October 2010 Mw 4.8 case study event including the location of the seismic source and strong motion stations. By understanding the predictive and inversion capabilities of SPECFEM3D, the current 3D Canterbury Velocity Model can be iteratively improved to better predict the observed ground motions. This is achieved by minimizing the misfit between observed and simulated ground motions using the built-in optimization algorithm. Figure 1 shows the Canterbury Velocity Model domain considered including the locations of small-to-moderate Mw events [3-4.5], strong motion stations, and ray paths of observed ground motions. The area covered by the ray paths essentially indicates the area of the model which will be most affected by the waveform inversion. The seismic sources used in the ground motion simulations are centroid moment tensor solutions obtained from GeoNet. All earthquake ruptures are modelled as point sources with a Gaussian source time function. The minimum Mw limit is enforced to ensure good signal-to-noise ratio and well constrained source parameters. The maximum Mw limit is enforced to ensure the point source approximation is valid and to minimize off-fault nonlinear effects.
Introduction This poster presents the inferred initial performance and recovery of the water supply network of Christchurch following the 22 February 2011 Mw 6.2 earthquake. Results are presented in a geospatial and temporal fashion. This work strengthens the current understanding of the restoration of such a system after a disaster and quantifies the losses caused by this earthquake in respect with the Christchurch community. Figure 1 presents the topology of the water supply network as well as the spatial distribution of the buildings and their use.
INTRODUCTION This project falls under the Flagship 3: Wellington Coordinated Project. It supports other projects within FP3 to create a holistic understanding of risks posed by collapsed buildings due to future earthquake/s and the secondary consequences of cordoning in the short, mid and long term. Cordoning of the Christchurch CBD for more than two years and its subsequent implications on people and businesses had a significant impact on the recovery of Christchurch. Learning from this and experiences from the Kaikōura earthquake (where cordons were also established around selected buildings, Figure 3) have highlighted the need to understand the effects of cordons and plan for it before an earthquake occurs
Heathcote Valley school strong motion station (HVSC) consistently recorded ground motions with higher intensities than nearby stations during the 2010-2011 Canterbury earthquakes. For example, as shown in Figure 1, for the 22 February 2011 Christchurch earthquake, peak ground acceleration at HVSC reached 1.4 g (horizontal) and 2 g (vertical), the largest ever recorded in New Zealand. Strong amplification of ground motions is expected at Heathcote Valley due to: 1) the high impedance contrast at the soil-rock interface, and 2) the interference of incident and surface waves within the valley. However, both conventional empirical ground motion prediction equations (GMPE) and the physics-based large scale ground motions simulations (with empirical site response) are ineffective in predicting such amplification due to their respective inherent limitations.
Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.
essential systems upon which the well-being and functioning of societies depend. They deliver a service or a good to the population using a network, a combination of spatially-distributed links and nodes. As they are interconnected, network elements’ functionality is also interdependent. In case of a failure of one component, many others could be momentarily brought out-of-service. Further problems arise for buried infrastructure when it comes to buried infrastructure in earthquake and liquefaction-prone areas for the following reasons: • Technically more demanding inspections than those required for surface horizontal infrastructure • Infrastructure subject to both permanent ground displacement and transient ground deformation • Increase in network maintenance costs (i.e. deterioration due to ageing material and seismic hazard) These challenges suggest careful studies on network resilience will yield significant benefits. For these reasons, the potable water network of Christchurch city (Figure 1) has been selected for its well-characterized topology and its extensive repair dataset.
1. Background and Objectives This poster presents results from ground motion simulations of small-to-moderate magnitude (3.5≤Mw≤5.0) earthquake events in the Canterbury, New Zealand region using the Graves and Pitarka (2010,2015) methodology. Subsequent investigation of systematic ground motion effects highlights the prediction bias in the simulations which are also benchmarked against empirical ground motion models (e.g. Bradley (2013)). In this study, 144 earthquake ruptures, modelled as point sources, are considered with 1924 quality-assured ground motions recorded across 45 strong motion stations throughout the Canterbury region, as shown in Figure 1. The majority of sources are Mw≥4.0 and have centroid depth (CD) 10km or shallower. Earthquake source descriptions were obtained from the GeoNet New Zealand earthquake catalogue. The ground motion simulations were performed within a computational domain of 140km x 120km x 46km with a finite difference grid spacing of 0.1km. The low-frequency (LF) simulations utilize the 3D Canterbury Velocity Model while the high-frequency (HF) simulations utilize a generic regional 1D velocity model. In the LF simulations, a minimum shear wave velocity of 500m/s is enforced, yielding a maximum frequency of 1.0Hz.
