1- Introduction

2- Case-study application

3- Results and discussion

4- Conclusions

References

Abstract

Many coastal regions lying on subduction zones are likely to experience the catastrophic effects of cascading earthquake and tsunami observed in recent events, e.g., 2011 Tohoku Earthquake and Tsunami. The influence of earthquake on the response of the structure to tsunami is difficult to quantify through damage observations from past events, since they only provide information on the combined effects of both perils. Hence, the use of analytical methodologies is fundamental. This paper investigates the response of a reinforced concrete frame subjected to realistic ground motion and tsunami inundation time histories that have been simulated considering a seismic source representative of the M9 2011 Tohoku earthquake event. The structure is analysed via nonlinear time-history analyses under (a) tsunami inundation only and (b) earthquake ground motion and tsunami inundation in sequence. Comparison of these analyses shows that there is a small impact of the preceding earthquake ground shaking on the tsunami fragility. The fragility curves constructed for the cascading hazards show less than 15% reduction in the median estimate of tsunami capacity compared to the fragility functions for tsunami only. This outcome reflects the fundamentally different response of the structure to the two perils: while the ground motion response of the structure is governed by its strength, ductility and stiffness, the tsunami performance of the structure is dominated by its strength. It is found that the ground shaking influences the tsunami displacement response of the considered structure due to the stiffness degradation induced in the ground motion cyclic response, but this effect decreases with increasing tsunami force.

Introduction

Tsunami have contributed to 250,125 deaths between 1994 and 2013 [1]. They are the deadliest natural hazard, with an average of 79 deaths for every 1000 people affected, compared to four deaths per 1000 for other natural hazards. Past tsunami have caused widespread damage and economic losses, with a direct loss of US$211 billion being estimated for the 2011 Tohoku event alone [2]. Exposure to this hazard is high, as 6 out of the 10 most populous megacities are at risk of being severely affected by storm surge and tsunami [3]. Moreover, regions at highest risk lie on subduction zones around the Pacific “Ring of Fire” (e.g., Japan, Indonesia, Pacific Northwest), and hence are likely to experience strong ground shaking as well as tsunami inundation [4]. An important component in the evaluation of tsunami impact or risk is the estimation of building response due to tsunami onshore flow. To date the majority of research on this topic has focussed on the development of fragility functions based on post-tsunami damage observed at a given location, so-called “empirical fragility functions”, e.g. Suppasri et al. [5] among many others. Empirical tsunami fragility functions are by their nature specific to the event represented in the post-event damage data as well as the local building stock, and are limited by the typical absence of locally recorded tsunami intensity measures, such as the flow velocity. They commonly adopt building damage observations from locations that have been affected by both earthquake and tsunami hazards, implicitly including the response of buildings to the combined hazards. Assessment of structural performance through numerical analyses is therefore essential to overcome these limitations. Analytical fragility functions are therefore needed to complement empirical assessments for a physical understanding of structural behaviour under cascading earthquake and tsunami.

