Effects of advection on predicting construction debris for vulnerability assessment under multi-hazard earthquake and tsunami

Coastal Engineering 153 (2019) 103541

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Effects of advection on predicting construction debris for vulnerability assessment under multi-hazard earthquake and tsunami Hyoungsu Park a, *, Daniel T. Cox b a b

Department of Civil and Environmental Engineering, 383 Holmes Hall, 2540 Dole Streets, Honolulu, HI, 96822, United States School of Civil and Construction Engineering, 101 Kearney Hall, Oregon State University, Corvallis, OR, 97331, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Tsunami Earthquake Debris Advection Community resilience

Post survey results from devastating events such as the 2010 Chile and 2011 Tohoku earthquake and tsunami reported that different types of debris could be generated and shifted landward during tsunami inundation. That advected debris can result in significant damage to structures and negative impacts on community resilience during the recovery process. To improve mitigation plans, minimize losses, and to improve the community resilience to future tsunami events, it is necessary to quantify the debris and predict its final distribution. In this study, we present a framework to quantify the amount and location of construction debris generated and advected from a multi-hazard earthquake and tsunami event. The framework performs fragility analysis based on maximum intensity measures of the hazards, quantifies the amount of debris, and then advects the buoyant portion of debris using a time-dependent inundation model to estimate the trajectory and final distribution of debris. We apply this framework to Seaside, Oregon, subjects to events from the Cascadia Subduction Zone for eight recurrence intervals over the range of 100 to 10,000 years. Comparison of the debris distribution with and without the advection model highlights the importance of including advection to understand the final debris distribution. We show that the final debris distribution could have a significant impact on the initial accessibility and functionality of critical facilities which would be difficult to estimate considering the hazard intensity only. We show how the volume of debris generated and advected increases with the decreasing annual exceedance probability (increasing return period) and how the location of the peak cross-shore debris profile is related to the maximum limit of tsunami runup. This analysis considers only buoyant construction debris and could be extended to consider nonbuoyant, natural (e.g., vegetation) and anthropogenic (e.g., vehicles, shipping con­ tainers, marine vessels) debris.

1. Introduction

damaged buildings, contents from the damaged buildings, uprooted trees, cars, marine vessels or shipping containers have caused significant damage either due to debris impact or by damming forces. It also in­ creases the hydrodynamic drag on structures during inundation (Yeh et al., 2014). Recent efforts have tried to quantify the impact of tsunami induced water-borne debris on structures through both physical exper­ iments (Riggs et al., 2013; Goseberg et al., 2016; Stolle et al., 2017; Alam et al., 2017) and numerical simulations (Como and Mahmoud, 2013; Ardianti et al., 2018). In addition to the direct damage from water-borne debris, posthurricane and tsunami survey results have shown that movement of debris due to advection during inundation can restrict access to critical facilities which negatively impacts community recovery (Ghobarah et al., 2006). Additionally, debris can disrupt transportation routes

The study of debris generated from natural disasters such as hurri­ canes, tornados, floods, earthquakes, and tsunamis is important to es­ timate damage to the built and natural environments, to estimate disruption to the transportation infrastructure (e.g., blockage of bridges and roadways), and to plan for debris removal to speed up the recovery process of communities affected by natural disasters. Typically, windborne debris is a major source of damage during strong wind events (Lin and Vanmarcke, 2008; Herbin and Barbato, 2012). Similarly, water-borne debris could exacerbate the damage to structures during riverine flooding events (Haehnel and Daly, 2004) and coastal inunda­ tion during hurricane storm surge or tsunamis (Naito et al., 2013; Ko et al., 2014). Particularly in the case of tsunamis, buoyant debris such as

* Corresponding author. E-mail address: [email protected] (H. Park). https://doi.org/10.1016/j.coastaleng.2019.103541 Received 20 January 2019; Received in revised form 31 August 2019; Accepted 2 September 2019 Available online 5 September 2019 0378-3839/© 2019 Elsevier B.V. All rights reserved.

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Coastal Engineering 153 (2019) 103541

to other structures ‘downstream’ of the original debris sources, and we do not consider the effects of vegetation, sediment or other natural debris. While these are perhaps crude assumptions, they were necessary for a first step to quantify the effects of tsunami debris advection. Further refinement of the model is possible in future studies and restates in the discussion section.

system which is connecting critical facilities of community, such as emergency shelters, hospitals, ambulance units, fire and police stations, jails, and government facilities like a city hall (e.g. Yuepeng et al., 2016). Moreover, even a small portion of debris can potentially clog rivers and streams causing additional flooding (Abbe and Montgomery, 1996), and hazardous debris can also contaminate land and drinking water (Channell et al., 2009). It is necessary to quantify the total volume or weight of debris and predict its trajectory and final location to minimize the negative impacts of debris on the recovery process. Moreover, the debris quantification is important to understand the content for subsequent recycling or disposal. In the case of tsunami, Tanikawa et al. (2014) quantified the debris from buildings and other infrastructure after 2011 Tohoku tsunami in Japan. Prasetya et al. (2012) used a Lagrangian 3-D disper­ sion model to evaluate the pathway and the distribution of suspended debris in Banda Ache from the 2004 India Ocean tsunami. Recently, the impact of debris on the tsunami run-up and the sensitivity of debris dislocation due to initial position of debris have been studied through physical modeling (Yao et al., 2018). Despite the importance of debris in disaster mitigation planning and response, there are only limited studies on tsunami induced debris quantification or prediction of debris tra­ jectory and final distribution. In this study, we provide a framework for a debris forecasting model under combined earthquake and tsunami hazards, including debris quantification and advection. We quantify the volume of construction debris (generated from damaged buildings) from tsunamigenic earth­ quake events for different recurrence intervals using fragility analysis. Then, we predict the final distributions of debris due to the tsunami inundation through an advection model using a Lagrangian approach for each debris particle. We compare the final distributions of debris with and without advection, and we also evaluate the cross profile of debris distribution concerning critical facilities and major routes in the study region of Seaside, Oregon. There are several simplifications as will be detailed later, including a relatively simple thresholding approach for initiation of debris motion and debris grounding; and we assume that there is no debris-debris or debris-structure interactions and that the debris does not affect the overall flow field. Further, we do not quantify the direct effect of the debris on damage

