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Nat. Hazards Earth Syst. Sci., 21, 1887–1908, 2021
https://doi.org/10.5194/nhess-21-1887-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Tsunami damage to ports: cataloguing damage to create
fragility functions from the 2011 Tohoku event
Constance Ting Chua1,2, Adam D. Switzer1,2, Anawat Suppasri3, Linlin Li4, Kwanchai Pakoksung3,
David Lallemant1,2, Susanna F. Jenkins1,2, Ingrid Charvet5, Terence Chua1, Amanda Cheong6, and Nigel Winspear7
1Asian School of the Environment, Nanyang Technological University, Singapore
2Earth Observatory of Singapore, Nanyang Technological University, Singapore
3International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
4School of Earth Sciences and Engineering, Sun Yat-sen University, Guangzhou, China
5Formerly Department of Statistical Science, University College London, London, United Kingdom
6JBA Risk Management Pte Ltd, Singapore
7Formerly SCOR Global P & C, Singapore
Correspondence: Constance Ting Chua (cchua020@e.ntu.edu.sg)
Received: 26 October 2020 – Discussion started: 28 October 2020
Revised: 21 April 2021 – Accepted: 19 May 2021 – Published: 17 June 2021
Abstract. Modern tsunami events have highlighted the vul-
nerability of port structures to these high-impact but in-
frequent occurrences. However, port planning rarely in-
cludes adaptation measures to address tsunami hazards. The
2011 Tohoku tsunami presented us with an opportunity to
characterise the vulnerability of port industries to tsunami
impacts. Here, we provide a spatial assessment and photo-
graphic interpretation of freely available data sources. Ap-
proximately 5000 port structures were assessed for damage
and stored in a database. Using the newly developed dam-
age database, tsunami damage is quantified statistically for
the first time, through the development of damage fragility
functions for eight common port industries. In contrast to
tsunami damage fragility functions produced for buildings
from an existing damage database, our fragility functions
showed higher prediction accuracies (up to 75 % accuracy).
Pre-tsunami earthquake damage was also assessed in this
study and was found to influence overall damage assess-
ment. The damage database and fragility functions for port
industries can inform structural improvements and mitigation
plans for ports against future events.
1 Introduction
Port assets are vulnerable to the physical damage caused
by tsunami and cascading effects such as extensive supply
chain disruption. For example, transoceanic waves from the
2004 Indian Ocean tsunami resulted in heavy damage to mar-
itime facilities across the Indian Ocean. On the west coast
of Banda Aceh, Indonesia, all harbours and landing piers
between Lhok Nga and Meulaboh were destroyed and un-
usable (Janssen, 2005), and across the Indian Ocean, heavy
damage to maritime facilities reportedly resulted in the clo-
sure of Nagapattinam Port, India, for weeks (Mahshwari et
al., 2005). On the same note, the 2011 Tohoku (Great East
Japan) tsunami caused damage to many ports along the Pa-
cific coast in the Tohoku region. The affected ports suffered
from a contraction in export and import values following
the tsunami (March–May 2011) of 57.5 % and 61.6 % re-
spectively, relative to the preceding 5-year average for the
same period (Japan Maritime Centre, 2011). Total economic
losses for tsunami damage to Japan’s marine vessels, ports
and maritime facilities were approximated at USD 12 billion
(Muhari et al., 2015). A recent study speculated that earth-
quakes greater than Mw=8.5 from the Manila Trench could
result in the loss of functions in up to five major ports includ-
ing Kaohsiung and Hong Kong (Otake et al., 2019). Addi-
tionally, threats from future tsunami events are expected to be
Published by Copernicus Publications on behalf of the European Geosciences Union.
1888 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
exacerbated by rising sea levels (Li et al., 2018), which imply
greater risks for port assets located near tsunami sources.
With about 80 % of global trade volume carried by sea,
ports are critical nodes in international trade. Ports are also
home to industrial clusters and critical facilities such as man-
ufacturing firms and power plants due to the convenience
they provide. With increased seaborne trade, globalisation
of complex industrial processes and dependence on ports
for economic development, port areas are only expected to
develop further. However, port planning rarely accounts for
adaptation to natural hazards, and coastal protection struc-
tures are usually built to mitigate short-term hazard scenarios
such as coastal flooding and wave damage (Lam and Lassa,
2017).
Tsunami are high-impact events but infrequent occur-
rences, which makes their potential impacts to ports difficult
to quantify. The expected increase in the exposure of port
assets to coastal hazards, combined with our limited expe-
rience with tsunami in modern ports, demonstrates a clear
need to better understand how port structures might respond
to tsunami impacts.
Structural damage resulting from tsunami impacts has
generated considerable interest since the 2004 Indian Ocean
tsunami (e.g. Nistor et al., 2010; Leelawat et al., 2016; Song
et al., 2017; Suppasri et al., 2019). Structural damage is most
commonly quantified in the form of tsunami damage fragility
functions. Tsunami fragility functions express the probabil-
ity that a structure exceeds a prescribed damage threshold
for a given tsunami flow characteristic or intensity measure
(Koshimura et al., 2009). Early work in the development of
tsunami fragility functions has been largely focused on dam-
age to residential and commercial buildings (e.g. Leone et
al., 2011; Reese et al., 2011; Mas et al., 2012; Gokon et
al., 2014). In recent years, the study of tsunami structural
fragility has been extended to critical infrastructure such as
roads and bridges (Akiyama et al., 2013; Shoji and Naka-
mura, 2017; Williams et al., 2020).
Despite recent efforts, our understanding of tsunami im-
pacts on ports still falls short. The coverage of tsunami-
induced damage on port structures in existing literature is
by and large limited to qualitative assessments. To date
most studies on tsunami structural damage to ports are in
the form of post-tsunami surveys, which document damage
observations and describe the failure mechanisms of har-
bour elements such as breakwaters, quay walls and wharves
(e.g. Meneses and Arduino, 2011; Fraser et al., 2013; Haz-
arika et al., 2013; Paulik et al., 2019; Benzair et al., 2020),
and port facilities such as oil tanks, cranes and equipment
(e.g. Scawthorn et al., 2016; Percher et al., 2013; Sugano et
al., 2014). Some studies have attempted to reconstruct struc-
tural impacts to port facilities by evaluating design specifica-
tions of structures or examining specific tsunami behaviour
such as bore impact linked to structural damage (e.g. Nayak
et al., 2014; Kihara et al., 2015; Chen et al., 2016; Huang and
Chen, 2020). Though recent studies attempted to quantify
tsunami damage to port facilities, the focus of these stand-
alone studies is specific to certain port industries, namely
warehousing (Karafagka et al., 2018) and fishery industries
(Imai et al., 2019), and therefore does not provide a compre-
hensive view of the damage sustained by different port in-
dustries. While necessary for the improvement of structural
design, efforts so far are not adequate in quantifying tsunami
damage statistically.
This study serves as a starting point in characterising the
vulnerability of port industries to tsunami impacts, through
the assessment and quantification of structural response to
tsunami inundation depths. The objective of this study is
twofold – (i) to develop a tsunami damage database for port
structures impacted during the 2011 Tohoku tsunami and,
based on the damage database, (ii) to construct tsunami dam-
age fragility functions for port industries. The 2011 Tohoku
tsunami presents a unique opportunity to study tsunami dam-
age to port structures due to the extent and severity of damage
and the large ensemble of data collected post-tsunami (Ta-
ble 1). The combination of densely recorded tsunami flow
measurements, well-documented surveyed damage data and
high-quality photographic evidence available offers an un-
paralleled resource for this research.
In this paper, we develop the first tsunami damage
database for port industries and their related structures. We
also present the first sets of tsunami damage fragility mod-
els for common industries found in the port hinterland. We
describe the data sources and methods to develop this dam-
age database and demonstrate in detail how the damage
database addresses limitations found in past studies. Fragility
functions are constructed by reviewing and employing best
practices in the field. Unique to this work, we also eval-
uated the robustness of tsunami fragility functions against
the influence of pre-tsunami earthquake effects. We conclude
by highlighting some key application opportunities of this
dataset and providing recommendations for overcoming cur-
rent limitations found in this study.
