Indonesia has experienced several tsunamis triggered by seismic and non-seismic (i.e., landslides) sources. These events damaged or destroyed coastal buildings and infrastructure and caused considerable loss of life. Based on the Global Earthquake Model (GEM) guidelines, this study assesses the empirical tsunami fragility to the buildings inventory of the 2018 Sunda Strait, 2018 Sulawesi–Palu, and 2004 Indian Ocean (Khao Lak–Phuket, Thailand) tsunamis. Fragility curves represent the impact of tsunami characteristics on structural components and express the likelihood of a structure reaching or exceeding a damage state in response to a tsunami intensity measure. The Sunda Strait and Sulawesi–Palu tsunamis are uncommon events still poorly understood compared to the Indian Ocean tsunami (IOT), and their post-tsunami databases include only flow depth values. Using the TUNAMI two-layer model, we thus reproduce the flow depth, the flow velocity, and the hydrodynamic force of these two tsunamis for the first time. The flow depth is found to be the best descriptor of tsunami damage for both events. Accordingly, the building fragility curves for complete damage reveal that (i) in Khao Lak–Phuket, the buildings affected by the IOT sustained more damage than the Sunda Strait tsunami, characterized by shorter wave periods, and (ii) the buildings performed better in Khao Lak–Phuket than in Banda Aceh (Indonesia). Although the IOT affected both locations, ground motions were recorded in the city of Banda Aceh, and buildings could have been seismically damaged prior to the tsunami's arrival, and (iii) the buildings of Palu City exposed to the Sulawesi–Palu tsunami were more susceptible to complete damage than the ones affected by the IOT, in Banda Aceh, between 0 and 2 m flow depth. Similar to the Banda Aceh case, the Sulawesi–Palu tsunami load may not be the only cause of structural destruction. The buildings' susceptibility to tsunami damage in the waterfront of Palu City could have been enhanced by liquefaction events triggered by the 2018 Sulawesi earthquake.

Indonesia regularly faces natural disasters such as earthquakes,
volcanic eruptions, and tsunamis because of its geographic location in a
subduction zone of three tectonic plates (Eurasian, Indo-Australian, and
Pacific plates) (Marfai et al., 2008; Sutikno, 2016). The Sunda Arc extends for 6000 km from the north of Sumatra to Sumbawa Island (Lauterjung et
al., 2010) (Fig. 1a). Megathrust earthquakes regularly occur in this region, causing horizontal and vertical movement of the ocean floor which tends to be tsunamigenic (McCloskey et al., 2008; Nalbant et al., 2005; Rastogi, 2007). These tsunamis are likely to cause greater destruction as they can follow prior damaging earthquake ground shaking and/or liquefaction (Sumer et al.,
2007; Sutikno, 2016). Earthquake-generated tsunamis also tend to have longer wave periods affecting the coast than non-seismic ones (Day, 2015; Grezio et al., 2017). On 26 December 2004, the Sumatra–Andaman earthquake (

The term “tsunami fragility” is a measure recently proposed to estimate structural damage and casualties caused by a tsunami, as mentioned by Koshimura et al. (2009b). Tsunami fragility curves are functions expressing the damage probability of structures (or death ratio) based on the hydrodynamic characteristics of the tsunami inundation flow (Koshimura et al., 2009a, b). These functions have been widely developed after tsunami events such as the 2004 Indian Ocean tsunami (IOT; Koshimura et al., 2009a, b; Murao and Nakazato, 2010; Suppasri et al., 2011), the 2006 Java tsunami (Reese et al., 2007), the 2010 Chilean tsunami (Mas et al., 2012), or the 2011 great eastern Japan tsunami (Suppasri et al., 2012, 2013). Several methods aim to develop building fragility curves based on (i) a statistical analysis of on-site observations during field surveys of damage and flow depth data (empirical methods) (Suppasri et al., 2015, 2020), (ii) the interpretation of damage data from remote sensing coupled with tsunami inundation modelling (hybrid methods) (Koshimura et al., 2009a; Mas et al., 2020; Suppasri et al., 2011), or (iii) structural modelling and response simulations (analytical methods) (Attary et al., 2017; Macabuag et al., 2014).