2. The methodology of debris forecasting under multi-hazard earthquake and tsunami The methodology of earthquake and tsunami induced debris fore­ casting model composes of four parts: (A) Hazard description, (B) Community description, (C) Damage to the built environment, and (D) Loss of community functionality as shown by the gray boxes in Fig. 1. Each arrow in the figure indicates the required input or output between each step. In general, the intensity measures provided in the hazard description (A) are utilized to estimate the combined earthquake and tsunami damages of the built environment (C) via fragility functions which are determined following the community descriptions (B). The results of the damage assessment are used to quantify debris and classify the portion of non-buoyant and buoyant debris. The time-dependent tsunami inundation model is applied as an advection model to tracking the portion of the buoyant debris, and the functional loss to the community (D) is assessed by evaluating the proximity of the advected debris to critical facilities (e.g., transport system). The earthquake and tsunami hazard in Part A can be characterized through either a scenariobased or a probabilistic approach. While scenario-based approaches are useful particularly for worst-case conditions involving life-safety, probabilistic approaches are becoming more favorable because they can lead to risk-based analysis. For earthquakes, the Probabilistic Seismic Hazard Assessment (PSHA) is a widely used approach that began in the early 1970s (Benjamin and Cornell, 1970). More recently for tsunamis, the Probabilistic Tsunami Hazard Assessment (PTHA) have been developed for a number of coastal communities (Mori et al., 2018). There are relatively few Probabilistic Seismic and Tsunami Hazard Assessment (PSTHA) studies that capture both tsunami and earthquake hazards (De Risi and Goda, 2016; Park et al., 2017b). PSTHA can eval­ uate the annual exceedance probability of both seismic and tsunami

Fig. 1. Flow chart of the debris forecasting model for earthquake and tsunami hazards. 2

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hazards that share the same fault sources. In all of these approaches, Intensity Measures (IMs) are used to describe the strength of hazards. For example, the peak ground acceleration (PGA), peak ground deformation (PGD), and spectral acceleration (Sa) are frequently used as the repre­ sentative IMs for PSHA. The maximum flow depth (hmax), velocity (Vmax), and momentum flux (Mmax) are often used as the representative IMs for PTHA. In addition to the maximum IMs for the damage in Part C, the time-dependent inundation hazards (time-series of flow depth and velocity fields over the entire study regions) are used in the input of debris advection model. The built-environment in the community description in Part B generally includes the information of buildings, critical facilities, and the lifeline infrastructure networks. For this study, we provide the detailed structural information of buildings for the damage assessment in Part C and utilize the geospatial locations of critical facilities and road network system for the loss of functionality analysis in Part D. The structural and non-structural damage assessment for the builtenvironment in Part C are performed using a fragility analysis. Analo­ gous to PSHA, PTHA, and PSTHA, the probabilistic damage assessment can be conducted for seismic (PSDA), tsunami (PTDA), and combined seismic-tsunami damage assessment (PSTDA). The initial inputs of PSTDA are the spatial distributions of seismic and tsunami hazards which are referred to as intensity measures (e.g. PGA, Sa for seismic motion, hmax, Mmax for tsunami inundation). They are generated from PSTHA in terms of the annual exceedance probability of each seismic and tsunami hazard. PSTDA provides the probabilistic results of damage assessment of the community considering combined impacts of seismic and tsunami hazards per each building utilizing fragility functions for different return periods (e.g. 500 yr). The example applications of PSTDA for this study are provided in Section 3, and further detailed description of PSTDA for this study is available in Park et al. (2019). The results of damage assessment and the description of buildings in the community are utilized to quantify the volume (weight) of debris depending on the debris types (e.g. materials, size) for buildings, explained in detail in Section 4. We track the trajectories of various sizes of debris based on tsunami inundation information and estimate the final location of debris (Section 5). The direct structural and non-structural damages on the builtenvironment from the earthquake and tsunami can be used to estimate direct losses (e.g., dollar loss or casualties) in communities. It is more difficult, however, to estimate the loss of functionality in Part D. For example, whether a building can still be used for its intended purpose requires an understanding of the use and the systems required to support it (e.g., water, electricity) and the relation to the populations (e.g. em­ ployees, customers). In this preliminary step to understand how debris generation and advection can impact the loss of functionality, we look at the proximity of the resulting debris field to critical facilities such as schools, hospitals, shelters, fire and police stations (Section 6). A more complex analysis, for example, could consider the disruption of the transportation network to these facilities for a range of needs (e.g. emergency vehicles, repair crews, etc.).

Fig. 2. Sketch of the study region and proximity of Seaside, Oregon, to the Cascadia Subduction Zone.

examples of IMs for tsunami inundation, such as the maximum flow depth (a), velocity (b), and momentum flux (c) for a 1000 year return period CSZ event. In general, relatively higher tsunami IMs are observed at the shoreline, and they generally decrease landward. Although only the 1000 year event is shown for brevity, the 100, 250, 500, 1,000, 2, 500, 5000 and 10,000 year events for both the seismic and tsunami hazards were computed by Park et al. (2017b) and the 250, 500, 1,000, 2500 and 10,000 are used in this analysis. Fig. 4 shows the details of the built environment for Part B. Fig. 4a shows the surface transportation network (roads, bridges) and location of critical infrastructure: electrical substation, airport, fire department, police department, hospital, nursing homes, schools, wastewater treat­ ment facility, and assembly areas for tsunami evacuation. The purple line shows the location of electrical transmission line, and the red line shows the maximum extent of tsunami inundation for the 1000 year event. The city is divided by Necanicum River and Neawanna Creek flowing to the north, parallel to the coastline, and seven bridges are crossing the Necanicum River, and 5 crossing the Neawanna Creek. Fig. 4b shows the building structure inventory at the parcel level described in more detail in Park et al. (2017a). As described in that study, we used a combination of tax assessor data and visual inspection to quantify the building type (wood, concrete), number of stories, and seismic code based on the date of construction. We assumed that there is only a single structure per each parcel located at the centroid of parcel. Fig. 5 shows the output of the building damage estimation for Part C, combining the PSTHA illustrated in Fig. 3 (Park et al., 2017b) and the built environment described in Fig. 4b using a fragility analysis. Fig. 5a and b shows the seismic and tsunami damages independently, where the concrete buildings in the central core are more susceptible to the seismic ground motion (5a), and the wooden residential structures to the north and south are more susceptible to the tsunami (5b). Moreover, the tsunami damage shows a strong gradient of decreasing damage land­ ward consistent with the decrease in tsunami hazards intensity, but the seismic damage is more or less uniform across the city since the seismic hazard is also uniform. Details of the tsunami damage analysis (PTDA) in

3. Application to multi-hazard debris forecasting model at seaside, Oregon To illustrate the framework outlined in Fig. 1, we apply the proposed debris forecasting model to Seaside, Oregon (Fig. 2). This community has been identified as highly vulnerable to future Cascadia Subduction Zone (CSZ) events due to its low elevation and a large percentage of infrastructure and population within the inundation zone (Wood, 2007). Because of its vulnerability, this city has been the subject of a number of tsunami studies (e.g. Tsunami Pilot Study Working Group, 2006; Gonz� alez et al., 2009; Park et al., 2013; Wiebe and Cox, 2014; Wang et al., 2016; Park and Cox, 2016). For Part A, the results of the probabilistic seismic and tsunami hazard analysis (PSTHA) by Park et al. (2017b) is utilized. Fig. 3 shows the 3

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Fig. 3. Spatial distribution of tsunami IMs for 1000 yr event: (a) maximum flow depth, (b) velocity, and (c) momentum flux. Contour line shows minimum values of hmax ¼ 0.3 m, Vmax ¼ 0.3 m/s, and Mmax ¼ 1 m3/s2.