2 Study site
The northeast coast of Japan, also known as the Tohoku
region, was severely impacted by the Tohoku tsunami on
11 March 2011 (Fig. 1). Port operations along the Pacific
coast in the Tohoku and eastern Kanto regions were disrupted
due to debris and severe damage to buildings, loading fa-
cilities, wharfs, fuel facilities and seawalls (Takano, 2011).
Damage patterns varied along the Tohoku coastline. The To-
hoku coastline is mainly coastal plains and ria coasts. Coastal
plains are extensive areas of low-lying flat terrain, while ria
coasts, formed by submergence of former river valleys, typi-
cally have limited flat terrain. Ria coasts are characterised by
narrow funnel-shaped coastal inlets bounded by steep slopes
such as mountains. In coastal plains, damage severity tran-
sitioned gradually with distance inland, decreasing as inun-
Nat. Hazards Earth Syst. Sci., 21, 1887–1908, 2021 https://doi.org/10.5194/nhess-21-1887-2021
C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1889
Figure 1. Six of the affected ports (circled dots) were selected in
this study due to similarities in their coastal morphologies – they
are located in coastal plains. Tsunami inundation heights were mea-
sured and collected by the Tohoku Earthquake Tsunami Joint Sur-
vey (TTJS, 2011) team. Inundation heights refer to the maximum
height of tsunami inundation above the mean sea level in Tokyo
Bay (the Tokyo Peil datum). The generalised 2011 fault-rupture area
(in light blue) was inferred from GPS data adapted from Ozawa et
al. (2011).
dation depths decrease with distance inland (De Risi et al.,
2017). In ria coasts, the spatial distribution of damage was
uneven because flow characteristics, i.e. velocity and hydro-
dynamic force, which influence damage severity, varied sig-
nificantly for different points at the same distance inland or
with similar inundation depths (Suppasri et al., 2013; De Risi
et al., 2017). This was due to the differences in local to-
pography (Tsuji et al., 2014). Coastal topography influences
tsunami behaviour on land and therefore influences tsunami
flow dynamics and inundation characteristics (Suppasri et
al., 2015). Previous studies have highlighted the importance
of separating the two types of coastlines when assessing
tsunami damage (Suppasri et al., 2013; Tsuji et al., 2014;
De Risi et al., 2017). This study focuses on ports located
in coastal plains, due to (i) the difficulty of accounting for
complexity of flow processes in ria coasts as well as (ii) sig-
nificantly less port activity found in the narrow strips of ria
coasts. Affected ports, namely Hachinohe, Kuji, Ishinomaki,
Sendai, Soma and Onahama, located in coastal plains were
selected as study sites for our damage assessment (Fig. 1).
3 Workflow and data sources
A goal of this study was to produce tsunami damage fragility
functions for industries commonly found in ports and their
hinterlands, such as chemical and energy-related industries.
The components required to derive fragility functions include
the explanatory variable (hazard intensity measure), the re-
sponse variable (damage data) and a statistical linking model
(Charvet et al., 2017). At present, a consolidated data source
for tsunami damage to port structures has yet to exist. This
data gap presents us with an opportunity to develop a dam-
age database for port structures and to use the damage data
for the construction of fragility functions. We developed a
framework (Fig. 2) for collecting and processing damage
data within a database and using a machine-learning work-
flow to evaluate those data and provide robust fragility func-
tions; more details on our approaches are provided over the
following subsections. We used freely available data where
possible to illustrate how our methods can also be reproduced
in other locations. A synopsis of the data used in this study
and their sources are presented in Table 1.
4 Data collection
4.1 Establishing a damage database
The port structures referred to in this study collectively
consist of a mixture of buildings and industry-related non-
building structures (henceforth referred to as port infrastruc-
ture). Detailed building damage data have been collected
by the Ministry of Land, Infrastructure, Transportation and
Tourism (MLIT, 2014) post-tsunami. However, the MLIT
database predominantly consists of residential, commercial
and some industrial buildings. Buildings within the port area
are mostly missing from the database, and infrastructure such
as silos, cranes and towers was not identified in the MLIT
database.
To develop our own database of port structures, we ex-
tended the MLIT database, which already consisted of out-
lines of 3057 buildings in the port area. To build the new
database, port structure outlines (n=2173) were digitised
into a geographic information system (ArcMap 10.5) using
building footprints from the Geospatial Information Author-
ity of Japan interactive map platform (Geospatial Informa-
tion Authority of Japan, 2013) as well as pre-tsunami aerial
images from Google Earth Engine (Table 1). We identified
3343 buildings and 1887 infrastructure types (5230 total).
The database is stored in the form of a geographic infor-
mation system (GIS) attribute table. For each structure, we
collected information on
1. the type of industry,
2. the name of port,
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1890 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
Table 1. Data used in this study, their sources and the reference period from which data are taken.
Data Source Data observation/ Citation
acquisition period
Tsunami inundation Ministry of Land, Infrastructure, Transportation and Tourism Mar 2011–Dec 2012 MLIT (2014)
depths
Building database Ministry of Land, Infrastructure, Transportation and Tourism Mar 2011–Dec 2012 MLIT (2014)
Port structure footprint GSI interactive web: map/aerial – Geospatial Information
for digitisation photo browsing service Authority of Japan (2013)
Google Earth Engine Mar 2009–Sep 2010 © Google Earth 2020
Aerial images for Google Earth Engine Mar 2009–Sep 2010a© Google Earth 2020
damage assessment Mar–May 2011b
Feb 2012c
GSI map: aerial photo of Mar–May 2011bGeospatial Information
affected area Apr 2012cAuthority of Japan (2012a)
Oblique images for GSI map: oblique photo of May 2011bGeospatial Information
damage assessment affected area Authority of Japan (2012b)
Street view images for Google Street View Jul–Aug 2011b© Google Street View 2020
damage assessment Aug 2013c
Land use (industry) Google Maps – © Google Maps 2020
classification
aPre-tsunami. bImmediate phase after tsunami. c1 to 2 years after tsunami (intermediate phase) for damage assessment.
Figure 2. The framework of this study follows the approach of a machine-learning workflow. A damage database for port structures is
constructed through data collection and processing. The consolidated data are then randomly split into training and test sets for model
building and evaluation. This process is usually iterated until a satisfactory model is selected for the development of fragility functions.
This is usually the case where there is more than one model or parameter to choose from, whereas in our case only inundation depth was
considered as an explanatory variable.
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1891
Table 2. Proposed classification for port activities found in the Tohoku region.
Industry type Description of port activities
Maritime industries Cargo handling industry Cargo handling services such as loading and unloading of
ships (stevedoring) as well as the handling of cargo on shore.
Typical infrastructure: loading and gantry cranes, storage
yards, storage sheds, silos, chillers and warehouses
(buildings).
Warehousing and distribution Cold storage, warehousing and logistics support.
Typical infrastructure: storage sheds, tanks and silos.
Non-maritime port- Chemical industry Bulk chemical production, e.g. alkane, propane and fertilisers.
related industries Typical infrastructure: distillation towers, tanks, silos,
conveyors, pipes, pumps, compressors, reactors, vessels,
wastewater treatment systems, chemical separation columns,
substations and open frame structures.
Construction material industry Concrete and cement manufacturing. Asphalt and wood
processing.
Typical infrastructure: rotary kiln/furnace, coal storage,
grinders, mills, pre-heating towers, coolers, tanks, silos,
conveyors, sorters and stackers.
Energy-related industry Coal power generation. Electric power generation and
distribution.
Typical infrastructure: mills, power plants, substations,
transformers, chimneys, boilers, generators, cooling towers,
turbines, condensers, pumps and electricity transmission
towers.