In this paper we apply Full waveform tomography (FWT) based on the Adjoint-Wavefield (AW) method to iteratively invert a 3-D geophysical velocity model for the Canterbury region (Lee, 2017) from a simple initial model. The seismic wavefields was generated using numerical solution of the 3-D elastodynamic/ visco- elastodynamic equations (EMOD3D was adopted (Graves, 1996)), and through the AW method, gradients of model parameters (compression and shear wave velocity) were computed by implementing the cross-adjoint of forward and backward wavefields. The reversed-in-time displacement residual was utilized as the adjoint source. For inversion, we also account for the near source/ station effects, gradient precondition, smoothening (Gaussian filter in spatial domain) and optimal step length. Simulation-to-observation misfit measurements based on 191 sources at 78 seismic stations in the Canterbury region (Figure 1) were used into our inversion. The inversion process includes multiple frequency bands, starting from 0-0.05Hz, and advancing to higher frequency bands (0-0.1Hz and 0-0.2Hz). Each frequency band was used for up to 10 iterations or no optimal step length found. After 3 FWT inversion runs, the simulated seismograms computed using our final model show a good matching with the observed seismograms at frequencies from 0 - 0.2 Hz and the normalized least-squared misfit error has been significantly reduced. Over all, the synthetic study of FWT shows a good application to improve the crustal velocity models from the existed geological models and the seismic data of the different earthquake events happened in the Canterbury region.
This poster provides a comparison between the strong ground motions observed in the 22 February 2011 Mw6.3 Christchurch earthquake with those observed in Tokyo during the 11 March 2011 Mw9.0 Tohoku earthquake. The destuction resulting from both of these events has been well documented, although tsunami was the principal cause of damage in the latter event, and less attention has been devoted to the impact of earthquake-induced ground motions. Despite Tokyo being located over 100km from the nearest part of the causative rupture, the ground motions observed from the Tohoku earthquake were significant enough to cause structural damage and also significant liquefaction to loose reclaimed soils in Tokyo Bay. The author was fortunate enough (from the perspective of an earthquake engineer) to experience first-hand both of these events. Following the Tohoku event, the athor conducted various ground motion analyses and reconniassance of the Urayasu region in Tokyo Bay affected by liquefaction in collaboration with Prof. Kenji Ishihara. This conference is therefore a fitting opportunity in which to discuss some of authors insights obtained as a result of this first hand knowledge. Figure 1 illustrates the ground motions recorded in the Christchurch CBD in the 22 February 2011 and 4 September 2010 earthquakes, with that recorded in Tokyo Bay in the 11 March 2011 Tohoku earthquake. It is evident that these three ground motions vary widely in their amplitude and duration. The CBGS ground motion from the 22 February 2011 event has a very large amplitude (nearly 0.6g) and short duration (approx. 10s of intense shaking), as a result of the causal Mw6.3 rupture at short distance (Rrup=4km). The CBGS ground motion from the 4 September 2010 earthquake has a longer duration (approx. 30s of intense shaking), but reduced acceleration amplitude, as a result of the causal Mw7.1 rupture at a short-to-moderate distance (Rrup=14km). Finally, the Urayasu ground motion in Tokyo bay during the 11 March 2011 Tohoku earthquake exhibits an acceleration amplitude similar to the 4 September 2010 CBGS ground motion, but a significantly larger duration (approx 150s of intense shaking). Clearly, these three different ground motions will affect structures and soils in different ways depending on the vibration characteristics of the structures/soil, and the potential for strength and stiffness degradation due to cumulative effects. Figure 2 provides a comparison between the arias intensities of the several ground motion records from the three different events. It can be seen that the arias intensities of the ground motions in the Christchurch CBD from the 22 February 2011 earthquake (which is on average AI=2.5m/s) is approximately twice that from the 4 September 2010 earthquake (average AI≈1.25). This is consistent with a factor of approximately 1.6 obtained by Cubrinovski et al. (2011) using the stress-based (i.e.PGA-MSF) approach of liquefaction triggering. It can also be seen that the arias intensity of the ground motions recorded in Tokyo during the 2011 Tohoku earthquake are larger than ground motions in the Christchurch CBD from the 4 September 2011 earthquake, but smaller than those of the 22 February 2011 earthquake. Based on the arias intensity liquefaction triggering approach it can therefore be concluded that the ground motion severity, in terms of liquefaction potential, for the Tokyo ground motions is between those ground motions in Christchurch CBD from the 4 September 2010 and 22 February 2011 events.