Fig. 4. Overview of Seaside, Oregon with (a) critical facilities and transportation network and (b) building inventory.

Fig. 5b including the spatial distribution details and comparison of depth-based and momentum-based fragilities is given in Park et al., (2017a). The combined seismic and tsunami damage (PSTDA) is shown in Fig. 5c, and details of the methods to combine seismic and tsunami damage are given in Park et al. (2019). Similar to the hazard description in Figs. 3 and 5 only shows an example of damage assessment results from the 1000 year event for illustration purposes, and the results of the PSTDA for the 100 to 10,000 year events are used in this study.

Following the approaches of Hazus-MH2.1 Technical Manual for earthquake (FEMA, 2012), we simplify the various type of debris com­ ponents into two groups: (1) structural fractions, which is a function of structural damage ratio shown in Fig. 5, and (2) non-structural fractions, which is a function of the non-structural damage ratio. We estimate the expected debris fraction (EDF) of each building which is defined as the expected percentage of debris at different levels of structural and non-structural damage due to the combined earthquake and tsunami event. The expected debris fraction for structural damage condition (EDFS) at a single building (at a single tax lot parcel) with a specific building type is given as:

4. The methodology of debris quantification This section introduces the debris quantification methodology shown in Fig. 1, Part C to estimate the debris volume or weight generated from buildings damaged due to both the earthquake and the tsunami.

4 X

EDFS ¼

PS ðiÞ⋅DFS ðiÞ i

4

(1)

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Fig. 5. Probability of structural damage ratio of buildings at Seaside, Oregon, from 1000 year CSZ event for (a) earthquake, (b) tsunami, and (c) combined earthquake and tsunami.

where, the subscript ‘S’ indicates the structural fraction, and the variable ‘i’ indicates the four damage states defined as slight, moderate, exten­ sive, and complete damage states. PS (i) is the probability of structural damage from the combined earthquake and tsunami event at damage state ‘i,’ which is provided from authors’ previous study (Park et al., submitted). DFS (i) is the structural debris fraction (percent) of unit weight at the ‘i’ damage states. Similarly, the expected debris fraction for the non-structural damage (EDFNS) is given by: 4 X

EDFNS ¼

DWNS ¼ ðEDFNS;B ⋅ UWNS;B þ EDFNS;NB ⋅ UWNS;NB Þ⋅ SQ ⋅ nf

(4)

Since structural and non-structural debris is co-mingled during the inundation process, the total debris weight (TDW) of a single building is given as: (5)

TDW ¼ DWS þ DWNS

For the application to advection model, we can re-group TDW as the total non-buoyant weight of debris (TDWNB) and total buoyant weight of debris (TDWB) from Eqs. (3) and (4) as follow

(2)

PNS ðiÞ⋅DFNS ðiÞ

�

i

where, the subscript, ‘NS’ indicates non-structural damage fraction. Tables 1 and 2 summarize the debris fraction (DF) by weight for the three major building types (W1, W2, and RC1) where W1 and W2 are for wood structures less than and greater than 5000 ft2 (464.75 m2) and RC1 are for reinforced concrete buildings with a moment frame. For advec­ tion modeling, each DFS and DFNS are separated into two groups: Group A for the buoyant debris fraction from structural damage (DFS, B) and non-structural damage (DFNS, B), and Group B for the non-buoyant debris fraction from structural damage (DFS, NB) and non-structural damage (DFNS, NB). In this study, we classified Group A as wood construction debris, and Group B as reinforced concrete, steel, or unreinforced ma­ sonry debris. Table 1 lists DFS,B and DFS at four damage states, and Table 2 shows DFNS,B and DFNS,NB at four damage states. To estimate the actual weight of debris from effective debris fraction (EDF), we utilize the given unit weight (ton) of buildings per 1000 ft2 (92.95 m2) of floor area for each type of building (UW), which are also tabulated in HazusMH (2003). We also applied the number of floors (nf) of each building to account for the total volume. There is two types of UW for structural damage (UWS) and non-structural damage (UWNS), corresponding to buoyant debris and non-buoyant debris as shown in Table 3. With the given information of the square footage of buildings in thousands of square feet (SQ) and number of stories (nf) for each building, we can quantify the weight (ton) of structural debris (DWS) and non-structural debris (DWNS) for each building as follows � (3) DWS ¼ ðEDFS;B ⋅ UWS;B þ EDFS;NB ⋅ UWS;NB Þ⋅ SQ ⋅ nf

TDWNB ¼ EDFS;NB ⋅SQ⋅nf ⋅UWS;NB þ DFNS;NB ⋅SQ⋅nf ⋅UWNS;NB

(6)

TDWB ¼ EDFS;B ⋅SQ⋅nf ⋅UWS;B þ DFNS;B ⋅SQ⋅nf ⋅UWNS;B

(7)

The total debris volume (TDV) of a single building is calculated from the given mean density of buoyant debris (ρB ) and non- buoyant debris (ρNB ) (8)

TDVB ¼ TDWB = ρB

(9)

TDVNB ¼ TDWNB = ρNB 3

Here, we set ρB ¼ 0.45 ton/m (density of Canadian Spruce), and ρNB ¼ 1.91 ton/m3 (the average density of concrete and brick) as default density in the model and to have the final output in SI units. The sum is given as (10)

TDV ¼ TDVB þ TDVNB 3

Fig. 6 shows the spatial distribution of volume (m ) of debris per unit area due to 1000 year event, without advection modeling. The total volume of debris (TDV) per each building is calculated using Eqs. 1 to 10, and a kernel density estimation (KDE) in Arc-GIS toolbox is utilized to generate a contour map of debris volume per unit area. We set the searching radius of the KDE as 75 m, and the unit area set as a hectare (ha, 100 m by 100 m) for the contour map. Fig. 6a shows the total vol­ ume of debris including both buoyant and non-buoyant debris, and 6b shows only the volume of buoyant debris. Fig. 6a and b shows a similar distribution of debris overall with a relatively larger volume of debris on the coastline, and smaller volume of debris located further inland re­ gion. This decrease in debris volume occurs for two reasons: first, the