Food industry Seafood processing and food packaging. Feed manufacturing.
Typical infrastructure: ovens, cold storage (buildings),
freeze dryers, tanks, mixers, conveyors, boilers and vessels.
Manufacturing industry Metal and alloy products. Plywood and paper products.
Typical infrastructure: grinders/refiners, chimneys,
furnaces, silos, tanks, screens, conveyors, cranes, mills and
rollers.
Petrochemical industry Oil depots, reserves and refineries.
Typical infrastructure: furnaces, distillation towers,
crackers, compressors, condensers, vessels, tanks, silos and
pipelines.
3. the name of company at the time of tsunami (where
available),
4. maximum inundation depth values,
5. assigned damage state, and
6. structure type (building or infrastructure).
4.2 Attributes of port structures and industry
Unique to this work, damaged structures were classified ac-
cording to their industry type (Table 2). As with the con-
struction of any fragility function, a key assumption is that
structures under the same taxonomy are likely to perform
similarly when exposed to a given hazard intensity (Pitilakis
et al., 2014). For that reason, the classification of structures
determines the robustness of the fragility functions devel-
oped. It was therefore important to create a suitable taxon-
omy for the types of structures being studied. Convention-
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1892 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
ally, building damage has been assessed by separating the
buildings into their various construction types (e.g. masonry,
wood, steel, unreinforced and reinforced concrete). Charvet
et al. (2014) noted differences in the performance of build-
ings with different construction types against tsunami im-
pacts following the Tohoku event. However, port structures
consist of both buildings and infrastructure, with the infras-
tructure being of a highly specialised nature where the de-
sign and construction criteria are industry-specific. A more
suitable approach then would be to classify port structures
according to their industry.
Different types of port activities occupy the port area.
Aside from the core business of terminal operations, the port
is also host to distribution centres and non-maritime activi-
ties. To the best of our knowledge, there is no standard in-
dustrial classification for port activities. We therefore pro-
posed a broad classification for the port activities found in
Tohoku ports, according to the general industry that they fall
into (Table 2). Classification for non-maritime port industries
was adapted from the terminologies used by European Sea
Ports Organisation (ESPO, 2016) for the various industrial
sectors found in European ports. We used Google Maps and
Google Street View to identify the business nature of each
company (industry type), commonly through the name of the
company at the time of the tsunami. We identified eight main
port industries based on our proposed taxonomy.
Buildings in port industries commonly include administra-
tive offices, control and maintenance buildings, warehouses,
and cold storage. Industrial buildings are typically of steel or
concrete construction. On the other hand, the types of port
infrastructure are diverse – ranging from small transformers
to large loading cranes. Some common infrastructure types
found in each industry are listed in Table 2, adapted from
the descriptions provided by the AIR Construction and Oc-
cupancy Class Codes (AIR Worldwide, 2019). Because of
their diversity, port infrastructure types vary widely in their
construction, and unlike buildings it is extremely challeng-
ing to classify them according to their construction nature. It
is interesting to note, however, that several industrial infras-
tructure types are installed in support structures or housed in
buildings. In the petrochemical industry, for example, oil and
gas are commonly stored in steel or concrete silos and tanks.
4.3 Maximum inundation depths
Various tsunami hazard intensity measures (e.g. inundation
depth, flow velocity and force) have been used in literature
to estimate structural fragility to tsunami impacts. Past stud-
ies (Macabuag et al., 2016; Park et al., 2017; Attary et al.,
2019) have shown that no single measure can fully charac-
terise structural fragility to tsunami impacts as it is impossi-
ble to explain a complex phenomenon through a sole param-
eter. For the purpose of this study, observed maximum inun-
dation depth was chosen as the representative intensity mea-
sure manifesting damage since depth is more easily estimated
from field survey after tsunami events as compared to other
flow values, which typically have to be simulated. Using ob-
servational data also minimises the uncertainty in intensity
measure as compared to using simulated data (e.g. velocity
and force).
Inundation characteristics were recorded and collected
from a number of sources, namely tsunami trace heights
by the Tohoku Earthquake Tsunami Joint Survey Group
(TTJS, 2011), MLIT survey, photographs, videos, eyewit-
ness accounts and other reports (Leelawat et al., 2014). The
MLIT (2014) compiled all the maximum inundation depth
values and building data into a single database. Inundation
depth refers to the depth of floodwater above ground. Each
building surveyed in the MLIT database is pegged with max-
imum inundation depth values, and where values were not
available for some buildings (e.g. those that were washed
away), they were interpolated from nearby buildings with in-
undation depth values (De Risi et al., 2017). Similarly, for
buildings and infrastructure that were identified in this study,
we interpolated inundation depth values from the nearest sur-
veyed buildings through visual assessment.
4.4 Proposed damage classification scheme
For the first time, a damage classification scheme for tsunami
damage to port structures is being proposed (Fig. 3). The
MLIT adopted a damage classification scheme for build-
ing damage assessment following the 2011 Tohoku tsunami
(see Leelawatt et al., 2014). Naturally, subsequent stud-
ies that used the MLIT damage database to analyse dam-
age and derive fragility functions followed the same clas-
sification scheme. The pitfalls of adopting the MLIT dam-
age classification have been highlighted in several studies
(Leelawat et al., 2014; Charvet et al., 2015, 2017). Firstly,
the MLIT classification consists of six damage states, which
were found to have overlaps in their definitions (Leelawat
et al., 2014; Charvet et al., 2015). The overlapping defini-
tions might have resulted in buildings being wrongly classi-
fied when performing damage assessment. Ideally, damage
states should be presented in a mutually exclusive and con-
secutive order (Charvet et al., 2015). Secondly, descriptions
in the MLIT classification scheme do not distinguish between
structural and non-structural damage. Therefore, the struc-
tural response of the buildings assessed is not being explicitly
assessed. Additionally, by specifying the range of inundation
depths associated with each damage state, such definitions
allude to inundation depths being a condition of damage.
This contradicts the objective of developing fragility func-
tions as predictive models of damage. Over and above the
limitations outlined, the MLIT damage classification solely
describes damage to buildings, which is otherwise unsuitable
for port structures.
To address the limitations of the existing damage classi-
fication of MLIT, we proposed a new damage classification
for port structures. This new classification scheme provides
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1893
Figure 3. Proposed new damage classification for port industries. Descriptions for damage to both buildings and non-building infrastructure,
together with simplified illustrations of the structures, are provided in the classification table. DS 1 and DS 2 are considered as non-structural
damages, while DS 3 and DS 4 are structural damages.
damage descriptions for both buildings and infrastructure.
Degrees of damage are classified into four levels (with dam-
age state DS 0 being no damage), ensuring that the descrip-
tions for each damage state are mutually exclusive and in
increasing order. Descriptions also include the expected ser-
viceability of the structure at each damage state. Pitilakis et
al. (2014) argued that physical damages would reflect the ex-
pected serviceability of the structure (condition for use) and
its corresponding functionality (i.e. can its functions still be
fulfilled?). The structural integrity of port structures is also
being considered. For instance, between DS 2 and DS 3,
damage is distinguished by whether it only affected non-
structural components and/or roof (DS 2) or structural com-
ponents such as columns and beams (DS 3). We assumed that
when the structural integrity of a structure is compromised,
the structure would be removed.
4.5 Damage assessment through spatio-temporal
analysis
A combination of free-to-use sources was used to inform
our classification decisions when assigning damage states
to individual port structures (Table 1). Port structures were
assessed through the analysis of satellite imagery, using
pre- and post-tsunami images from Google Earth Engine
and the Geospatial Information Authority of Japan (2012a),
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1894 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
as well as photographic interpretations of post-tsunami
oblique images from the Geospatial Information Authority
of Japan (2012b). Pre- and post-tsunami images refer to ob-
servations made before 11 March 2011 and on and after
11 March 2011 respectively (Table 1). Apart from aerial and
oblique images, we visually assessed the conditions of port
structures through Google Street View images. Google Street
View, a service available on Google Maps web, provides a
panoramic view of the landscape at a street level. An exam-
ple of how a building or infrastructure was being assessed is
illustrated in Fig. 4.
