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Explore the physical environment features in earthquake disaster area – A case study
of 921 Earthquake in Taiwan
Abstract
Earthquake has been regarded as an infrequent and unpredictable, but fatal
disaster. Conventional mitigation measures focused on structural engineering
enhancement. However, weakness appeared by even larger earthquakes has outpaced
the ability to mitigate the impacts to acceptable levels. Non-structural engineering
measures way beyond just retrofitting have received attention. In fact, sensitive
geologic environment might result in earthquake-induced ground damage. Hence, this
study attempts to base on previous earthquake disaster to explore if there is any
similarity in such physical environment by using spatial statistic analysis and
principle component analysis (PCA). The results show that similar physical
environment features including landslide prone areas and close to fault. In addition,
some of high damage areas appear newly built buildings collapsed completely.
Although the building construction process might be one of the factors, the continued
approval in such sensitive environment might result in much serious fatalness in the
future. Therefore, the retrofitting in sensitive physical environment is not only urgent
but the avoidance new development might be important as well.
Keywords: earthquake, spatial statistic analysis, principle component analysis,
non-structural engineering measures
1. Introduction
Asian region has been regarded as most frequently hit by natural disasters,
especially for Asia is riddled with faults. Earthquakes are infrequent hazards but
unpredictable feature that result in higher fatalness (Guha-Spair et al., 2010). In fact,
earthquake don’t kill people, buildings do (Ambraseys, 2010). Therefore,
conventional ways to mitigate earthquake disaster are to enhance buildings codes and
structural engineering measures. However, weakness appeared in structural
engineering measures such as risk of loss happened wherever development is allowed
in hazardous areas, or the disaster beyond design standard might have much serious
impacts on lives and properties. The threat posed by even larger earthquakes has
outpaced the ability to mitigate the impacts to acceptable levels. People start to be
aware that such disaster can be revised by humans but is not ultimately reducible to a
human construction.
Since the 1980s, non-structural measure such as land use, insurance, warning
system way beyond retrofitting of seismic damages, seismic resistance, and better
anti-seismic structures in both urban planning and architecture has received lot more
attention in the worldwide (CENDIM et al., 2001; Coburn and Spence, 2002). Indeed,
building is the major object to mitigate such serious disaster but should base on
different perspective. When buildings located nearby geographical sensitive areas
such as loose saturated sands and deep deposits of soft clays might result in
earthquake-induced ground damages such as surface rupture, liquefaction, landslides,
and land subsidence easily. In fact, many other studies started to discuss the potential
relationship between earthquake hazard and some particular factors in earthquake
prone countries such as Greece and New Zealand (Athanasopoulou et al., 2008; Ansal
et al., 2011; Becker et al., 2013).
Multiple land use regulations proposed on development including prohibition of
development in high-hazard areas, low-density zoning to limit building intensity in
hazardous areas, density bonus in return for reduced density in areas subject natural
hazards, reduced property tax to encourage more open spaces, transfer of
development rights (Burby and Dalton, 1994). However, it is difficult for zoning
boards or governing bodies to identify such high-hazard areas for lacking of accurate
earthquake forecast. Consequently, development continues in the potential path of
intense ground shaking, and ground failures, and existing development in these areas
remains at risk (Committee to Develop a Long-Term Research Agenda for the
Network for Earthquake Engineering Simulation (NEES), 2003).
Therefore, this study attempts to base on previous earthquake disaster to explore
if there is any similarity in such physical environment and building feature. There are
two kinds of methods are used in this study, spatial statistic analysis and principle
component analysis (PCA). Location-specific characteristics such as landslide prone
area, close to fault, and others are crucial to such large magnitude disaster area
(Sklenicka et al., 2013). The application of spatial statistic analysis is to depict and
explain how those damage buildings distributed in the space based on similar
neighboring values (Tobler, 1970; Goodchild, 1986). Afterwards, PCA is then applied
to categorize the similar features in different kinds of clustered patterns. The
incorporation of both spatial statistic analyses and PCA into land use planning policy
can provide more accurate information to allocate or prevent development in certain
areas.
Taiwan locates on the Pacific Ring of Fire where a seismically active zone is (the
frequent convergence of the Philippine Sea plate and the Eurasian Plate), and
geologists have identified forty-two active faults. Currently, a fault zone area 15
meters on each side of fault trance has been executed in Chelungpu Fault only in
Taiwan. An improved understanding of the similarities of the physical environment in
earthquake disaster area is able to help for recognizing earthquake prone areas for
further building retrofitting and land allocation in the future. Therefore, this study
attempts to apply spatial statistic analyses and principle component analysis (PCA) to
explore spatial distribution pattern and similar physical environment and building
features. In the next section discusses the conceptual model, variable definition, and
methods. The following sections are the result of spatial statistic analyses on damage
buildings and principle component. This paper concludes with a comparative study of
physical environment and building feature in previous earthquake disaster area.