Here, we empirically developed building fragility curves for the 2018 Sunda Strait, 2018 Sulawesi–Palu, and 2004 Indian Ocean (Khao Lak–Phuket, Thailand) tsunamis based on the Global Earthquake Model (GEM) guidelines (Rossetto et al., 2014). From the field surveys conducted after the 2018 Sunda Strait (Syamsidik et al., 2019), 2018 Sulawesi–Palu (Paulik et al., 2019), and 2004 Indian Ocean (Khao Lak–Phuket, Thailand) (Foytong and Ruangrassamee, 2007; Ruangrassamee et al., 2006) events, we utilize three databases called DB_Sunda2018, DB_Palu2018, and DB_Thailand2004, respectively. In the literature, tsunami inundation modelling has been performed many times to better understand the tsunami hydrodynamics, especially for earthquake-generated tsunamis (Charvet et al., 2014; Gokon et al., 2011; Koshimura et al., 2009a; Macabuag et al., 2016; De Risi et al., 2017; Suppasri et al., 2011). Compared to the 2004 IOT, the 2018 Indonesian tsunamis are uncommon events remaining less understood. Therefore, to improve our understanding of the structural damage caused by the Sunda Strait and Sulawesi–Palu tsunamis and to discuss the impact of wave period, ground shaking, and liquefaction events, we reproduce their tsunami intensity measures (i.e., flow depth, flow velocity, and hydrodynamic force) based on two-layer modelling (TUNAMI two-layer). We then compared the fragility curves of the Sunda Strait, Sulawesi–Palu, and Indian Ocean (Khao Lak–Phuket) tsunamis to those derived for the 2004 IOT in Banda Aceh (Indonesia), produced by Koshimura et al. (2009a). In this study, we explore the characteristics of building fragility curves for the 2018 Sunda Strait event and 2004 IOT in Khao Lak–Phuket, as well as for complex events, such as the 2018 Sulawesi–Palu tsunami in Palu City and the 2004 IOT in Banda Aceh, where the tsunamis may not be the only cause of structural destruction. Studying the impact of the wave period, ground shaking, and liquefaction events on the structural performance of buildings aims to improve our knowledge on the relationship between local vulnerability and tsunami hazard in Indonesia.

A post-tsunami database has been established for the Sunda Strait area by Syamsidik et al. (2019), Palu Bay by Paulik et al. (2019), and Khao Lak–Phuket by Ruangrassamee et al. (2006) and Foytong and Ruangrassamee (2007) in urban areas strongly affected by these events. These databases include 98, 371, and 120 observed flow depth traces at buildings, respectively. Here, the tsunami fragility analysis stands on subsets of the original databases of the 2018 Sunda Strait, 2018 Sulawesi–Palu, and 2004 Indian Ocean (Khao Lak–Phuket) tsunamis, as explained in Sects. 3.2.2, 3.2.3, and 2.2, respectively. We define these subsets as “new” databases, and we call them DB_Sunda2018, DB_Palu2008, and DB_Thailand2004, respectively. We note that the use of smaller databases for the fragility assessment is expected to increase the uncertainty in the exact shape of the fragility curves. Each database gathers exclusive information regarding the degree of damage, the building characteristics, and the flow depth traces (Tables 1 and 2). A brief analysis of the key variables (i.e., damage scale, building class, and tsunami intensity) are presented below.

Harmonization between the different damage scales used in DB_Sunda2018, DB_Palu2018, and DB_Thailand2004.