The three types of images (aerial, oblique and street view)
provided different, yet complementary, types of informa-
tion. Aerial images were particularly useful in assessing
washed-away and collapsed structures (DS 4). Street view
images were used to identify damage from the façade level,
which provided alternatives to ground truth surveys. The
high-resolution imagery provided by Google Street View al-
lowed us to pick up finer details such as structural and non-
structural damage to port structures, which would otherwise
be missing from aerial imagery. However, because street
view imagery was captured through vehicle-mounted cam-
eras, the availability of these images is constrained by the
accessibility of roads by the vehicle at the time of survey.
Where imagery was not captured by Google Street View due
to such constraints, we capitalised on the alternative views
provided by GSI oblique images.
Advances in mapping technologies mean that temporal
changes are also being captured and documented in these
mapping applications. The time-slider functions on Google
Earth Engine and Google Street View web, as well as the
date stamps on GSI images, allowed us to review temporal
changes in the built environment. For images in Google Earth
and Google Street View, different phases of the tsunami,
i.e. pre-tsunami (before March 2011), immediately after the
tsunami (up to 6 months after the tsunami) and the interme-
diate recovery phase (1–2 years), were all captured in the
same point locations. With coordinates being embedded in
the aforementioned data sources, we were also able to refer-
ence GSI aerial and oblique post-tsunami images to the same
locations. The large amount of high-quality data provided by
these image databases and mapping applications have been a
large driver of our data collection in this study.
5 Model building
Fragility functions describe the probabilities of damage ex-
ceedance for a given intensity measure or flow characteris-
tic. The probability of damage exceedance can simply be ex-
pressed as
PDS =P (ds ≥DS|IM), (1)
where “ds” is the observed damage state of a structure,
“DS” is the classification provided by the damage scale and
“IM” is the intensity measure (Charvet et al., 2017). In the
case of this study, tsunami inundation depth was used as an
explanatory variable in the prediction of structural damage
probability. Typically, empirical tsunami fragility functions
are constructed by fitting an appropriate statistical model to
post-tsunami damage data.
5.1 Evaluation of statistical models available
In recent years, a number of studies evaluated the suitabil-
ity of various statistical models in representing tsunami dam-
age to structures (Charvet et al., 2014, 2017; Macabuag et
al., 2016). Parametric (e.g. ordinary least squares regression,
generalised linear model or ordinal logistic regression mod-
els), semi-parametric (e.g. generalised additive model) and
non-parametric (e.g. kernel smoother) statistical model types
are amongst the most commonly used. These statistical mod-
els are extensively reviewed in Rossetto et al. (2014), Lalle-
mant et al. (2015), Macabuag et al. (2016) and Charvet at
al. (2017), and readers are referred to these studies for a more
comprehensive understanding of the advantages and disad-
vantages of using the various types of statistical models.
Generalised linear models (GLMs), an extension of clas-
sical linear regression models, have been recommended as
more reliable forms of fragility functions for the following
reasons.
–Discrete probability distributions can be used to predict
discrete responses (Charvet et al., 2017). This is espe-
cially important for categorical data (such as damage
states), because it is statistically incorrect to assume that
the difference between categories is linear/continuous –
e.g. the difference between DS 1 and DS 2 holds the
same meaning for the difference between DS 2 and DS 3
(Guisan and Harrell, 2000).
–Unlike classical linear regression models (e.g. ordinary
least squares regression) which assume either a normal
or lognormal distribution, the response variable need
not be normally distributed and can take on any of the
exponential family distributions.
–It does not assume a linear relationship between the ex-
planatory variable and response variable, but a linear re-
lationship is assumed between the transformed response
through a link function and the explanatory variables.
–Maximum likelihood estimation (MLE) is used rather
than ordinary least squares to estimate the parameters.
MLE has the advantage of explicitly reflecting the prob-
ability distribution of the random variable of interest.
–Overfitting of data can be avoided by using cross-
validation analysis to determine optimal model parame-
ter values.
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1895
Figure 4. A building (circled in red) in Ishinomaki Port has been selected to demonstrate how spatio-temporal damage assessment had been
conducted in this study. For every port structure, we reviewed four main sources of data (© Google Earth 2020, © Google Street View 2020,
GSI aerial and oblique images) to estimate the level of damage sustained during the tsunami.
–Model uncertainty can be quantified by supplementing
the median of the response with confidence or predic-
tion intervals.
5.2 Data exploratory analysis
The response variable is ordinal (in the sense that
DS 0 <DS 1 <DS 2 <DS 3 <DS 4). A visual inspection
of the distribution of depth given damage data (Fig. 5) in-
dicates non-normality, with the distribution skewed towards
the right, indicating a lognormal transformation of inunda-
tion depth variable would be appropriate. Frequency counts
of the damage data show that damage state (DS 1) makes up
the majority of the dataset (n=2710), and DS 3 and 4 make
up a much smaller proportion (n=576 and n=605 respec-
tively).
5.3 Selection of a suitable statistical model
An ordinal logistic regression model, an ordinal and logis-
tic recourse of GLMs, is adopted. It has the additional ad-
vantage of accounting for and maintaining the ordered na-
Figure 5. Histograms of each damage state. Distribution of damage
data indicates non-normality, and DS 1 accounts for the majority of
the dataset.
ture of damage-state data. As this model recognises the or-
dered nature of the damage states, overlapping pathways of
the fragility functions can be avoided (Charvet et al., 2017).
Overlapping fragility functions, as is common when fitting
separate GLMs, may unwittingly imply that the probabil-
ity of a higher damage state (e.g. DS 4) being exceeded is
higher than that of a lower damage state (e.g. DS 3) as inun-
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1896 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
dation depth increases. Ordinal models also make full use of
the ranked data rather than simplifying them into binary ex-
ceedance and non-exceedance, therefore preventing the loss
of information (Ananth and Kleinbaum, 1997).
The dependence of the response variable DS on predictor
variable Xcan then be represented as follows:
PDS =Pds ≥DSi|Xj,(2)
where DSirefers to the ith damage state, jthe specified pre-
dictor (IM) or combination of predictors. The model relates
the probability of the outcome, PDS, to all explanatory vari-
ables (X1,X2, . . . Xj) through a linear predictor. There are
three basic components to any GLM, and Table 3 describes
the components in the context of the ordinal logistic model
used in this study.
The conditional probability P (ds ≥DSi|Xj)is a common
vector of regression coefficients β, which connects proba-
bilities for varying levels of damage. When expressing the
cumulative probabilities of each damage state as separate
curves, the relationships between damage states in increas-
ing order of severity are defined as follows:
PDS =Pds =DSi|IM =Xj=
1−Pds ≥DSi|Xji=0
Pds ≥DSi|Xj−Pds ≥DSi+1|Xj0≤i≤NDS
Pds ≥DSi|Xji=NDS
,(3)
where NDS refers to the number of damage states, including
DS 0 (Macabuag et al., 2016).
6 Model evaluation
6.1 Ten-fold cross-validation
Model accuracy was used as a quantitative indicator of the
performance of our models. We wanted to assess the good-
ness of fit of the models and determine its predictive ability. It
was difficult to test the predictive ability of our models where
there were no further samples to test with. In order to opti-
mise model design while preventing overfitting, the cross-
validation method was applied to evaluate the prediction ac-
curacy of our models. Cross-validation techniques make use
of the available dataset by dividing them into two subsamples
– one to train the model and the other to predict the model on.