Table 1 Structural element fraction of non-buoyant and buoyant debris fraction in percent of weight. Building Type W1 W2 RC1

DFS,B (%)

DFS,NB (%)

Slight (i ¼ 1)

Moderate (i ¼ 2)

Extensive (i ¼ 3)

Complete (i ¼ 4)

Slight (i ¼ 1)

Moderate (i ¼ 2)

Extensive (i ¼ 3)

Complete (i ¼ 4)

0.0 0.0 0.0

5.0 6.0 0.0

34.0 33.0 0.0

100.0 100.0 100.0

0.0 0.0 0.0

3.0 2.0 5.0

27.0 25.0 33.0

100.0 100.0 100.0

5

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Table 2 Non-structural element fraction of non-buoyant and buoyant debris fraction in percent of weight. Building Type W1 W2 RC1

DFNS,B (%)

DFNS,NB (%)

Slight (i ¼ 1)

Moderate (i ¼ 2)

Extensive (i ¼ 3)

Complete (i ¼ 4)

Slight (i ¼ 1)

Moderate (i ¼ 2)

Extensive (i ¼ 3)

Complete (i ¼ 4)

2.0 2.0 1.0

8.0 10.0 7.0

35.0 40.0 35.0

100.0 100.0 100.0

0.0 0.0 0.1

0.0 10.0 8.0

0.0 28.0 28.0

100.0 100.0 100.0

damaged at the 2 500 year event. We can also observe that the dominant hazard changes from earthquake (red) to tsunami (blue) and back again as the return period increases. It is caused by variations in the earth­ quake and tsunami hazards with recurrence interval and the suscepti­ bility of the buildings to these hazards. In other words, a significant increase of tsunami induced debris is observed from the 500 year–2 500 year event, while the area damaged by the tsunami is limited to the maximum extension of tsunami, which is quite similar for 2500 and 10,000 year event due to the relatively high ground elevation further inland. Fig. 7b for the buoyant debris shows a quite similar pattern to 7a, but overall there is a larger volume of buoyant debris generated from the tsunami than from the earthquake for the 500 and 1000 year events. The main reason of this observation is that a wood structure has a signifi­ cantly larger unit weight of buoyant debris compared to reinforced concrete buildings and because wood structures are more vulnerable to the tsunami and vice versa for a reinforced concrete building. This is P P clearly shown in Fig. 7c where the ratio of R TDV B = R TDV for tsunami (blue square) is always higher than that for the earthquake (red triangle) across all return periods. The combined earthquake and tsunami ratio (black circle) is located somewhat in the middle of tsunami and earth­ quake debris. The ratio of the combined earthquake and tsunami is initially about 50% and increases to 65% beyond the 1000 year event.

Table 3 Unit weight (tons per 1000 ft2) for structural and non-structural elements for building types. Building Type

Structural Buoyant UWS,B

Nonbuoyant UWS,NB

Non-structural Buoyant UWNS,B

Nonbuoyant UWNS,NB

W1 W2 RC1

6.5 4.0 0.0

15.0 15.0 98.0

12.1 8.1 5.3

0.0 1.0 4.0

SQ Square footage of building in 1 000 ft2 1.5 2.5 3.0

building stock has a higher density closer to the shoreline, and second, the hazard intensity for the tsunami is greater near the shoreline. The volume of buoyant debris (6b) is smaller than the total volume of debris (6a), but the overall pattern of higher volume closer to the shoreline is similar. One exception is that there a relatively small amount of buoyant debris observed at the center of Seaside (red oval in Fig. 6b) where the buildings are predominantly reinforced concrete which is classified as RC1 in Fig. 4b. Fig. 7 shows the quantity of total debris as a function of return pe­ riods (T) for the 250, 500, 1,000, 2,500, and 10,000 events for earth­ quake (red triangle), tsunami (blue rectangle) and combined earthquake and tsunami (black circle). Fig. 7a, b, c shows the total sum of TDV for P P Seaside ( R TDV), the buoyant debris only ( R TDV B ), and the ratio of P P the buoyant debris to the total debris ( R TDV B = R TDV), respectively at the study region (dashed box in Fig. 6a). Not surprisingly, Fig. 7a P shows that the total sum of TDV ( R TDV) for the combined earthquake and tsunami (black circle) increases as the return period increase. However, the rate of increase is not constant: it increases sharply for relatively small return periods (<500 yr), and then more slowly as the return period increases from 2500 year to 10,000 year event. It is because most of the buildings in the study regions have already severely

5. Debris advection The total debris estimation in the previous section tells only a part of the story because the buoyant debris (Fig. 6b) can be mobilized easily by the tsunami flow. This section provides the methodology for debris advection described in the lower right box Part C in Fig. 1. We apply a Lagrangian (particle tracking) model using the time-dependent tsunami inundation originally used to construct the PTHA (Park and Cox, 2016). We assume that each buoyant debris follows the fluid particle, ignoring any interactions between particles, between particle and structures, and

Fig. 6. Expected volume (m3) of debris per unit area (hectare) for 1000 year event considering both earthquake and tsunami damage to buildings without advection: (a) total debris, (b) buoyant debris only. 6

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�

1 if hðtÞ � hc �0 if hðtÞ < hc 1 if VðtÞ � Vc δðVðtÞÞ ¼ 0 if VðtÞ < Vc

δðhðtÞÞ ¼

(12)

Here, X is the position vector of each debris particle, U is the velocity vector, obtained from the numerical inundation model, and δ is the Dirac delta function representing the thresholds for debris movement. At time t, each h(t) and V(t) is scalar flow depth and flow speed, respectively. Correspondingly, hc and Vc are the critical thresholds for the size class of debris. Table 4 summarizes the critical threshold values of flow depth and flow speed for this study. We assumed the values of 3.0, 1.0 and 0.5 m for the three size classes and then related the critical velocity using a Froude number criteria (Fradv) such as (13)