Each field survey adopted a different scale to record the degree of
structural damage. In DB_Sunda2018, the five-state damage scale proposed by
Macabuag et al. (2016) and Suppasri et al. (2020) is adopted, ranging
from no damage to complete damage or washed away. In DB_Palu2018, the observed damage was classified into four states: no damage, partial damage repairable, partial damage unrepairable, and complete damage, as proposed by Paulik et al. (2019). Finally, in DB_Thailand2004, a four-state damage scale is defined by Ruangrassamee et al. (2006). To simplify the comparison between the fragility curves, a harmonization of damage scales is proposed (Table 1). In this study, a four-state damage scale ranging from

Each survey also recorded the building construction type, which influences
the damage probability (Suppasri et al.,
2013). In Table 2, among the 94 buildings included
in DB_Sunda2018, 67 are confined masonry, 26 are timber, and 1
is a steel frame building. In DB_Palu2018, most of the
buildings are confined masonry with unreinforced clay bricks
(

The tsunami intensity has been measured in terms of flow depth level. Table 2 also presents the number of flow depth traces at surveyed buildings and the range of flow depth levels for each database.

Observed flow depth traces at buildings, range of flow depth levels, and building characteristics in DB_Sunda2018, DB_Palu2018, and DB_Thailand2004.

The TUNAMI two-layer tsunami model used in the Sunda Strait and Palu areas
relies on a two-layer numerical model solving non-linear shallow water
equations. It considers two interfacing layers and appropriate kinematic and
dynamic boundary conditions at the seafloor, interface, and water surface
(Imamura and
Imteaz, 1995; Pakoksung et al., 2019). To reproduce the landslide-generated
tsunami, we model the interactions between tsunami generation and submarine
landslides as upper and lower layers. The mathematical model performed in
the landslide-tsunami code is obtained from a stratified medium with two
layers. The first layer, composed of a homogeneous inviscid fluid with
constant density,

The continuity equation of the seawater (first layer) is

The momentum equations of the seawater in the

The continuity equation of the landslide (second layer) is

The momentum equations of the landslide in the

Index 1 and 2 refer to the first and the second layers, respectively, and

BATNAS and DEMNAS, Indonesia, provided the bathymetric and topographic data
at 180 and 8 m resolutions, respectively. The data were established from
synthetic aperture radar (SAR) images (

For tsunami inundation modelling in a densely populated area, we apply a
resistance law with the composite equivalent roughness coefficient depending
on the land use and building conditions, as shown in Eq. (8)
(Aburaya and Imamura, 2002; Koshimura et al., 2009a).

The tsunami inundation model is calibrated using two performances
parameters:

Topographic corrections performed on the DSM and the 1st DEM. The 2nd DEM is used as new topography in the TUNAMI two-layer model.

To correct the digital surface model (DSM), we removed the vegetation,
building, and infrastructure elevations based on the linear smoothing
method and used the resulting digital elevation model (1st DEM) as
topography in the tsunami inundation model (Fig. 3). The vertical accuracy of the DSM and DEM is about 4 m. The 2018 Sunda Strait tsunami model depends on the density of the landslide (

We increased the mean sea level (MSL) by 2.3 m to reproduce the high tide during the 2018 Sulawesi–Palu tsunami. As shown by Pakoksung et al. (2019), the observed waveform at Pantoloan tidal gauge does not fit the simulated one with the finite fault model of TUNAMI-N2. Although recent studies show that seismic seafloor deformation may be the primary cause of the tsunami (Gusman et al., 2019; Ulrich et al., 2019), in this study, the main assumption is that the 2018 Sulawesi–Palu event was triggered by subaerial and submarine landslides. According to Heidarzadeh et al. (2019), a large landslide to the north or the south of Pantoloan tidal gauge is responsible for the significant height wave recorded. Arikawa et al. (2018) also identified several sites of potential subsidence in the northern part of Palu Bay. Based on these previous studies, we assume two large landslides: L1 and L2. Small landslides (S1–S12) also occurred in the bay; their location is known from observations from satellite imagery, field surveys, and video footage (Arikawa et al., 2018; Carvajal et al., 2019) (Fig. 6). From the trial and error method and the topographic and bathymetric data provided by the Geospatial Information Agency (BIG), we determined the soil property and achieved the volume of the landslides (Table 3). In Fig. 7, the submarine landslides model reproduces well the tsunami observations at Pantoloan.

Location of the hypothesized landslides (S: small; L: large) in Palu Bay (background ESRI).

Hypothesized landslide parameters (location and volume) in Palu Bay.