One cross-validation technique is Kfold, where the
dataset is divided into Knumber of approximately equal-
sized subsets as illustrated in Fig. 6a. One subset is taken out
as a test set for validation, and the remaining K−1 subsets
are then used to train a model. This hold-out method is then
repeated for Knumber of times, with a new subset being
used as a test set in each iteration. Only after all Kmodels
are fitted, statistics of the model performance are tabulated.
For the purpose of this study, a 10-fold cross-validation ap-
proach was taken.
The accuracy of a model is determined by the proportion
of correctly classified responses. When applied to the k-fold
technique, the fitted model is used to predict response on the
held-out kth subset in each iteration. The recorded response
is tabulated against actual observations in the kth subset, and
a confusion matrix is constructed as demonstrated in Fig. 6b.
The diagonal of the confusion matrix represents the sum of
correctly predicted response; the proportion of correctly clas-
sified response is then calculated by
accuracy =sum of correctly predicted response
sum of total observations .(4)
Accuracies are recorded in each iteration of the Kfold, and
the mean and standard deviation of the tabulated accuracies
are taken to assess the predictive ability of the model. All
statistical analyses and modelling in this study were carried
out using the statistical software R (R Core Team, 2020).
6.2 Quantification of uncertainty
The fragility functions, when presented as curves or plots,
represent the expected value of the response variable. There-
fore, they represent only a sample estimate of the population
values. Statistical variations of the fragility functions can be
accounted for by estimating the confidence intervals. In this
study, we adopted bootstrap-based confidence intervals to es-
timate the uncertainty in estimation or prediction. The boot-
strap method treats the original dataset of values as a realised
sample from the true population and does not make any as-
sumptions about the underlying distribution of the population
parameters (Yung and Bentler, 1996). Values from the origi-
nal dataset are resampled repeatedly, with replacement. This
was done for 1000 iterations, with the predicted logit com-
puted in each iteration. To derive a 95 % confidence band, the
2.5th and 97.5th quantiles of the 1000 estimates were drawn
at each inundation depth interval (0.01 m).
7 Results
7.1 Damage database for port structures
To characterise the vulnerability of assets in various port
industries, damage assessment was performed for build-
ings and infrastructure in the Tohoku region. We com-
piled damage information on port structures into a database,
which is available online through an unrestricted data repos-
itory (DR-NTU) hosted by Nanyang Technological Uni-
versity (https://doi.org/10.21979/N9/OTZMT1) (Chua et al.,
2020).
The port damage database consists of 5230 port structures,
of which 3343 are buildings and 1887 are infrastructure. The
port structures were identified in six case study ports, across
eight port industries. The damage dataset shows that most
port structures sustained minimal structural damage classi-
fied as damage state DS 1 (Table 4). Consistently for all port
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1897
Figure 6. (a) An example of a five-fold cross-validation technique for the purpose of illustration. The same dataset can be folded into five
equal sizes: one fold is held out for testing, and the remaining four folds are used to develop a training model to predict the accuracy of
the training model. This is repeated five times, with accuracies being tabulated in each iteration. (b) An accuracy table (confusion matrix) is
produced in each iteration of the kfolds. The sum of the diagonal in the table is divided by the sum of observations to get the percentage of
accuracy in the kth fold.
Table 3. Components of an ordinal logistic regression model.
Random component The probability distribution of the response variable.
A multinomial distribution is assumed for the cumulative probabilities in an ordinal logistic
regression model.
Systematic component The explanatory variable (Xj)or the linear combination of the explanatory variables
(X1,X2, . . . Xj)in creating the linear predictor, e.g.β0+β1, X1,β2, X2+... +βj, Xj, whereβ0and
β1,j are transformed constant and regression coefficients through maximum likelihood
estimation.
Link function The link between random and systematic components.
Describes how the cumulative probability PDSiof the expected outcome for any damage state
DSirelates to the linear predictor of explanatory variables Xj. In this instance, the link function
chosen takes on a logit form g, where
gPDSi=logPDSi
1−PDSi,
with
PDSi=Pds≥DSi|Xj∀i∈(1, ..., I ).
Therefore, the dependence of the response variable DS on the linear predictor can be
re-expressed as
logPDSi
1−PDSi=β0,i +β1X1+β2X2+... +βJXJ,
logPDSi
1−PDSi=β0,i +
J
P
j=1
βjXj.
The corresponding regression coefficients β1,j in the link function are fixed across every
damage state except for the intercept, so as to maintain the order of the response categories.
industries, the majority of the observed damage corresponds
to DS 1 (Fig. 7). Notably, many industries such as chem-
ical, petrochemical and energy-related industries sustained
minimal structural damage mainly due to flooding at DS 1,
which only required some clean-up and interior restoration
and remained mostly operational after restoration. On the
other hand, cargo handling and food industries sustained a
wide range of damage from minimal damage (DS 1) to total
damage (DS 4), corresponding to nearly all damage states.
Tsunami floodwaters at depths of less than 5 m inundated
most port structures. In extreme cases, inundation depths af-
fecting port structures reached as high as 7.5 m.
7.2 Fragility functions for port industries
Fragility functions were produced for eight major port indus-
tries as depicted in Fig. 8. Individual fragility curves were
plotted for each damage state, and the solid lines represent
the probabilities of a structure exceeding each damage state
given a range of inundation depths and the shaded regions
their corresponding 95 % confidence intervals.
The fragility functions (Fig. 8) suggest that chemical,
cargo handling and construction material industries are more
vulnerable. Higher probabilities of damage exceedance are
reached at a more rapid rate as compared to other indus-
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1898 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
Figure 7. Data attributes of the port industries affected by the 2011 Great East Japan tsunami.
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1899
Figure 8. Fragility curves with 95 % confidence bands for port industries identified in this study. Chemical, cargo handling and construction
material industries appear to be more vulnerable to tsunami inundation depths, while petrochemical and warehousing and distribution indus-
tries have lower damage probabilities for the same inundation depths. Wider confidence bands imply greater variability in uncertainty and
could be results of smaller sample sizes.
tries. In contrast, energy-related industry and warehousing
and distribution are showing a gentler incline in damage
probability for higher levels of damage (DS 3 and DS 4),
indicating a greater resistance to tsunami impacts. A key as-
sumption of fragility studies and of this study is that dam-
age is directly related to the properties of the elements at
risk. Thus, aside from tsunami intensity measures, the com-
position and structural design of each industry could deter-
mine the differences in vulnerabilities. For example, power
plants (energy-related industries) and warehouses are struc-
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1900 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
Table 4. Summary of port structures identified in the various ports, sorted according to their industries.
North Tohoku South Tohoku
Hachinohe Kuji Ishinomaki Sendai Soma Onahama Total
Cargo handling industry 31 9 31 32 25 62 190
Warehousing and distribution 111 16 175 105 39 17 463
Chemical industry 236 – 208 27 85 – 556
Construction material industry 29 20 20 99 9 37 214
Energy-related industry 125 – – 104 134 50 413
Food industry 12 37 430 151 – – 630
Manufacturing industry 1010 60 587 279 144 – 2080
Petrochemical industry 202 41 38 324 – 79 684
Total 5230
turally robust by design. Most heavy equipment found in
power plants is normally supported in large reinforced con-
crete foundations or housed in large steel structure build-
ings (Cruz and Valdivia, 2011) and is therefore more re-
sistant to tsunami loads. Likewise, many warehouses in the
studied ports were reinforced concrete buildings with their
warehouse floor raised above road levels, which increased
the height of non-structural elements (e.g. docks and doors)
relative to tsunami inundation. Comparatively, chemical fa-
cilities typically consist of more fragile components which
are not part of the primary load-resisting systems such as
pipelines, pumps, compressors and tanks, and they are ex-
tremely vulnerable to damage from tsunami inundation and
forces. As observed in the 2011 event, hydrodynamic and
hydrostatic forces from the tsunami resulted in the breaking
of pipe connections, floating tanks and overturning of unan-
chored infrastructure (Krausmann and Cruz, 2013). Mean-
while in cargo handling facilities, loading and unloading in-
frastructure types were mostly anchored, but instances of
cracked pavements and damaged crane rail foundations by
the earthquake and tsunami were reported to result in the de-
railment and collapse of cranes (Technical Council on Life-
line Earthquake Engineering, 2013).