At 12:03 am local time, on November 14th, 2016, a moment magnitude (Mw) 7.8 (USGS) earthquake struck the Kaikoura ¯ region of South Island, New Zealand. The earthquake occurred at a complicated portion of the Australian–Pacific plate boundary, where the motion between the two plates decreases southwestward, and the azimuth changes from trench-normal to trench-parallel along the 3000 km southward-developing subduction system (Litchfield et al., 2014) (Fig. 1 inset). The epicenter of the earthquake (Fig. 1a and inset) is located at the southern end of the subducting system (Barker et al., 2009; Eberhart-Phillips and Bannister, 2010; Williams et al., 2013). The 2016 Kaikoura ¯ earthquake was not unexpected, given its plate boundary location and the historical seismicity, yet it exhibited exceptional complexity. This complexity was initially reflected by the significant non-double-couple centroid moment tensor (CMT) solutions from the USGS (USGS, 2016) and GCMT (Ekstrom et al., 2012) (Fig. 1). Subsequently, field and aerial observations and satellite radar images acquired immediately after the earthquake showed that the quake produced one of the most complex surface ruptures ever recorded (Clark et al., 2017; Litchfield et al., 2017; Stirling et al., 2017). Although the epicenter is ∼50 km inland from the coast, the accompanying tsunami caused up to 5 m of run-ups at Goose Bay, on the Bank Peninsula, to the south of the epicenter and was well recorded by  many tide gauges located along the east coast of New Zealand (Lane et al., 2017; Power et al., 2017) (Fig. S1). The seismic and tsunami hazard due to such a complex rupture cannot be faithfully characterized based on a more typical single-fault rupture scenario (Hamling et al., 2017; Shi et al., 2017). Dense geodetic measurements and strong motion seismometers onshore provided valuable near-field observations to study slip partitioning in the up-dip portion of the subduction system, as the coastline is particularly close to the trench (<50 km). Preliminary seismological analysis shows that the mechanism of the mainshock was oblique thrust (Kaiser et al., 2017). Hamling et al. (2017) derived a static slip model with more than 10 fault segments using GPS and InSAR data. The surface displacement can be fitted reasonably well from their preferred model with most of the slip occurred on crustal faults, yet they have difficulties in simulating the observed tsunami waveforms. This is partially because they did not use a fault segment for the Papatea fault as they considered the associated deformation was inelastic and they could not fit it. Hollingsworth et al. (2017) used optical satellite imagery and teleseismic data to analyze the earthquake rupture process. Their rupture model, which was derived with only seismic data assuming a two-segment fault geometry, shows a source duration of ∼100 s with unilateral rupture to the northeast and most of the slip occurring on the subduction interface. Their model is similar to the result of multiple-point source models derived from the long-period teleseismic data (Duputel and Rivera, 2017), and the result from the back-projection analysis (Zhang et al., 2017).

Seismological, geodetic, and tsunami observations have shown that subduction zones are complex systems where the properties of earthquake rupture vary with depth (Lay et al., 2012). For example, earthquake duration normalised for event size has been observed to decrease with depth; this recurrent feature has been attributed to depth varying shear modulus and/or stress drop for individual earthquakes (Bilek and Lay, 1999; Bilek et al., 2016; Geist and Bilek, 2001). Depth variation in subduction ruptures is, for example, evident when comparing the different historical earthquakes that occurred off the Pacific coast of Tohoku region in Japan * Corresponding author. E-mail address: shane.murphy@ifremer.fr (S. Murphy). (Fig. 1). A number of major (Mw 7–7.9) thrust earthquakes mostly slipped within a depth range of 10–40 km. These events involved individual patches of concentrated slip implying the breaking of at least one prominent, high stress asperity (Shao et al., 2011; Yamanaka and Kikuchi, 2004). Conversely, the 1896 Meiji event (M 8.2–8.4), likely involved slip primarily at the base of the shallow accretionary wedge or beneath it. This earthquake produced a disproportionately large tsunami relative to its moment magnitude, possibly making it a potential ‘tsunami earthquake’ (Kanamori, 1972). The great Mw 9.0 2011 Tohoku earthquake nucleated at ∼20–25 km depth, and produced slip at traditionally expected depths while also realising a substantial amount of slip all the way to the trench (i.e., at less than 10 km depth) (Chu et al., 2011; Ide et al., 2011; Romano et al., 2014). Numerical models of the dynamic rupture process have successfully described either individual types of earthquakes, for example the Tohoku event (Kozdon and Dunham, 2013; Noda and Lapusta, 2013), or both thrust and tsunami earthquakes in the same model (Mitsui and Yagi, 2013). Numerical models coupled with the rateand-state friction law have been used to reproduce full seismic cycles for subduction environments. However, this comes at the expense of either failing to account for geometry/free surface effects and inhomogeneity in the material surrounding the fault (Cubas et al., 2015; Noda and Lapusta, 2013), or by simplifying wave propagation to static stress changes on the fault plane (Shibazaki et al., 2011). Fully dynamic simulations including a free surface and variable geometry have tended to focus on specific rupture features of the Tohoku earthquake such as the slip in the trench or long period guided wave propagation in the ocean (Hirono et al., 2016; Huang et al., 2013; Kozdon and Dunham, 2014). Depth dependent changes in frictional parameters have been tested using rate-andstate models for the 2011 Tohoku (Kozdon and Dunham, 2013). However, to our knowledge, no numerical model has been able to reproduce a range of different observed earthquakes types (e.g. Fig. 1) while at the same time accounting for the fault geometry and complex structure as proposed here. The focus of this study is to provide a simple yet geologically consistent model that reconciles the different observed earthquake types with fault properties from independent theoretical and laboratory studies. We focus our investigation on the aspect of rupture dynamics due to depth-dependent frictional conditions focusing on a specific time window of the seismic cycle including the subseismic frictional properties of the fault materials (den Hartog et al., 2012). Hence, the friction law parameters were chosen based on available geological and geophysical constraints. On the other Fig. 2. Numerical model set up. a) Structural model used in the numerical simulations, black line denotes the subduction interface. b) Variation of frictional coeffi- cients with depth: the dark and light grey boxes denote the clay-rich and crystalline rock frictional coefficients; the white is the transition between the two materials.