In the period between September 2010 and December 2011, Christchurch (New Zealand) and its surroundings were hit by a series of strong earthquakes including six significant events, all generated by local faults in proximity to the city: 4 September 2010 (Mw=7.1), 22 February 2011 (Mw=6.2), 13 June 2011 (Mw=5.3 and Mw=6.0) and 23 December 2011 (M=5.8 and (M=5.9) earthquakes. As shown in Figure 1, the causative faults of the earthquakes were very close to or within the city boundaries thus generating very strong ground motions and causing tremendous damage throughout the city. Christchurch is shown as a lighter colour area, and its Central Business District (CBD) is marked with a white square area in the figure. Note that the sequence of earthquakes started to the west of the city and then propagated to the south, south-east and east of the city through a set of separate but apparently interacting faults. Because of their strength and proximity to the city, the earthquakes caused tremendous physical damage and impacts on the people, natural and built environments of Christchurch. The 22 February 2011 earthquake was particularly devastating. The ground motions generated by this earthquake were intense and in many parts of Christchurch substantially above the ground motions used to design the buildings in Christchurch. The earthquake caused 182 fatalities, collapse of two multi-storey reinforced concrete buildings, collapse or partial collapse of many unreinforced masonry structures including the historic Christchurch Cathedral. The Central Business District (CBD) of Christchurch, which is the central heart of the city just east of Hagley Park, was practically lost with majority of its 3,000 buildings being damaged beyond repair. Widespread liquefaction in the suburbs of Christchurch, as well as rock falls and slope/cliff instabilities in the Port Hills affected tens of thousands of residential buildings and properties, and shattered the lifelines and infrastructure over approximately one third of the city area. The total economic loss caused by the 2010-2011 Christchurch earthquakes is currently estimated to be in the range between 25 and 30 billion NZ dollars (or 15% to 18% of New Zealand’s GDP). After each major earthquake, comprehensive field investigations and inspections were conducted to document the liquefaction-induced land damage, lateral spreading displacements and their impacts on buildings and infrastructure. In addition, the ground motions produced by the earthquakes were recorded by approximately 15 strong motion stations within (close to) the city boundaries providing and impressive wealth of data, records and observations of the performance of ground and various types of structures during this unusual sequence of strong local earthquakes affecting a city. This paper discusses the liquefaction in residential areas and focuses on its impacts on dwellings (residential houses) and potable water system in the Christchurch suburbs. The ground conditions of Christchurch including the depositional history of soils, their composition, age and groundwater regime are first discussed. Detailed liquefaction maps illustrating the extent and severity of liquefaction across Christchurch triggered by the sequence of earthquakes including multiple episodes of severe re-liquefaction are next presented. Characteristic liquefaction-induced damage to residential houses is then described focussing on the performance of typical house foundations in areas affected by liquefaction. Liquefaction impacts on the potable water system of Christchurch is also briefly summarized including correlation between the damage to the system, liquefaction severity, and the performance of different pipe materials. Finally, the characteristics of Christchurch liquefaction and its impacts on built environment are discussed in relation to the liquefaction-induced damage in Japan during the 11 March 2011 Great East Japan Earthquake.