Other factors such as debris impact and proximity of the
structure to the shoreline should not be discounted when
considering differences in the response of each industry to
tsunami impacts. Tsunami-borne debris can contribute sig-
nificantly to structural damage. This issue is particularly
present in port facilities, where ships, containers, mobile
equipment, and construction materials such as wood logs and
concrete objects can impact on structures. Port structures are
typically of more robust construction, and therefore they act
as barriers in the path of debris motion for as long as inun-
dation depth is lower than the structure height (Reese et al.,
2007; Naito et al., 2014). As a result, they are more likely
to be subjected to damage from debris impact (Charvet et
al., 2015). While debris impact is location-specific and does
not affect all areas in the same ways, some industries may
be more susceptible to debris impact than others. For exam-
ple, in cargo handling and construction material industries,
where mobile large objects such as containers and wood logs
are stored in open yards, there is a higher concentration of
potential debris and therefore a higher debris delivery po-
tential (Naito et al., 2014). Kumagai (2013) surveyed the
post-mortem dispersal of containers after the 2011 Tohoku
event and found that containers, which were not washed out
to sea, were mostly dispersed within the terminals where
they were located in. Many of these containers were also
found to be concentrated around buildings surrounding the
container yards without travelling further inland (Kumagai,
2013; Naito et al., 2014), which suggests that damage sus-
tained to structures within these facilities is more likely a
consequence of the combined effect of debris impact and
tsunami flow than hydrodynamic force alone.
For each damage state, we considered the minimum depths
where damage exceedance probability reaches near 1 or be-
comes nearly certain. Minimum damage (DS 1) is almost
certain at 2.5 m consistently for all industries except energy-
related industry. DS 1 occurs when there is water pene-
tration into the building and interior restoration is required
(Fig. 3). Logically, water penetration into buildings would be
expected from 0.45 m since buildings are required to be con-
structed 0.45 m above road level as specified by the Build-
ing Standard Law of Japan (Building Centre of Japan, 2013).
Threshold depths for DS 1 might have occurred at 2.5 m be-
cause of the aggregation of data for both infrastructure and
buildings. We observed that there were many buildings (es-
pecially warehouse) and infrastructure such as storage tanks
and silos that were elevated above ground, and therefore the
number of exposed assets at lower inundation depths were
reduced. The trend for other damage states is, however, not
obvious, and it is difficult to pinpoint minimum depth values
where damage becomes certain.
A threshold value is said to be reached when damage
curves from all states of damage converge at the proba-
bility of near 100 %. Key threshold value can be defined
as the parameter (in this case, inundation depth) criteria at
which DS 4 (collapse) becomes certain. Earlier studies of
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1901
the 2011 Great East Japan tsunami (Suppasri et al., 2013;
Charvet et al., 2014) examined the key threshold values for
buildings, using damage data provided by MLIT. Suppasri et
al. (2013) identified 2 m to be the key threshold value for all
building types. More recent analysis found inundation depth
thresholds to differ between construction types: from 2 m for
wooden buildings (Charvet et al., 2014) to more than 10 m
(Charvet et al., 2015) for steel and reinforced concrete con-
struction types. Similar patterns have emerged in the present
analysis. The near 100 % probability of collapse occurs at
inundation depth exceeding 10 m for all industries. As such
we were unable to quantify the key threshold values for col-
lapse for port industries. There are several possible reasons
for this observation. Two likely explanations stand out. The
first being port structures are structurally much more resis-
tant to tsunami loads than regular low-rise buildings because
industrial buildings and structures are designed to withstand
greater loads, including but not limited to dead loads, live
loads, and wind and earthquake loads. Therefore, greater
tsunami inundation depths are required to overcome the re-
sistance of port structures. A second possible explanation is
that inundation depth alone is insufficient to explain damage,
although it provides a first indication.
The effects of uncertainty were quantified through the con-
struction of confidence intervals around the median of the re-
sulting probabilities. Confidence intervals around DS 1 are
consistently narrow in width for all industries (Fig. 8), which
could be associated with its large sample size. Contrastingly,
for higher levels of damage (DS 3 and DS 4), confidence in-
tervals tend to widen towards higher inundation depths. An
observation made in the process of damage data collection
through photographic interpretations was that many struc-
tures sustained very little damage despite high inundation
depth values, which explains the smaller sample sizes and
therefore wider confidence intervals for DS 3 and DS 4 at
higher depth values. In the same way, industries with the
widest confidence intervals such as cargo handling industry
and construction material industry tend to have smaller sam-
ple sizes. By contrast, variabilities around the median curves
tend to be smaller for the manufacturing industry, food in-
dustry, warehousing and distribution, and petrochemical in-
dustry due to their larger sample sizes.
These findings can alternatively be justified by the ef-
fects of debris impact. A couple of studies (e.g. Charvet
et al., 2015; Macabuag et al., 2018) have found the inclu-
sion/omission of debris impact to have an effect on fragility
models. Macabuag et al. (2018) demonstrated that models
that include regression parameters considering debris impact
have a better fit (statistically more significant) than models
that do not. The authors also argued that the omission of de-
bris information will likely introduce systematic bias to the
fragility models. In this study, debris impact has not been
explicitly considered in the development of fragility models,
though it could be a source of uncertainty in our fragility
models. Intuitively, structures that were damaged by debris
would fall into higher damage states and likely experienced
higher tsunami intensity values (i.e. depth and velocity). By
neglecting debris impact, it is unsurprising that confidence
intervals tend to widen towards higher depth values for DS 3
and DS 4 (Fig. 8). Similarly, by neglecting debris informa-
tion, fragility functions derived for industries, such as cargo
handling and construction material industries, that are more
heavily impacted by the debris-related damage are expected
to have greater uncertainties.
8 Discussion
8.1 Comparison of damage dataset with functionality
of port industries post-tsunami
We compared the damage database with existing literature to
validate our observations. Most of the existing literature is
either limited to descriptive analysis of damage to port facil-
ities or is not available in English. We found only one study
to be comparable with this study, in terms of the quantifi-
cation of damage to port industries. A post-2011 tsunami
survey was carried out by the Tohoku Regional Develop-
ment Bureau (MLIT, 2011) between October and Novem-
ber 2011. We considered the survey period as the interme-
diate period for reconstruction after the tsunami. The survey
is a questionnaire survey on the recovery status of compa-
nies in tsunami-affected ports, including ports outside of our
study sites. A total of 226 of the 233 companies found in the
affected ports responded to the survey. Findings from the sur-
vey were adapted from MLIT (2011), and we have translated
them into English (Fig. 9).
We drew comparisons between the recovery status of the
companies affected (MLIT survey) and the serviceability of
port structures at each damage state (this study). It is difficult
to make a direct comparison between the two. While port
structures are the physical components of these companies,
port structures and companies are inherently different enti-
ties. Therefore, an assumption made here is that the service-
ability of port industries is indicative of the recovery status
of the companies surveyed in the MLIT survey.
A total of 13 % of the companies were found to be un-
affected by the tsunami (Fig. 9), which marks a good agree-
ment with our study where port structures sustaining no dam-
age (DS 0) make up 9 % of the dataset (Fig. 4). In addition,
approximately 12 % of the companies were found to be un-
recoverable, which we assume to correspond to damage state
DS 4 (11 %) in our study. The MLIT survey found 72 % of
the companies to be in various stages of recovery during the
survey, and a majority (46.8 %) of the companies were almost
fully recovered (>80 % recovery) in the intermediate phase.