Comparison between observed and simulated wave heights at Pantoloan tidal gauge in Palu Bay, Sulawesi, Indonesia.

The calibration of the model depends on the landslide S8 because (i) as a
small landslide, its volume is too small to distort the simulated wave
height at the Pantoloan tidal gauge, (ii) it has the largest volume among
the other small landslides, and (iii) it is close and ideally oriented to
Palu City; the slide direction, captured by an aircraft pilot, is
perpendicular to the bay (Carvajal et al., 2019).
The density of the landslides (

Sulawesi–Palu final tsunami inundation model with the maximum simulated flow depth overlaid on the damaged building data (background ESRI).

Comparison between observed and simulated flow depths at damaged building for an S8 ratio of 1.2; a confidence interval is set at 1 m flow depth.

The proposed fragility assessment framework has two main steps. In the first step, an exploratory analysis aims to (i) assess the trends that the available data follow and (ii) determine the main explanatory variables that need to be included in the statistical model and their influence on the slope and intercept of the fragility curves. Then, we select a statistical model and examine its goodness-of-fit to the data based on the observations of the exploratory analysis. We note that the development of the computed fragility curves for the 2018 Sunda Strait and 2018 Sulawesi–Palu tsunamis is directly based on DB_Sunda2018 and DB_Palu2018, in which each building has both observed and simulated flow depth values (Table 4).

Number of buildings used for the tsunami fragility analysis of the 2018 Sunda Strait, 2018 Sulawesi–Palu, and 2004 IOT (Khao Lak–Phuket) events.

To explore the relationship between the tsunami intensity and the
probability of damage, we fit a generalized linear model (GLM) to the data
of each database, as proposed by the GEM guidelines
(Rossetto et al., 2014). A GLM assumes that the
response variable

Based on the aforementioned observations, we construct parametric statistical models for the three databases to (i) identify the simulated tsunami measure type that fits the data best and (ii) construct fragility curves for the tsunami intensity type that fits the data best.

Ideally, the response variable

Statistical models examined for each database.

Probit functions fitted for each individual damage state to DB_Sunda2018

In what follows, we fit multiple models to each database based on the observations of the exploratory analysis. We examine the goodness of fit of these models for a given tsunami intensity measure and link function with two formal tests, as proposed in the GEM guidelines (Rossetto et al., 2014). Firstly, we compare the Akaike information criterion (AIC) values, which estimates the prediction error of the examined models (Akaike, 1974). The model with the lowest value fits the data best. The alternative models used in this study are nested, which means that the more complex model includes all the terms of the simpler ones plus an additional term. For this reason, we also perform a series of likelihood ratio tests to examine whether the fit provided by the model with the lowest AIC value is statistically significant over its alternative nested models, which relaxes its assumptions (Rossetto et al., 2014). We also use the AIC value to determine which of these simulated intensity measures fits the data best. Furthermore, the 90 % confidence intervals of the best-estimate fragility curves are constructed using bootstrap analysis. According to the latter analysis, 1000 samples of the database are obtained with a replacement, and the selected model is refitted to each sample.

We fit the GLM models to the data in DB_Sunda2018
(irrespective of their structural characteristics), and we plot the obtained
probit functions against the natural logarithm of the observed flow depth to
explore how the slope and the intercept of the models change for each damage
state (Fig. 10a). The 90 % confidence intervals
around the best-estimate curves are also included. All three curves have
positive slopes, which indicates that the flow depth is an adequate
descriptor of the damage caused by a tsunami as the probability of a given
damage state being reached or exceeded increases with the increase in the
flow depth. The slope of each function is similar for

Following the main observations of the exploratory analysis, we consider
that M3 is an acceptable model with two explanatory variables: the tsunami
intensity and the construction type. To assess its goodness of fit, we
consider each link function with three alternatives for the linear predictor
(i.e., M4, M5, and M1) which relax some of its assumptions. In
Table 6, we compare the AIC values of the three
models to assess the fit of the different models for the observed flow depth
levels assuming the probit link function. M3 has the smallest AIC value compared to its alternatives, which indicates that it fits the data better than the
remaining three models. Nonetheless, some of these differences are rather
small, and it raises the question of whether the improvement in the fit
provided by M3 is statistically significant over its alternatives. To
address this, we perform likelihood ratio tests, and the results are reported
in Table 7. We note that the