The increase of the world's population located near areas prone to natural disasters has given rise to new ‘mega risks’; the rebuild after disasters will test the governments’ capabilities to provide appropriate responses to protect the people and businesses. During the aftermath of the Christchurch earthquakes (2010-2012) that destroyed much of the inner city, the government of New Zealand set up a new partnership between the public and private sector to rebuild the city’s infrastructure. The new alliance, called SCIRT, used traditional risk management methods in the many construction projects. And, in hindsight, this was seen as one of the causes for some of the unanticipated problems. This study investigated the risk management practices in the post-disaster recovery to produce a specific risk management model that can be used effectively during future post-disaster situations. The aim was to develop a risk management guideline for more integrated risk management and fill the gap that arises when the traditional risk management framework is used in post-disaster situations. The study used the SCIRT alliance as a case study. The findings of the study are based on time and financial data from 100 rebuild projects, and from surveying and interviewing risk management professionals connected to the infrastructure recovery programme. The study focussed on post-disaster risk management in construction as a whole. It took into consideration the changes that happened to the people, the work and the environment due to the disaster. System thinking, and system dynamics techniques have been used due to the complexity of the recovery and to minimise the effect of unforeseen consequences. Based on an extensive literature review, the following methods were used to produce the model. The analytical hierarchical process and the relative importance index have been used to identify the critical risks inside the recovery project. System theory methods and quantitative graph theory have been used to investigate the dynamics of risks between the different management levels. Qualitative comparative analysis has been used to explore the critical success factors. And finally, causal loop diagrams combined with the grounded theory approach has been used to develop the model itself. The study identified that inexperienced staff, low management competency, poor communication, scope uncertainty, and non-alignment of the timing of strategic decisions with schedule demands, were the key risk factors in recovery projects. Among the critical risk groups, it was found that at a strategic management level, financial risks attracted the highest level of interest, as the client needs to secure funding. At both alliance-management and alliance-execution levels, the safety and environmental risks were given top priority due to a combination of high levels of emotional, reputational and media stresses. Risks arising from a lack of resources combined with the high volume of work and the concern that the cost could go out of control, alongside the aforementioned funding issues encouraged the client to create the recovery alliance model with large reputable construction organisations to lock in the recovery cost, at a time when the scope was still uncertain. This study found that building trust between all parties, clearer communication and a constant interactive flow of information, established a more working environment. Competent and clear allocation of risk management responsibilities, cultural shift, risk prioritisation, and staff training were crucial factors. Finally, the post-disaster risk management (PDRM) model can be described as an integrated risk management model that considers how the changes which happened to the environment, the people and their work, caused them to think differently to ease the complexity of the recovery projects. The model should be used as a guideline for recovery systems, especially after an earthquake, looking in detail at all the attributes and the concepts, which influence the risk management for more effective PDRM. The PDRM model is represented in Causal Loops Diagrams (CLD) in Figure 8.31 and based on 10 principles (Figure 8.32) and 26 concepts (Table 8.1) with its attributes.
On 22 February 2011，a magnitude Mw 6.3 earthquake occurred with an epicenter located near Lyttelton at about 10km from Christchurch in Canterbury region on the South Island of New Zealand (Figure 1). Since this earthquake occurred in the midst of the aftershock activity which had continued since the 4 September 2010 Darfield Earthquake occurrence, it was considered to be an aftershock of the initial earthquake. Because of the short distance to the city and the shallower depth of the epicenter, this earthquake caused more significant damage to pipelines, traffic facilities, residential houses/properties and multi-story buildings in the central business district than the September 2010 Darfield Earthquake in spite of its smaller earthquake magnitude. Unfortunately, this earthquake resulted in significant number of casualties due to the collapse of multi-story buildings and unreinforced masonry structures in the city center of Christchurch. As of 4 April, 172 casualties were reported and the final death toll is expected to be 181. While it is extremely regrettable that Christchurch suffered a terrible number of victims, civil and geotechnical engineers have this hard-to-find opportunity to learn the response of real ground from two gigantic earthquakes which occurred in less than six months from each other. From geotechnical engineering point of view, it is interesting to discuss the widespread liquefaction in natural sediments, repeated liquefaction within short period and further damage to earth structures which have been damaged in the previous earthquake. Following the earthquake, an intensive geotechnical reconnaissance was conducted to capture evidence and perishable data from this event. The team included the following members: Misko Cubrinovski (University of Canterbury, NZ, Team Leader), Susumu Yasuda (Tokyo Denki University, Japan, JGS Team Leader), Rolando Orense (University of Auckland, NZ), Kohji Tokimatsu (Tokyo Institute of Technology, Japan), Ryosuke Uzuoka (Tokushima University, Japan), Takashi Kiyota (University of Tokyo, Japan), Yasuyo Hosono (Toyohashi University of Technology, Japan) and Suguru Yamada (University of Tokyo, Japan).