Similarly, a large proportion (52 %) of our damage data falls
into DS 1 where port structures can be operational almost im-
mediately after a tsunami (Fig. 3). It is challenging, however,
to draw parallels between the degrees of recovery with the
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1902 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
Figure 9. Damage conditions and degrees of recovery of companies in the tsunami-affected ports of Hachinohe, Kuji, Miyako, Kamaishi,
Ofunato, Ishinomaki, Sendai-Shiogama, Soma and Onahama. A total of 65 % of the recovering companies were almost close to full recovery
(>80 %) at the time of the survey. Adapted and translated from MLIT (2011).
Table 5. Mean accuracies and standard deviations of accuracies of
the various port industries.
Industry type Mean SD
accuracy accuracy
Cargo handling industry 0.374 0.221
Warehousing and distribution 0.397 0.198
Chemical industry 0.687 0.300
Construction material industry 0.502 0.285
Energy-related industry 0.707 0.245
Food industry 0.283 0.204
Manufacturing industry 0.638 0.249
Petrochemical industry 0.746 0.218
All industries (whole Tohoku) 0.587 0.203
damage states presented in this study. We stress that this ap-
proach is a relative measure of the validity of our dataset and
damage assessment. Nonetheless, we can infer that damage
observations made from photographic interpretations in this
study are rather similar to actual observations.
8.2 Fragility models and their classification accuracies
Using the 10-fold cross-validation technique, we evaluated
the prediction accuracies of our models. Mean accuracies and
their standard deviations for each industry are illustrated in
Table 5. Port structures have an overall accuracy of 59%. The
petrochemical industry, energy-related industry, chemical in-
dustry and manufacturing industry display higher accuracies
– 75 %, 70 %, 69 % and 64 % respectively. In contrast, the
warehousing and distribution industry, cargo handling indus-
try, and food industry display lower prediction accuracies –
40 %, 38 % and 28% respectively.
We looked at the underlying nature of our datasets to bet-
ter understand the differences in accuracies. The petrochem-
ical industry, energy-related industry, chemical industry and
manufacturing industry display higher accuracies and are
represented by large sample sizes (Fig. 7). On the contrary,
the cargo handling industry is represented by only 190 data
points. However, because the food industry is represented by
a large sample size but seemingly displays very low accu-
racy, we were unable to conclude that sample size has an in-
fluence on the accuracies of the fragility models. In addition,
the three industries (warehousing and distribution, cargo han-
dling, and food industries) which display low accuracies are
well represented across all damage states.
The intrinsic differences between industries could have an
effect on reducing accuracies. The composition of buildings
and infrastructure differ from industry to industry. For in-
stance, the cargo handling industry, which displays lower ac-
curacy, typically consists of mobile equipment such as cranes
and conveyors as well as temporary transitional storage and
components such as chillers and tanks. Damage to transient
port structures as such may be reflected in the damage data
as part of the overall assessment and introduce noise to the
damage data, thus reducing model accuracy. In addition, the
structural design of port structures may vary between facil-
ities of the same industry. For example, warehouses in the
studied ports were mostly reinforced concrete buildings, but
some were made of mixed materials such as reinforced con-
crete foundations with light metal or masonry walls, whereas
power plants (energy-related industry) and the petrochemi-
cal industry are consistent in construction material and more
robust by design, which perhaps explains their higher accu-
racies. Thus, variability between port structures of the same
industries can also impact accuracy if those variables are not
accounted for in the models. Second-order factors beyond
flow regime such as debris impact and proximity to the shore-
line could also have an effect on model accuracies.
Another possible explanation is that many assets might
have sustained extensive damage from earthquake activities
such as ground motion and liquefaction prior to the tsunami,
as was observed by Kazama and Noda (2012). A preliminary
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C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1903
inspection of the damage dataset indicated a greater repre-
sentation of data from ports that have experienced stronger
ground motion for the following industries – food, cargo han-
dling, and warehousing and distribution (Table 4). On the
other hand, industries that display higher accuracies have
a greater data representation from ports that were not as
severely affected by ground motion. The significance of this
relationship between the effects of the preceding earthquake
and the damage observed is further investigated in the pro-
ceeding section.
For most industries, our models performed better in terms
of their classification accuracies as compared to fragility
models developed for buildings using the MLIT damage clas-
sification, which were found to have an accuracy of 52 %
(Leelawat et al., 2014). As this is the first time tsunami dam-
age is being quantified as a response of inundation depth for
port industries, we have no other models that we could use
for comparison.
8.3 Effects of pre-tsunami earthquake activities on
observed damage to port structures
One of the concerns raised in the process of this research
was the effect of ground motion, which preceded the arrival
of the tsunami, on asset damage. The effect of ground mo-
tion on damage to coastal structures was studied by Sugano
et al. (2014). The authors noted that in the northern Tohoku
region, only little damage was sustained due to ground mo-
tion, and the damage observed was to a greater effect due
to tsunami inundation. On the other hand, damage due to
ground motion was substantially greater in the southern To-
hoku region, more specifically coastal areas south of Miyagi
Prefecture. Similar observations were made by Okazaki et
al. (2013), who conducted surveys in Ishinomaki and Sendai
ports and found that the two sites were exposed to both severe
ground motions and great tsunami wave heights. Kazama and
Noda (2012) have also highlighted the possibilities of liq-
uefaction prior to the arrival of the tsunami but noted the
impossibility of identifying locations where liquefaction had
occurred after the tsunami.
To assess if ground-motion-induced damage affects the ac-
curacies of our models, we separated the damage data ac-
cording to the locations of ports (between northern Tohoku
and southern Tohoku regions). The ports of Hachinohe and
Kuji fall within the northern region, and the ports of Ishi-
nomaki, Sendai, Soma and Onahama are located within the
southern region (Fig. 10). We selected two industries to cap-
ture the effect of ground motion, instead of using the entire
dataset since it has the effect of aggregating data from differ-
ent industries, and hence neglect differences in their physical
characteristics. The manufacturing industry was considered
because of its high prediction accuracy and its large sam-
ple size. The food industry was also considered due to its
poor prediction accuracy – we wanted to examine if pre-
Figure 10. Mercalli intensities (MI) recorded by United States Ge-
ological Survey (USGS, 2020) for the Great East Japan earthquake
and tsunami. Earthquake intensities differ between the northern (MI
VI) and southern (MI VII–VIII) regions of Tohoku. North Tohoku
experiences less effects from ground shaking than in the south.
earthquake activities might explain the poor prediction abil-
ity of the fitted model.
Damage data for both industries were split into two sites
(North and South Tohoku). For each dataset, an ordinal re-
gression model was fitted, and its response was captured in
a 10-fold cross-validation. The resulting fragility models and
their mean accuracies are shown in Fig. 11. We observe that
port structures in South Tohoku tend to reach high proba-
bilities of non-structural (DS 1 and DS 2) damage at lower
inundation depths than structures in North Tohoku. This sug-
gests that earthquake damage might have weakened struc-
tures prior to the tsunami, leading to a steeper incline in
damage probabilities as compared to structures in North To-
hoku. However, at higher levels of damage (DS 3 and DS 4),
ground shaking appears to have had less influence on dam-
age. For both industries in the northern region, models depict
a smaller initial increase in damage for higher levels of dam-
age DS 3 and DS 4, but probabilities incline more rapidly at
higher inundation depths. The opposite holds true for both
industries in the southern region, i.e. damage probabilities
for DS 3 and DS 4 incline at a slower rate at higher inun-
dation depths, implying that a larger depth is required to in-
duce structural damage (DS 3) and collapse (DS 4). Ground
shaking therefore only influenced lower levels of damage;
tsunami inundation and flow characteristics still had a greater
influence on higher levels of damage.