The world in various places is now facing a multitude of crises that may affect human health, climate, and environment (Son and Thanh, 2018). A crisis is defined as the appearance of a state of disorder, abnormal, dysfunction, deep imbalance, serious disorder (Girard et al., 2006). Crisis management is “a series of functions or processes to identify, study and forecast crisis issues, and set forth specific ways that would enable an organization to prevent or cope with a crisis (Kash and Darling, 1998)”. Crisis management is a complex topic since it needs the best understanding of the involved phenomena with its consequences (including scenarios, events, and outcomes), the knowledge of the underlying processes and the considered root causes analysis (Jensen and Aven, 2018). Crisis can be caused by two types of major risks (Table1). The resolutions of all these crises have a common denominator: crisis management to return to a normal situation and also minimize the consequences of such a crisis in a safe and effective manner (Vardarlıare, 2016). A good crisis management requires a number of preconditions. It must be planned, prepared and executed in the appropriate way and this process can be divided into three main stages, as shown in Figure 1: (1) dissemination of the alert message, (2) evacuation and (3) protection of evacuated areas (crisis recovery) in order to save more lives and to reduce the severity of collateral and environmental damage (Zhou et al., 2017; Devlin, 2006) In emergency contexts, the remote medical activities delivered by telemedicine are interesting for the protection of human life in crisis management. The crisis managers and actors will thus be better equipped to face risk and sensitive situations and to reduce the impact of the complex medical problems caused by the natural or industrial risks. In addition, the development of telemedicine software and platforms have made significant advances in recent years, involving a substantial increase in the number of communications and network collaboration tools. So, there is a strong need for interoperable information systems to facilitate their effective communication which involves the sharing and exchange of data between these systems (Patient medical record, X-ray image, medical analysis data, etc.). The main objective of this study is to provide a new methodological framework for remote crisis management integrating information modeling on the collaboration of telemedicine acts. This methodology is based on a formal specification of information flows and how to exploit them to provide crisis managers with relevant ways for collaboration towards critical decisionmaking. The application of this methodology highlights two main interests: (1) the strengthening of remote collaboration based on existing of telemedicine software (Saidi et al., 2018) and (2) the improvement of medical attention in order to reduce the number of deaths due to lack of medical assistance (ASIP, 2012). Knowing that such medical attention can be done during the transport phase of the victims and also with a remote support from health structures to fill the lack of medical professionals or specialists.