The regression coefficients of the 2018 Sunda Strait fragility curves based on the best-fitted M3 model with a probit link function are listed in Table E1. An advantage of constructing a complex model that accounts for the ordinal nature of the damage and for the two main construction types in the systematic component is that fragility curves for timber buildings can be obtained even for the states for which there are available data. A timber building is found to sustain more damage than confined masonry buildings for the more intense damage states. Nonetheless, there is substantially more uncertainty in the prediction of the likelihood of damage, and this can be attributed to the rather small sample size.

AIC values for the three models assuming probit link function fitted to the observed and simulated tsunami intensity measures of DB_Sunda2018.

Likelihood ratio test summary for all available observed and simulated tsunami intensity measures of DB_Sunda2018.

We also fit GLM models to the data in DB_Palu2018 using the
observed tsunami flow depth to express the tsunami intensity and then to
construct fragility curves and their 90 % confidence intervals for the
three individual damage states (Fig. 11). The data
seem to produce fragility curves with positive slopes for

Probit functions fitted for each individual damage state to DB_Palu2018 to assess whether the observed flow depth is an efficient descriptor of damage. The 90 % confidence interval is plotted.

Based on the observations of the exploratory analysis, we use identical
slopes for the fragility curves for all three damage states
(

AIC values for model M1 fitted to the simulated tsunami intensity measures of DB_Palu2018.

The exploratory analysis aims to identify trends in the shape of the fragility curves for each damage state. Thus, we fit GLM models to DB_Thailand2004 to construct fragility curves for the three individual damage states, and we plot them with their 90 % confidence interval in Fig. 12. The data seem to produce fragility curves with positive slopes for all three damage states and also are parallel to each other, which suggests that the slope should be identical for all three curves.

Probit functions fitted for each individual damage state to DB_Thailand2004 to assess whether the observed flow depth is an efficient descriptor of damage. The 90 % confidence interval is plotted.

Based on the observations of the exploratory analysis, we consider model M1
as the most suitable. To test its goodness of fit, model M2, which relaxes
the assumption that the slope of all three curves is identical, is also
fitted to the data. In Table 9, the comparison of
the AIC values for the two models also shows that M1 is the model which fits
the data best for all three link functions considered in this study (i.e.,
probit, logit, and cloglog). We also perform a likelihood ratio test to
confirm that the improvement in the fit provided by the more complex M2
model over M1 is not statistically significant. The

AIC values for the two models fitted to the observed flow depth of DB_Thailand2004.

The fragility curves determine conditional damage probabilities according to
the tsunami intensity measures of the 2018 Sunda Strait event for both
confined masonry concrete (Fig. 13a–c) and timber (Fig. 14a–c) buildings of DB_Sunda2018. In Fig. 14a and b, there are no data to predict the shape of the curves between 0–1 m flow depth and 0–1

The 2018 Sunda Strait curves for confined masonry concrete buildings. Best-estimate fragility curves, with their 90 % confidence intervals, as functions of

The 2018 Sunda Strait curves for timber buildings. Best-estimate fragility curves, with their 90 % confidence intervals, as functions of

The 2018 Sulawesi–Palu tsunami curves are developed for confined masonry
buildings with unreinforced clay brick of DB_Palu2018. The
computed and surveyed curves show a similar damage trend. When the observed
and simulated flow depths reach 1.5 m, the building damage probabilities for
partial damage repairable (i.e.,

The 2018 Sulawesi–Palu curves for confined masonry buildings. Best-estimate fragility curves, with their 90 % confidence intervals, as functions of

In Fig. 16, we compare (i) the Sunda Strait and Sulawesi–Palu

Best-estimate fragility curves for the 2018 Sunda Strait tsunami, 2018 Sulawesi–Palu tsunami, and 2004 IOT in Khao Lak–Phuket (Thailand) and Banda Aceh (Indonesia) as functions of