Vc ¼ Fradv ðghc Þ1=2

Here, we set Fradv ¼ 0.1 as a default model setup and g is the gravitational acceleration. The choice of Fradv ¼ 0.1 was used to relate the tsunami flow depth and velocity during inundation in a meaningful way. For example, the relationship between the maximum flow depth and flow speed for tsunami inundation were evaluated numerically by the author’s previous work (Park et al., 2018). The value Fradv ¼ 0.1 gave a reasonable lower bound to the joint distribution of flow depth and speed for this region (see Fig. 8). More work is necessary to define the relationship among the general tsunami flow depth, flow velocity, and corresponding debris advection, but this provides a reasonable starting point. In reality, the debris would not be generated instantaneously. The total damage of a building could occur near the time of the maximum momentum flux when the maximum hydrodynamic force is loading on a building. However, there are lots of uncertainties to determine the initial moving time of construction debris. It could be variable depending on the building shape or materials and debris sizes. In this study, we assume that both earthquake and tsunami debris are generated instantaneously when an event occurs, and the advection will start when the leading edge of tsunami wave satisfies one of the threshold values (hc or Vc) which pre-determined in Table 4 for the model simplicity. Fig. 8 shows the spatial distribution of the debris advection at 5 times during the inundation with three different size conditions for the 1000 year event. Each dot in the figure indicates a single debris particle generated from the damage assessment and quantified through Eqs. (1)– (10) for each building. It is noted that the dots only indicate the position but do not reflect the total weight or volume. The first row (Fig. 8a0 to 8a4), second row (Fig. 8b0 to 8b4), third row (Fig. 8c0 to 8c4) show the advection model results of large, medium, and small debris, respectively. The first column (Fig. 8a0, b0, c0) are for the debris at t ¼ 35 min, when the leading edge of tsunami wave just approaches to the coastline, and the second column (Fig. 8a1, b1, c1) is at t ¼ 40.0 min, when the leading edge of tsunami reaches the Necanicum River. The third column (Fig. 8a2, b2, c2) is at t ¼ 45.0 min, when the leading edge of tsunami reaches Neawanna Creek. The fourth column (Fig. 8a3, b3, c3) is at t ¼ 54.9 min and we can observe initial return flow from the tsunami inundation. The fifth column (Fig. 8a4, b4, c4) is at t ¼ 120.3 min, after the tsunami inundation has quiesced. For the large size class (top panels), only the debris located near the coast are advected inland and most of the debris located landward of the Necanicum River remain at their initial position. Some of the large

Fig. 7. Sum of TDV over the study region at different recurrence time (year) per each hazard sources (EQ þ TSU, EQ, and TSU). (a) A total volume of debris (buoyant and non-buoyant debris) (b) buoyant debris, (c) percent of the volume of buoyant debris from total debris. Each symbol and color indicates the debris induced from the combined earthquake and tsunami (black circle), earthquake only (red triangle), and tsunami only (blue rectangle). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

with the flow itself. Although these are gross simplifications of the problem and may not be applicable to extremely large debris, we include two realistic threshold conditions that can affect the movement of debris, namely the minimum flow depth and the minimum flow speed. If either flow depth or flow speed is less than the thresholds, the particle does not move along the water particle. In general, these thresholds would be different depending on the characteristics of debris, such as the materials (density), size, and shape. For simplicity, we consider three different sizes of debris (large, medium, and small) and three sets of depth and velocity thresholds corresponding to each debris size. We assume that large and medium debris is generated from the structural buoyant debris (DWS) and that the small debris is generated from the non-structural buoyant debris (DWNS). We also assume that each size has an equal amount of debris, so we set the weight of each size of debris has equally distributed (TDWB/3). Again, these simplifying assumptions are made to demonstrate the methodology. Any number of classes could be used, and different distributions could be assigned to each size class. Furthermore, a probabilistic approach could be used where the thresh­ olds are assigned a mean and variance so that there is a statistical variation in threshold applied to debris generated at each parcel, and we applied constant thresholds in this study for simplicity. With the known location and velocity vector of each particle at a certain time t, the trajectory of the particle after a unit time increment (Δt) is calculated according to Xðt þ ΔtÞ ¼ XðtÞ þ UðX; tÞδðhðtÞÞδðVðtÞÞΔt

Table 4 Threshold values of h and V used in this study.

(11)

7

Debris size

hc(m)

Vc (m/s)

Large Medium Small

3.0 1.0 0.5

0.54 0.31 0.22

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Coastal Engineering 153 (2019) 103541

Fig. 8. Spatial distribution of the debris advection at 5 specific times for large (red) debris (a0 to a4), medium (blue) debris (b0 to b4), and small (black) debris (c0 to c4) at 1000 yr event. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9. Expected volume (m3) of debris per unit area (hectare) for 1000 year event considering advection: (a) total debris, (b) buoyant debris only.

debris is observed to flow out to through the river during drawback of the flow (Fig. 8a3, a4). For the middle size class (middle panels), debris cross over the Necanicum River and are deposited between the

Necanicum River and Neawanna Creek. For the small size class (bottom panels), most of the debris is advected to the maximum tsunami inun­ dation line (Fig. 8c2), which is the edge of just cross the Neawanna Creek 8

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Coastal Engineering 153 (2019) 103541

Fig. 10. Detailed contour map of total debris volume including both buoyant debris and non-buoyant debris with critical structure and transportation network at Seaside, Oregon, for 1000 year-event: (a) before the advection, (b) after advection.

at this 1000 year event. During the return tsunami flow (Fig. 8c3, c4), some of the debris flows out through the river, but most of the debris is concentrated in specific locations near the maximum inundation (debris hot spots) due to the uneven topography. We can quantify the volume of debris which is transferred through the advection model by plotting the total volume of debris per unit area (hectare) using KDE as shown in Fig. 6. Fig. 9a shows the total volume of debris by adding both advected debris and non-buoyant debris. Fig. 9b shows the volume of advected buoyant debris only. Comparing 6a and 6b (without advection model) with 9a and 9b (with advection model), it can be seen that near the shoreline, there is a significant decrease in debris, consistent with observations of tsunami patterns where wood structures are essentially ‘washed away’. The pattern of debris distri­ butions further inland near the maximum extension of a tsunami (solid red line) is also consistent with field observations and show ‘hot spots’ due to the convergence of debris in certain areas due to local topo­ graphic effects and changes to the tsunami speed. However, there is no spatial variation of debris beyond the inundation zone (landward of the red line) in figures (6a, 9a, and 6b, 9b).