The mean accuracies of using only datasets from North
Tohoku are significantly higher than those of South Tohoku
datasets. It appears that the aggregation of datasets from the
two environments has the effect of averaging the mean accu-
https://doi.org/10.5194/nhess-21-1887-2021 Nat. Hazards Earth Syst. Sci., 21, 1887–1908, 2021
1904 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
Figure 11. Fragility functions developed for the manufacturing industry in (a) North Tohoku and (b) South Tohoku as well as the food
industry in (c) North Tohoku and (d) South Tohoku. To evaluate the effects of preceding earthquake damage on overall damage assessment,
datasets for each industry were divided into North and South Tohoku regions. Mean accuracies for each dataset were derived using a 10-fold
cross-validation to determine if the accuracies of the fragility models are affected by the compound effect of earthquake and tsunami.
racies for the whole region (Table 5, Fig. 11). It suggests that
damage sustained by port structures in the southern Tohoku
region was influenced by the compound effects of earth-
quake and tsunami loads. Inundation depth alone is not suffi-
cient to explain the damage observed. However, as Charvet et
al. (2014) pointed out, it is difficult to distinguish the extent
to which buildings had already been affected by earthquake
damage prior to the arrival of the tsunami. Therefore, it was
difficult to separate the effects of ground motion and lique-
faction when we developed our fragility models.
There are other factors such as debris impact, the effect of
shielding and local characteristics of the built environment
that may have influenced the results observed (Tarbotton et
al., 2015). Regardless, we note that while the fragility model
developed for the food industry using only data from the
north has an improved mean accuracy, there is a substantial
increase in the uncertainty of the model (Fig. 11). It is not
surprising as wider confidence intervals are a reflection of a
limited sample size. An unbiased sample is not representa-
tive of the whole population, and therefore it is prudent that
all available samples are used to fit the fragility functions.
9 Conclusions
9.1 Main findings and limitations
We presented a first attempt to quantifying structural vulner-
ability of port industries to tsunami impacts by developing a
damage database for port structures and constructing damage
fragility functions for various port industries. We were able
to collect damage data for more than 5000 port structures and
produce damage fragility functions for eight main port indus-
tries. Through the interpretations of our damage assessment
and statistical analyses of our fragility model, a number of
significant findings have emerged from this study.
1. Energy-related and warehousing and distribution in-
dustries showed relatively higher resistance to tsunami
loads, whereas chemical, cargo handling and construc-
tion material industries appeared to be more vulnerable.
2. Using our proposed damage classification scheme, our
fragility models were able to reproduce damage with
prediction accuracies of up to 75 %, which outperforms
models created using aggregated building damage data
from MLIT (Leelawat et al., 2014).
3. Pre-tsunami earthquake activities have an influence on
port structural damage. It is unavoidable that the com-
Nat. Hazards Earth Syst. Sci., 21, 1887–1908, 2021 https://doi.org/10.5194/nhess-21-1887-2021
C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event 1905
pound effects of ground shaking and liquefaction are
captured in the damage data and unaccounted for in
the process of developing fragility functions. However,
ground shaking appears to influence building damage at
lower damage states.
We are also aware of other limitations of this study. One of
the limitations which has repeatedly surfaced in our find-
ings is that inundation depth alone is not sufficient to explain
the damage observed in port industries. Key threshold depths
were difficult to capture for all industries, which suggests that
by only using inundation depth as a predictor, the fragility
models may underestimate the levels of damage sustained by
port structures. The models can be further refined by con-
sidering other measures of damage such as other tsunami
flow characteristics (e.g. velocity, hydrodynamic force), de-
bris impacts or the effects of shielding.
9.2 Future use of the damage database and
recommendations
This study presents an array of potential applications in fu-
ture port damage studies. First and foremost, a new damage
classification scheme was proposed to characterise damage
to port structures. This scheme is transferable to other study
sites for damage assessment and can be applied to damage
assessments through ground survey, photographic interpreta-
tion, remote sensing and machine-learning techniques. Sec-
ondly, we outlined a reproducible method for damage assess-
ment in place of an actual ground survey, especially since this
assessment was performed years after the event. The manual
assessment allowed us to capture damage details from a side
profile, which otherwise would have been missing from au-
tomated techniques such as change detection in remote sens-
ing imagery. However, we note that this approach may not be
feasible where there is poor observational data available.
In addition, the damage database can also be used in fu-
ture work to investigate the influence of different parameters
such as tsunami flow characteristics and construction char-
acteristics amongst others on the damage observed. Last but
not least, our findings, quantified through the development
of fragility functions, can be used to estimate damage to port
structures in future tsunami events. They can also be used to
motivate improvement in structural designs, tsunami mitiga-
tion measures and current methods of damage assessment.
However, caution must be exercised when applying these
models outside of Japan as structural integrity differs from
place to place, though we expect that there would be less re-
gional variability for port industries as compared to building
codes in houses and commercial buildings.
We invite and provide recommendations for potential
users to expand the database and improve the predictive abil-
ity of the existing fragility models.
1. Expand the database by collecting damage data from
other events and improve the quality of the database by
providing more details on the (i) origin of the tsunami,
(ii) coastal morphological setting, and (iii) method of
data collection.
2. Perform tsunami simulation to collect other intensity
measures such as velocity and hydrodynamic force.
3. Study the performance of buildings and port infrastruc-
ture separately. This would, however, require a larger
dataset than presented in this study because fragility
models built on smaller sample sizes tend to have
greater uncertainty.
Data availability. The database provides a comprehensive inven-
tory of port structures and their associated damage in the 2011 Great
East Japan tsunami. The database is available through an unre-
stricted data repository (DR-NTU) hosted by Nanyang Technologi-
cal University (https://doi.org/10.21979/N9/OTZMT1) (Chua et al.,
2020). A database guide is provided in the Supplement.
Supplement. The supplement related to this article is available on-
line at: https://doi.org/10.5194/nhess-21-1887-2021-supplement.
Author contributions. CTC designed the study, collected all data
and information, performed all statistical analysis, and prepared the
manuscript. ADS provided direction for conceptualisation and ad-
vice on paper structure. AS provided the original MLIT damage
data and provided guidance on the development of fragility func-
tions. LL and KP provided advice on the structural response and
tsunami behaviour. DL provided advice for statistical analysis and
development of fragility functions. IC provided advice on build-
ing damage assessment and development of the damage database.
TC provided advice for statistical analysis and developed code for
bootstrapping techniques. AC assisted in the development of the
damage database. SJ and NW provided general direction of paper.
All authors contributed to the scientific discussion of the methods
and results, as well as the editing of the manuscript.
Competing interests. The authors declare that they have no conflict
of interest.
Acknowledgements. This research was supported by the Earth Ob-
servatory of Singapore via its funding from the National Research
Foundation Singapore and the Singapore Ministry of Education un-
der the Research Centres of Excellence initiative. This work com-
prises EOS contribution number 329. We are grateful for the support
and advice we have received from Paul Nunn (SCOR Global P & C)
and Nigel Winspear (formerly SCOR Global P &C). This study was
supported in part by the facilities and staff at the International Re-
search Institute of Disaster Science (IRIDeS, Tohoku University).
Special thanks go to Fumihiko Imamura, the director of the Inter-
national Research Institute of Disaster Science, for supporting and
hosting Constance Chua in IRIDeS. We would also like to thank
https://doi.org/10.5194/nhess-21-1887-2021 Nat. Hazards Earth Syst. Sci., 21, 1887–1908, 2021
1906 C. T. Chua et al.: Cataloguing damage to create fragility functions from the 2011 Tohoku event
Janneli Lea Soria, Stephen Chua and Jedrzej Majewski for provid-
ing feedback on the organisation of the manuscript.
Financial support. The project was funded by SCOR Reinsur-
ance Asia-Pacific. This work formed part of the PhD study of
Constance Chua, who received funding from the Nanyang Re-
search Scholarship. Anawat Suppasri and Kwanchai Pakoksung
were funded and supported by Tokio Marine&Nichido Fire Insur-
ance Co. Ltd. and Willis Research Network (WRN).
Review statement. This paper was edited by Mauricio Gonzalez
and reviewed by Patricio A. Catalan and one anonymous referee.
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