The reliability of the curves depends mainly on (i) the quality and the
quantity of post-tsunami data and (ii) whether the tsunami intensity
measures are efficient predictors of damage. With regard to the first
factor, DB_Sunda2018, DB_Palu2018, and
DB_Thailand2004 include relatively little data
(Table 2). For each database, the relatively broad
confidence intervals around the best-estimate fragility curves reflect the
small sample size. Moreover, the complexity of each studied event also plays
a role in how well the selected tsunami intensity measure can represent the
tsunami damage. In particular, in DB_Sunda2018 and
DB_Thailand2004, only the tsunami load is responsible for the
building damage. In contrast, in DB_Palu2018, buildings may
have suffered prior damage due to ground shaking and liquefaction
(Kijewski-Correa and Robertson, 2018; Sassa
and Takagawa, 2019). Nonetheless, we are not able to establish precisely
which of the surveyed buildings had suffered prior damage in the database
and to what extent. The complexity of the 2018 Sulawesi–Palu event could
introduce a bias in the tsunami fragility assessment, and this has also been
mentioned for other events such as the 2011 great eastern Japan tsunami
(Charvet et al., 2014). This bias
could explain why we observed a negative slope for our

The curve comparison illustrates well the relationship between the 2004 Indian Ocean, the 2018 Sunda Strait, and the 2018 Sulawesi–Palu tsunamis characteristics, summarized in Table 11, and the structural performance of buildings.

Damage probabilities of buildings reaching complete damage according to the intensity measures of the 2018 Sunda Strait, 2018 Sulawesi–Palu, and 2004 Indian Ocean (Khao Lak–Phuket and Banda Aceh) tsunamis.

Characteristics of the 2004 Indian Ocean tsunami in Banda Aceh (Indonesia) and Khao Lak–Phuket (Thailand), as well as the 2018 Sulawesi–Palu and 2018 Sunda Strait tsunamis.

The 2018 Sunda Strait tsunami and the 2004 IOT (Khao Lak–Phuket, Thailand) are characterized by dominant wave periods of about 7 min (Muhari et al., 2019) and 40 min (Karlsson et al., 2009; Puspito and Gunawan, 2005; Tsuji et al., 2006), respectively (Table 11). Damage from ground shaking or liquefaction episodes was not reported, so the tsunami is the main cause of building damage. We compare the Sunda Strait and the Indian Ocean (Khao Lak–Phuket) curves based on the flow depth to investigate the impact of the tsunami wave period on buildings. In Fig. 16a, the curves showed that the short wave period tsunami in the Sunda Strait is less damaging than the 2004 IOT below 5 m flow depth. For instance, for 3 m flow depth, the likelihood of complete damage is around 20 % in Khao Lak–Phuket against only 10 % in the Sunda Strait area (Table 10). On the other hand, above 5 m flow depth, the structures in Khao Lak–Phuket reveal a better performance than the ones in the Sunda Strait area. As few data points are available beyond this value for completely damaged buildings, the Sunda Strait and the Indian Ocean (Khao Lak–Phuket) curve reliability is insufficient. Even though the long wave periods of the IOT seem to increase the likelihood of building damage, the sample size of collapsed buildings beyond 5 m flow depth is too small to validate this assumption.