Fig. 4a. Although a complete analysis of the functional losses during this event is beyond the scope of this work (e.g., Kameshwar et al., submit­ ted), it can be assumed that the proximity of debris to these critical fa­ cilities and components of the transportation infrastructure would impact initial rescue and recovery processes. Fig. 11a, b, c shows the along-shore total volume of debris without (black solid) and with advection (red dashed) along the three vertical routes AA0 , BB0 , and CC0 , respectively. Fig. 11d, shows the location of major critical facilities on the three major routes. On route AA’, almost a half of the debris by volume is reduced along this route due to the tsunami advection. The remaining volume of debris is mostly nonbuoyant debris as we observed at Fig. 9b. The spike in Fig. 11a at y ¼ 1500 m is because the area is the commercial region of Seaside composed of mostly RC buildings. Inversely at Fig. 11b and c, the advection increases debris on both routes BB0 and CC0 , decreasing the accessibility especially to the elec­ trical substation and police station along route BB0 and the assembly areas, a nursing house, a school, and a hospital along route CC’. Even if there is no reduction in the functionality of structures from the seismic and tsunami hazard directly, the limited accessibility due to debris advection could result in loss of functionality for those facilities after the tsunami event. Interestingly, the increased volumes of debris are not uniform along BB0 and CC0 even though the debris decreased somewhat uniformly along AA’. This highlights the need for site-specific studies with detailed flow models that can account for bathymetric and topo­ graphic conditions. Fig. 12a and b shows the volume of the total debris at route AA0 without and with advection for the five different recurrence intervals, respectively. As expected, the total volume of debris without advection increases as the return period increases up to the 1000 year event. Beyond this, we observe little to no significant increases of debris (except at y ¼ 1500 m), implying that most of the buildings were fully damaged at 1000 year under combined earthquake and tsunami forces along AA’. As mentioned earlier, the city center is located at y ¼ 1500 m and is composes of RC buildings. Applying the advection model (Fig. 12b), the overall volume of debris is generally observed to decrease

6. Loss of functionality In this section, we evaluate the potential negative consequences of debris on the critical facilities and the transportation network. Fig. 10a and b provide details of the volume of total construction debris, gener­ ated from 1000 year event, without advection and with advection, respectively. In Fig. 10a, there are three dotted lines denoting route AA0 , BB0 , and CC0 to highlight three major vertical routes in Seaside. Route AA0 and BB0 are comprised of local city streets, and route CC0 is a local highway and is connected to other cities along the coast. We designate these as ‘vertical routes’ in the ‘along-shore’ direction. To show the horizontal distribution of the volume of debris from the coast to inland areas, we draw the white solid lines in Fig. 10b, which pass horizontally across the community and refer these as the ‘cross-shore’ direction. Fig. 10 also shows several critical facilities, including evacuation assembly areas, fire station, hospital, school and so on as indicated in 9

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Coastal Engineering 153 (2019) 103541

Fig. 11. Along-shore total volumes of debris without (solid) and with advection (red dash) along three vertical routes, AA0 (a), BB0 (b), and CC0 (c), and the location sketch of critical facilities in Seaside, OR along three vertical routes (d). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 12. Alongshore distribution of total volume of debris at AA0 for five return periods. (a) without and (b) with advection over different return periods.

when compared with Fig. 12a, except for the 250 year event because of the relatively weak tsunami flow condition at this return period. Fig. 13a and b shows the results at route BB0 without and with advection for the 5 intervals, respectively. Similar to the previous figure, Fig. 13a shows an increase in debris volume with increasing return period. After advection, Fig. 13b shows a large change of debris volume, especially for the 1000- and 2500 year events. Both of these events produce hot spots of debris at different locations. For example, we can observe the hot spots around at y ¼ 1000 m and y ¼ 2100 m for 1000 year event and at 100 < y < 500 m, 700 < y < 1400 m and y ¼ 1900 m for the 2500 year event. Interestingly, the volume of debris for the 10,000 year event shows the much smaller volume of debris than the previous two because much of the debris is advected beyond BB’, including the debris moving from the coast to the landward.

Fig. 14a and b shows the results at route CC0 without and with advection respectively. As we observed at the two previous routes, the debris volume without advection (Fig. 13a) increases as the return period increases. However, there are large increases of debris over the route CC0 with advection, especially at several specific hot spots (Fig. 14b). Note here that the scale of Fig. 14b is 10 times larger than Fig. 14a. Similar to Fig. 13b, the largest hotspot is found for the 2500 yr event, and there are different hotspots for different return periods, highlighting the complexity of this problem, even with the relatively simple assumptions used to construct the debris model. Moreover, there is a relatively smaller volume of debris from the 10,000 yr event compared to the 1000 and 2500 yr events because the intensity of the inundation of the 10,000 yr event displaced debris beyond route CC’. Lastly, the average volume of debris across the horizontal 10

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Coastal Engineering 153 (2019) 103541

Fig. 13. Cross-distribution of debris at the route BB0 without advection (a) and with advection (b) over different return periods.

Fig. 14. Cross-distribution at CC0 cross-profile without advection (a) and with advection (b) over different return periods.

transactions is presented in Fig. 15. We estimate each volume of debris over the 28 transactions described in Fig. 10b (white lines), and plot the averaged volume of debris for all transects for the five return periods without (15a) and with (15b) advection. The regions filled with gray color indicate the range of the distances of the three routes from the coastline and each reverse triangle in Fig. 15b presents the average extent of the maximum inundation for each recurrence interval. We note that there is essentially no inundation for 250 yr event, only minor flooding near the Necanicum River (x ¼ 500 m) and Neawanna Creek (x ¼ 1300 m). Fig. 15a shows that without advection the average cross-shore profile of debris generally decreases further from the coast and that the amount

of debris increases somewhat uniformly with recurrence interval as seen in Figs. 12a, 13a and 14a. Note that the relatively small amount of debris at x ¼ 500 m is due to the Necanicum River. Fig. 15b shows a dramatic change in the cross-shore debris distribution due to the advection. In general, there is a correlation between the location of the peak of the debris volume and the average maximum extent of inundation for the larger recurrence interval, particularly for the 10,000 year event. Initially, the debris is transferred from the offshore to the inland following initial peak tsunami wave, but debris could transfer various directions during return tsunami flow when the gravity and local topographic conditions are governing in the flow system. (e.g. river streams). At Seaside, the relatively flat topographic condition allows the 11

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Coastal Engineering 153 (2019) 103541

Fig. 15. Averaged distribution of the horizontal cross-profiles (white line in Fig. 10b) without and with advection over different occurrence years.

debris initially move perpendicular to the shoreline along with the initial flow direction, but after the peak tsunami flow, debris starts to flow out the alongshore direction, especially near the two river stream, Necanicum river, and Newanna creek. The maximum moving distance of debris to the inland is depending on the intensity of tsunami inundation, while debris is also transferred and collected at the specific area and create several debris hot spots. Those hot spots are mostly found near the edge of maximum inundation boundary, but their intensities and exact locations are complex because they depend on several factors including initial debris location, size, local topography, and intensity of tsunami inundation.