On 4 September 2010 the Magnitude 7.1 'Darfield' Earthquake marked the beginning of the Canterbury earthquake sequence. The Darfield earthquake produced strong ground shaking throughout the centralCanterbury Plains, affecting rural areas, small towns and the city of Christchurch. The event produced a 29km long surface rupture through intensive farmland, causing localised flooding and liquefaction. The central Canterbury plains were subjected to a sustained period of thousands of aftershocks in the months after the Darfield earthquake. The primary sector is a major component of the in New Zealand economy. Business units are predominantly small family-run farm organisations, though there are increasing levels of corporate farming. The agribusiness sector contributes 20 per cent of real GDP and 47 per cent of total exports for New Zealand. Of the approximately 2,000 farms that are located in the Canterbury Plains, the most common farming sectors in the region are Mixed farming (mostly comprised of sheep and/or beef farming), Dairy farming, and Arable farming (cropping). Many farms on the Canterbury Plains require some form of irrigation and are increasingly capital intensive, reliant on built infrastructure, technology and critical services. Farms are of great significance to their local rural economies, with many rural non-farming organisations dependent on the health of local farming organisations. Despite the economic significance of the sector, there have been few, if any studies analysing how modern intensive farms are affected by earthquakes. The aim of this report is to (1) summarise the impacts the Darfield earthquake had on farming organisations and outline in general terms how farms are vulnerable to the effects of an earthquake; (2) identify what factors helped mitigate earthquake-related impacts. Data for this paper was collected through two surveys of farming and rural non-farming organisations following the earthquake and contextual interviews with affected organisations. In total, 78 organisations participated in the study (Figure 1). Farming organisations represented 72% (N=56) of the sample.
This report presents the simplified seismic assessment of a case study reinforced concrete (RC) building following the newly developed and refined NZSEE/MBIE guidelines on seismic assessment (NZSEE/MBIE, semi-final draft 26 October 2016). After an overview of the step-by-step ‘diagnostic’ process, including an holistic and qualitative description of the expected vulnerabilities and of the assessment strategy/methodology, focus is given, whilst not limited, to the implementation of a Detailed Seismic Assessment (DSA) (NZSEE/MBIE, 2016c). The DSA is intended to provide a more reliable and consistent outcome than what can be provided by an initial seismic assessment (ISA). In fact, while the Initial Seismic Assessment (ISA), of which the Initial Evaluation Procedure is only a part of, is the more natural and still recommended first step in the overall assessment process, it is mostly intended to be a coarse evaluation involving as few resources as reasonably possible. It is thus expected that an ISA will be followed by a Detailed Seismic Assessment (DSA) not only where the threshold of 33%NBS is not achieved but also where important decisions are intended that are reliant on the seismic status of the building. The use of %NBS (% New Building Standard) as a capacity/demand ratio to describe the result of the seismic assessment at all levels of assessment procedure (ISA through to DSA) is deliberate by the NZSEE/MBIE guidelines (Part A) (NZSEE/MBIE 2016a). The rating for the building needs only be based on the lowest level of assessment that is warranted for the particular circumstances. Discussion on how the %NBS rating is to be determined can be found in Section A3.3 (NZSEE/MBIE 2016a), and, more specifically, in Part B for the ISA (NZSEE/MBIE 2016b) and Part C for the DSA (NZSEE/MBIE 2016c). As per other international approaches, the DSA can be based on several analysis procedures to assess the structural behaviour (linear, nonlinear, static or dynamic, force or displacement-based). The significantly revamped NZSEE 2016 Seismic Assessment Guidelines strongly recommend the use of an analytical (basically ‘by hand’) method, referred to the Simple Lateral Mechanism Analysis (SLaMA) as a first phase of any other numerically-based analysis method. Significant effort has thus been dedicated to provide within the NZSEE 2016 guidelines (NZSEE/MBIE 2016c) a step-by-step description of the procedure, either in general terms (Chapter 2) or with specific reference to Reinforced Concrete Buildings (Chapter 5). More specifically, extract from the guidelines, NZSEE “recommend using the Simple Lateral Mechanism Analysis (SLaMA) procedure as a first step in any assessment. While SLaMA is essentially an analysis technique, it enables assessors to investigate (and present in a simple form) the potential contribution and interaction of a number of structural elements and their likely effect on the building’s global capacity. In some cases, the results of a SLaMA will only be indicative. However, it is expected that its use should help assessors achieve a more reliable outcome than if they only carried out a detailed analysis, especially if that analysis is limited to the elastic range For complex structural systems, a 3D dynamic analysis may be necessary to supplement the simplified nonlinear Simple Lateral Mechanism Analysis (SLaMA).” This report presents the development of a full design example for the the implementation of the SLaMA method on a case study buildings and a validation/comparison with a non-linear static (pushover) analysis. The step-by-step-procedure, summarized in Figure 1, will be herein demonstrated from a component level (beams, columns, wall elements) to a subassembly level (hierarchy of strength in a beam-column joint) and to a system level (frame, C-Wall) assuming initially a 2D behaviour of the key structural system, and then incorporating a by-hand 3D behaviour (torsional effects).