The city of Banda Aceh and the Khao Lak–Phuket area were damaged by the 2004 IOT. Along Banda Aceh shores, the simulated tsunami wave period ranges from 40 to 45 min (Prasetya et al., 2011; Puspito and Gunawan, 2005), and the one simulated in Khao Lak–Phuket is estimated at approximatively 40 min (Karlsson et al., 2009; Puspito and Gunawan, 2005; Tsuji et al., 2006). Although the tsunami wave periods are similar at both locations, the 2004 Indian Ocean earthquake was strongly felt in the city of Banda Aceh, where it lasted about 10 min (Table 11). The earthquake intensity is estimated at VII to VIII on the Modified Mercalli Scale (Ghobarah et al., 2006; Saatcioglu et al., 2006). Despite the ground acceleration not being recorded in the damage zones, seismic failure was distinguished from tsunami damage. For example, buildings with three to five stories were heavily damaged by the ground motion, which was amplified by the soft soil characteristics, compared to low-rise structures. In Fig. 16a, the curves estimate about 50 % and 20 % of building damage probabilities for complete damage in Banda Aceh and Khao Lak–Phuket, respectively, for 3 m flow depth (Table 10). Therefore, the building resilience is higher in Khao Lak–Phuket than in Banda Aceh. It comes from the fact that the Khao Lak–Phuket curve is developed for reinforced concrete buildings, while the ones in Banda Aceh are produced for mixed buildings (Koshimura et al., 2009a). Another reason is that the 2004 Indian Ocean earthquake was not recorded in Khao Lak–Phuket, so the ground motion did not damage the buildings before the tsunami's arrival. Furthermore, the likelihood of complete damage is very high for low inundation depth levels in Banda Aceh. This feature is usually observed for buildings suffering prior damage such as ground shaking and/or liquefactions episodes, as mentioned by Charvet et al. (2014) for the 2011 great eastern Japan event.

The 2018 Sulawesi–Palu event is characterized by short wave periods of about 3.5 min according to Syamsidik et al. (2019), like the 2018 Sunda Strait tsunami (Table 11). However, the curves based on the flow depth are remarkably different (Fig. 16a). For instance, for 3 m flow depth, the likelihood of complete damage is 25 % in Palu against 10 % in Sunda Strait, which means that buildings affected by the Sulawesi–Palu tsunami were more susceptible to complete damage. Most importantly, up to 2 m flow depth, the building damage probability is higher in Palu than in Banda Aceh, affected by ground shaking and then being hit by a long wave period tsunami. As an example, for 1 m flow depth, the building damage probability of complete damage is about 10 % in Palu against less than 5 % in Banda Aceh (Table 10). The main cause of structural damage caused by the Sulawesi–Palu tsunami is still being investigated. Mas et al. (2020) suggested that the tsunami hydrodynamic or debris impact might be the main cause of structural destruction in the waterfront area of Palu Bay. Here, the flow velocity and the hydrodynamic force are not good descriptors of damage, so we cannot support this assumption (Song et al., 2017). On the other hand, Palu City sits on alluvial soil layers from Palu River and is thereby vulnerable to liquefaction disaster (Darma and Sulistyantara, 2020; Goda et al., 2019; Kijewski-Correa and Robertson, 2018). Even though the largest liquefaction areas were recorded outside the inundation zone (Watkinson and Hall, 2019), Sassa and Takagawa (2019) and Kijewski-Correa and Robertson (2018) observed land retreats along the coastal area of Palu City (Fig. 17a and b). Most of the masonry-type buildings completely damaged are very close to these coastal retreats. Some of them were washed away by the tsunami. Therefore, these buildings do not have flow depth values and could not be used for the tsunami fragility assessment (Fig. 17b). Furthermore, in Palu, the earthquake intensity is estimated at VII to VIII on the Modified Mercalli Scale, but ground shaking was not the main cause of structural destruction (Kijewski-Correa and Robertson, 2018; Supendi et al., 2019). The likelihood of complete damage is also relatively high for low flow depth levels, so ground motion could have triggered liquefaction events and enhanced the building susceptibility to tsunami damage in the waterfront of Palu City. This assumption cannot be verified through satellite images; it needs direct and close observations, which might be erased by the tsunami.