2. The spatial distribution of debris can be vastly different when considering the effects of advection due to the tsunami inundation. A relatively simple particle advection model without interaction terms using reasonable thresholds for motion can give a qualitative description of the debris field consistent with the general pattern of observed tsunami debris. 3. The debris advection model can create “debris hot spots” located near the maximum extent of tsunami inundation. Details of the debris hotspot depend on several factors, including debris origin, topography, and inundation dynamics. 4. The total volume of debris increases with recurrence intervals. However, the rate of increase is not uniform and is dependent on details of the hazard type (earthquake, tsunami), a building type (wood, concrete), and location (proximity to shoreline in the case of tsunami). 5. The recurrence interval (return period) will influence the spatial distribution of debris, and this influence is not uniform. For example, the “worst case” debris field for a particular part of the study area is not necessarily due to the most extreme (highest recurrence interval) event. 6. The advected debris field can impact portions of the transportation infrastructure and limit access to critical facilities, even when the facilities are located outside of the inundation zone and are not affected by the earthquake. This can lead to functional loss of the facility even if there is no structural damage.

7. Conclusions This paper presents a framework for debris forecasting and advection of debris for multi-hazards from earthquake and tsunami event. We apply this framework to the coastal community, Seaside, Oregon. We quantify the potential volume (weight) of structural and non-structural debris from tsunamigenic earthquake events at Cascadia Subduction Zone based on the combined damage analysis results of PSTDA, and evaluate the final distribution of construction debris using an advection model. We adapt a Lagrangian approach for the advection model and track the motions of single debris particle during tsunami inundation and evaluate both their trajectories and final volume distributions for different return period events. To the authors’ knowledge, this is the first attempt to quantify and track the construction debris due to tsunami inundation at a community scale. The main conclusions from this work are:

8. Discussion This study provides the preliminary framework to quantify the vol­ ume of debris from tsunamigenic earthquake event over the community and evaluate the pattern of final debris distribution considering different recurrence intervals. The current study could be helpful to determine the initial site for the debris collection or the locations for debris disposal (Brandon et al., 2011) for the debris management after natural disasters. However, the current approach has several limitations on both

1. The results of debris advection clearly show the potential negative impacts of advected debris on the community (e.g., transportation network system), and it highlights that the immediate necessity to consider debris advection impact on the recovery plan to improve the resilience of community from natural disasters.

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methodologies for the debris quantification and advection. We quantify the weight of debris per each building based on the assumption that the weight of debris linearly increases as a function of damage ratio following the Hazus approach. It is a rational approach but still needs to be validated through field survey. Regarding the debris advection (transport) model, we assume that the buoyant debris follows the water particle trajectory and that the nonbuoyant debris does not move at all. This assumption does not account for the possible rolling and shifting of non-buoyant debris as has been observed in the field (e.g., Chau and Bao, 2010). Non-buoyant debris could be applicable in the advection model using relatively higher threshold values or other concepts which are applied in the sediment transport process (e.g. shielding parameter). However, in this study, we limited the buoyant debris only for the model simplicity, and leave non-buoyant debris displacement as future work. In addition, we utilized a Lagrangian approach with the given hydrokinematic condition under the two thresholds, minimum flow depth and velocity conditions depending on the size of debris, which is defined based on engineering judgment for this study. The threshold values of each debris class should be validated and generalized experimentally or numerically, and also a sensitivity test of threshold should be conducted near future. As mentioned previously, the thresholds could be improved using a statistical approach with a mean and variance applied at each parcel. Furthermore, quantification of debris hot spots and quantified contributions of each factor to the debris hot spots would be important to understand and manage the negative debris advection impacts on the community. Our study only includes the debris generated by damaged structures, but we excluded other possible sources of debris from natural environ­ ment (vegetation, boulder, rock, gravel, sand, mud, etc.) and other damaged infrastructure systems (vehicles, vessels, cracked roads, elec­ tricity poles, etc.). Those different type of debris may need a different quantification methodology or different classification rule to be applied to the advection model. The current advection model does not include the interaction among the debris, or between debris and existing structure and possible collision of debris during inundation. The impact of debris on the structure could cause significant damages on the builtenvironments during tsunami inundation process, thus the trace of quantified debris could be utilized in the damage analysis. Also, the currently hydro-kinematic conditions, which is provided from the nu­ merical tsunami modeling did not consider any macro-roughness effects which could complicate the flow field. Lastly, the final outputs of this study are only focused on to show the spatial quantification and distributions of advected debris over the major road system in the community near the critical infrastructure buildings. This work could be extended for the analysis of debris man­ agement processes considering the accessibility of emergency rescue and initial recovery efforts.