According to the GEM guidelines, building fragility curves of the 2018 Sunda
Strait, 2018 Sulawesi–Palu, and 2004 Indian Ocean (Khao Lak–Phuket, Thailand)
tsunamis are empirically developed from post-tsunami databases respectively
called DB_Sunda2018, DB_Palu2018, and DB_Thailand2004. To improve our understanding of the
structural damage caused by the Sunda Strait and Sulawesi–Palu tsunamis, we
reproduce their tsunami intensity measures (i.e., flow depth, flow velocity,
and hydrodynamic force) with the TUNAMI two-layer model for the first time. The
flow depth is constantly the best descriptor of tsunami damage for each
event. The building fragility curves for complete damage reveal the following. (i) The buildings affected by the Sunda Strait tsunami sustained less damage
than the ones in Khao Lak–Phuket (IOT). For example, for 3 m flow depth, the
building damage probability is around 20 % in Khao Lak–Phuket against 10 % in the Sunda Strait area hit by a short wave period tsunami (landslide
source). Considering that the tsunami was the main cause of structural damage
(i.e., damage related to ground shaking and/or liquefaction was
not recorded), the longer wave period of the 2004 IOT may have increased the
likelihood of complete damage, and (ii) the building resilience is weaker in
Banda Aceh than in Khao Lak–Phuket. For 3 m flow depth, the likelihood of
complete damage is about 50 % in Banda Aceh and 20 % in Khao Lak–Phuket. Although both locations were hit by the 2004 IOT, Banda
Aceh was strongly affected by ground shaking before the tsunami's arrival, and
(iii) the buildings affected by the Sulawesi–Palu tsunami were more
susceptible to be completely damaged than the ones affected by the IOT in
Banda Aceh (i.e.,

Two-layer modelling of a subaerial and submarine landslide (from the original sketch of Pakoksung et al., 2019):

Temporal evolution of the 2018 Sunda Strait tsunami wave

Temporal evolution of the 2018 Sunda Strait tsunami wave

Temporal evolution of the 2018 Sulawesi–Palu tsunami wave

Temporal evolution of the 2018 Sulawesi–Palu tsunami wave

Sulawesi–Palu final tsunami inundation model with the maximum simulated flow velocity overlaid on the damaged building data (background ESRI).

AIC values for the three models assuming logit and cloglog link functions fitted to the observed and simulated tsunami intensity measures of DB_Sunda2018.

Regression coefficients for the 2018 Sunda Strait tsunami fragility curves based on DB_Sunda2018.

Regression coefficients for the 2018 Sulawesi–Palu tsunami fragility curves based on DB_Palu2018.

Regression coefficients for the 2004 IOT in Khao Lak–Phuket (Thailand) based on DB_Thailand2004.

Post-tsunami field survey data are available from references cited in the text. The bathymetric and topographic data for the Sunda Strait area were provided by BATNAS and DEMNAS, Indonesia, respectively
(

FI, AS, KP, EL, II, and FB designed and coordinated this research. AS, KP, and EL performed the tsunami simulations and participated in the calibration of the inundation models in Palu Bay and in Sunda Strait. SS and RP contributed to the tsunami data collection in the Sunda Strait and Palu areas, respectively. II developed the fragility functions through advanced statistical analysis. All authors contributed to the drafting of the manuscript.

The authors declare that they have no conflict of interest.

We greatly acknowledge the three reviewers for their constructive comments and recommendations that helped to improve the quality of this manuscript. This research was funded and supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Young Scientists, the JSPS-NRCT Bilateral Research grant, the World Class Professor (WCP) programme 2018–2020 promoted by the Ministry of Education and Culture of the Republic of Indonesia, the Pacific Consultants Co., Ltd., the Willis Research Network (WRN), the Tokio Marine & Nichido Fire Insurance Co., Ltd., the National Institute of Water and Atmospheric Research (Project: CARH2106), UKRI GCRF Urban Disaster Risk Hub, and GLADYS.

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This research has been supported by the Japan Society for the Promotion of Science (Grant-in-Aid for Young Scientists (B)), the National Research Council of Thailand (Bilateral Research grant, fiscal year 2017–2018), the Kementerian Riset Teknologi Dan Pendidikan Tinggi Republik Indonesia (World Class Professor, WCP, programme 2018–2020), the Université de Montpellier (grant GLADYS), the Global Challenges Research Fund (grant no. Urban Disaster Risk Hub NE/S009000/1), the National Institute of Water and Atmospheric Research (project: CARH2106), Pacific Consultants Co., Ltd., Willis Research Network (WRN), and Tokio Marine & Nichido Fire Insurance Co., Ltd.

This paper was edited by Maria Ana Baptista and reviewed by three anonymous referees.