References Abbe, T.B., Montgomery, D.R., 1996. Large woody debris jams, channel hydraulics and habitat formation in large rivers. Regul. Rivers Res. Manag. 12 (2–3), 201–221. Alam, M.S., Barbosa, A.R., Scott, M.H., Cox, D.T., van de Lindt, J.W., 2017. Development of physics-based tsunami fragility functions considering structural member failures. J. Struct. Eng. 144 (3), 04017221. Ardianti, A., Mutsuda, H., Kawawaki, K., Doi, Y., 2018. Fluid structure interactions between floating debris and tsunami shelter with elastic mooring caused by run-up tsunami. Coast Eng. 137, 120–132. Benjamin, J.R., Cornell, C.A., 1970. Probability, Statistics, and Decision for Civil Engineers. McGraw-Hill, New York. Brandon, D.L., Medina, V.F., Morrow, A.B., 2011. A case history Study of the recycling efforts from the United States army corps of engineers hurricane katrina debris removal mission in Mississippi. Mississippi. Adv. Civ. Eng. vol.2011. Channell, M., Graves, M.R., Medina, V.F., Morrow, A.B., Brandon, D., Nestler, C.C., 2009. Enhanced Tools and Techniques to Support Debris Management in Disaster Response Missions (Flood and Coastal Storm Damage Reduction Research and Development Program) (No. ERDC/EL-TR-09-12). Chau, K.T., Bao, J.Q., 2010. Hydrodynamic analysis of boulder transportation on phi-phi island during the 2004 Indian Ocean tsunami. In: The Fifth International Conference on Earthquake Engineering 5ICEE, vol. 2. Tokyo Institute of Technology, Japan, pp. 1693–1699, 3-5 March 2010. Como, A., Mahmoud, H., 2013. Numerical evaluation of tsunami debris impact loading on wooden structural walls. Eng. Struct. 56, 1249–1261. De Risi, R., Goda, K., 2016. Probabilistic earthquake–tsunami multi-hazard analysis: application to the Tohoku Region, Japan. Front. Build. Environ. 2, 25. Federal Emergency Management Agency [FEMA], 2011. earthquake model Hazus-MH 2.1 technical manual. FEMA, Mitigation DivisionUnites States, Washington, DC. Ghobarah, A., Saatcioglu, M., Nistor, I., 2006. The impact of the 26 December 2004 earthquake and tsunami on structures and infrastructure. Eng. Struct. 28 (2), 312–326. Gonz� alez, F.I., Geist, E.L., Jaffe, B., K^ ano� glu, U., Mofjeld, H., Synolakis, C.E., Titov, V.V., Arcas, D., Bellomo, D., Carlton, D., Horning, T., 2009. Probabilistic tsunami hazard assessment at seaside, Oregon, for near-and far-field seismic sources. J. Geophys. Res.: Oceans 114 (C11). Goseberg, N., Stolle, J., Nistor, I., Shibayama, T., 2016. Experimental analysis of debris motion due the obstruction from fixed obstacles in tsunami-like flow conditions. Coast Eng. 118, 35–49. Haehnel, R.B., Daly, S.F., 2004. Maximum impact force of woody debris on floodplain structures. J. Hydraul. Eng. 130 (2), 112–120. Herbin, A.H., Barbato, M., 2012. Fragility curves for building envelope components subject to windborne debris impact. J. Wind Eng. Ind. Aerodyn. 107, 285–298. Kameshwar, S., Cox, D.T., Barbosa, A.R., Farokhnia, K., Park, H., Alam, M., and van de Lindt, J. Probabilistic Decision-Support Framework for Community Resilience: Incorporating Multi-Hazards, Infrastructure Interdependencies, and Resilience Goals in a Bayesian Network. Reliability Engineering & System Safety, (Submitted). Ko, H.S., Cox, D.T., Riggs, H.R., Naito, C.J., 2014. Hydraulic experiments on impact forces from tsunami-driven debris. J. Waterw. Port, Coast. Ocean Eng. 141 (3), 04014043. Lin, N., Vanmarcke, E., 2008. Windborne debris risk assessment. Probabilistic Eng. Mech. 23 (4), 523–530. Mori, N., Goda, K., Cox, D., 2018. Recent process in probabilistic tsunami hazard analysis (PTHA) for mega thrust subduction earthquakes. In: Santiago-Fandi~ no, V., Sato, S., Maki, N., Iuchi, K. (Eds.), The 2011 Japan Earthquake and Tsunami: Reconstruction and Restoration. Advances in Natural and Technological Hazards Research, vol. 47. Springer, Cham. Naito, C., Cercone, C., Riggs, H.R., Cox, D., 2013. Procedure for site assessment of the potential for tsunami debris impact. J. Waterw. Port, Coast. Ocean Eng. 140 (2), 223–232. Park, H., Cox, D.T., 2016. Probabilistic assessment of near-field tsunami hazards: inundation depth, velocity, momentum flux, arrival time, and duration applied to Seaside, Oregon. Coast Eng. 117, 79–96. Park, H., Cox, D.T., Lynett, P.J., Wiebe, D.M., Shin, S., 2013. Tsunami inundation modeling in constructed environments: a physical and numerical comparison of freesurface elevation, velocity, and momentum flux. Coast Eng. 79, 9–21. Park, H., Cox, D.T., Barbosa, A.R., 2017. Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis. Coast Eng. 122, 10–26. Park, H., Cox, D.T., Alam, M.S., Barbosa, A.R., 2017. Probabilistic seismic and tsunami hazard analysis conditioned on a megathrust rupture of the Cascadia subduction zone. Front. Build. Environ. 3, 32. Park, H., Cox, D.T., Barbosa, A.R., 2018. Probabilistic Tsunami Hazard Assessment (PTHA) for resilience assessment of a coastal community. Nat. Hazards 94 (3), 1117–1139. Park, H., Alam, M.S., Cox, D.T., Barbosa, A.R., van de Lindt, J.W., 2019. Probabilistic seismic and tsunami damage analysis (PSTDA) of the Cascadia Subduction Zone applied to Seaside, Oregon. Int. J. Disaster Risk Reduct. 101076. Prasetya, G., Black, K., de Lange, W., Borrero, J., Healy, T., 2012. Debris dispersal modeling for the great Sumatra Tsunamis on Banda Aceh and surrounding waters. Nat. Hazards 60 (3), 1167–1188. Riggs, H.R., Cox, D.T., Naito, C.J., Kobayashi, M.H., Aghl, P.P., Ko, H.S., Khowitar, E., 2013. Water-driven debris impact forces on structures: experimental and theoretical program. In: ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers (pp. V001T01A059V001T01A059).

Acknowledgment Funding for this study was provided as part of the cooperative agreement 70NAB15H044 between the National Institute of Standards and Technology and Colorado State University. This material is based upon work supported by the National Science Foundation under grant 1661315. The content expressed in this paper are the views of the au­ thors and do not necessarily represent the views of NIST, the U.S. Department of Commerce, or the National Science Foundation. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.coastaleng.2019.103541.

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Stolle, J., Nistor, I., Goseberg, N., Mikami, T., Shibayama, T., 2017. Entrainment and transport dynamics of shipping containers in extreme hydrodynamic conditions. Coast Eng. J. 59 (03), 1750011. Tanikawa, H., Managi, S., Lwin, C.M., 2014. Estimates of lost material stock of buildings and roads due to the Great East Japan Earthquake and tsunami. J. Ind. Ecol. 18 (3), 421–431. Tsunami Pilot Study Working Group, 2006. Seaside, Oregon, Tsunami Pilot StudyModernization of FEMA Flood Hazard Maps. Joint NOAA/USGS/FEMA Special Report 94. Wang, H., Mostafizi, A., Cramer, L.A., Cox, D., Park, H., 2016. An agent-based model of a multimodal near-field tsunami evacuation: decision-making and life safety. Transp. Res. C Emerg. Technol. 64, 86–100.

Wiebe, D.M., Cox, D.T., 2014. Application of fragility curves to estimate building damage and economic loss at a community scale: a case study of Seaside, Oregon. Nat. Hazards 71 (3), 2043–2061. Wood, N., 2007. Variations in City Exposure and Sensitivity to Tsunami Hazards in Oregon (No. 2007-5283). Geological Survey (US). Yao, Y., Huang, Z., Lo, E.Y., 2018. Experimental study on floating debris generated by solitary waves running up a composite beach. J. Coast. Res. 85 (sp1), 851–855. Yeh, H., Barbosa, A., Mason, B.H., 2014. Tsunamis effects in man-made environment. Encycl. Complex. Syst. Sci. 1–27. Yuepeng, C., Liang, D., Song, L., 2016. Simplified method for evaluating the impact of a transportation network on post hurricane access to healthcare facilities. J. Perform. Constr. Facil. 30 (1), 04014182.

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