2. Research design and methodology
2.1 Conceptual model
After 921 Chi-Chi Earthquake, over 2,400 people were killed, 10,000 people
injured, and 100,000 buildings damage. Due to the epicenter, fault dislocation and
ground deformation, huge live and property losses were aggregated in the central
Taiwan, and 5,213 damage buildings are used in this study. The application of two
spatial statistic analyses is to probe into if there is any significant cluster pattern on
particular distance. Afterwards, Principle component analysis (PCA) will then be
applied to categorize particular features of damage buildings. (See Fig.1)
921 Earthquake
Disaster
Buildings damage:
5,213
Spatial Distribution
Pattern
Cluster
Distance
Cluster
Pattern
Buffer
High-High
High-Low
Physical Environment
Low-High
Low-Low
Building Features
Soil
Landslide
Height
Age
Fault
mudslide
Material
Use
Structure
Façade
Vegetation
Fig. 1 Conceptual model
2.2 Variable definition
2.2.1 Physical environment
The variables selected in physical environment are to assess the natural
environmental conditions, and the variables include landslide risk area, land cover,
soil type, fault, river, and debris flow stream. In each category, multiple variables are
selected based on this study including high/moderate/others landslide risk, bare land,
vegetation lane, colluvium, alluvium, debris flow stream, fault, and river. (See Table
1)
Table 1 Variables in physical environment
Variables
Definition
The variables to forecast landslide include
slope, earthquake, geology, and ground
structure etc. The landslide risk area can be
Landslide risk area
divided into three categories: high,
moderate, and others.
Bare land includes falling rock, slide, and
Bare land flow etc.
Land
cover
Vegetation Vegetation land includes grass, crops, and
land
trees etc.
Colluvium is composed of a heterogeneous
Soil type Colluvium
range of rock types and sediments.
Source
Central
Geological
Survey, MOEA
National Land
Surveying and
Mapping Center
Taiwan
Agricultural
Alluvium
Debris flow stream
Alluvium refers to loose, unconsolidated
soil or sediments, and the composition is
ranging from silt, clay, and sand etc.
A monitor database of landslide, complex
landslide, debris flows and soil erosion etc.
Fault
Forty-two active fault traces have been
identified.
River
River is a flowing watercourse toward
ocean, sea, and lake etc.
Research
Institute, COA
Soil and Water
Conservation
Bureau, COA
Central
Geological
Survey, MOEA
Water Resources
Agency, MOEA
2.2.2 Building features
Architecture and Building Research Institute (1999) conducted a report particular
on damage building after 921 Chi-Chi Earthquake. The survey included building
height, building year, construction material, building use, anti-earthquake structure,
and façade pattern. (See Table 2)
Table 2 Variables is building features
Variables
Definition
According to Building Code and
(1) ≤ 3 (floors)
Building
Regulations, the construction structure
(2) 4-6 (floors)
height
is related to the height.
(3) ≥ 7 (floors)
Building
(1) Before 1974
Different building time refers to
year
different period of the improvement in
(2) 1975-1982
earthquake resistance requirement in
(3) 1983-1989
Building Code and Regulations.
(4) 1990-1997
(5) After 1997
Construction (1) Reinforced concrete (RC) The Manual of Structure Repairment
Material
and enhancement has explained how
(2) Steel frame/ Steel RC
different material should be improved.
(3) Brick/Wood/others
Building use (1) Residential
The use of building is related to the
capacity it is. Generally, public use
(2) Mixed use
such as hospital and school etc. might
(3) Public use
have more people in the daytime.
(4) Industrial use
Residential has relatively lower people
but in the nighttime.
Anti-earthquake structure
Anti-earthquake structures include
special moment – resisting frame,
diagonal bracing frame, shear wall,
and brick wall.
Façade pattern
Façade patterns include Arcade, high
ceiling, arcade without pillar, and
second floor setback.
Resource: Architecture and Building Research Institute, 1999
2.3 Methods
2.3.1 Spatial autocorrelation analysis
Spatial autocorrelation analysis often applies Moran’s I to comparing factors of
neighboring areal units, and similar values among neighboring units indicates a strong
positive spatial autocorrelation, and vice versa. Basically, the concept of spatial
autocorrelation analysis is based on Tobler’s (1970) statement that everything is
related but near things are more closely related (his “First Law of Geography”).
Moran’s I can be defined as
𝐼=
𝑛 ∑ ∑ 𝑤𝑖𝑗 (𝑥𝑖 −𝑥̅ )(𝑥𝑗 −𝑥̅ )
𝑊 ∑(𝑥𝑖 −𝑥̅ )2
…………(1)
where 𝑥𝑖 and 𝑥𝑗 are the values of variables in areal unit i and unit j; 𝑥̅ is the
mean value of variables in all spatial units; 𝑤𝑖𝑗 is the spatial weights matrix; (𝑥𝑖 −
𝑥̅ )(𝑥𝑗 − 𝑥̅ ) is cross –product of the variances between neighboring values and the
overall mean; W is the sum of all elements of the spatial weights matrix.
The value of Moran’s I ranged from -1 to 1. -1 indicates an extremely negative
spatial autocorrelation while 1 is extremely positive autocorrelation. In order to detect
the spatial autocorrelation, it should be compared to the expected value of Moran’s I:
𝐸(𝐼) = −(1)/(𝑛 − 1) …………(2)
E(I) is always negative for E(I) is inversely related to the areal units. I > E(I)
indicates a clustered pattern for similar features in adjacent areal units; I ≅ E(I)
indicates random pattern for no particular patterns or similarity; I < E(I) indicates a
dispersed pattern for different features in adjacent areal units.
2.3.2 Principal component analysis (PCA)
Principal component analysis (PCA) is a transformation process from correlated
variables to uncorrelated variables through orthogonal linear transformation. It is a
useful approach to explore patterns within multivariate data set (Sun et al., 2009; Abdi
and Williams, 2010; Shi, 2013). Principle components will then be designed
according to variance. Normally, the first principle component represents the largest
variance, and the variance will be decreased afterwards. The formula of PCA is as
follows:
𝑈1 = 𝑙11 𝑥1 + 𝑙12 𝑥2 + ⋯ + 𝑙1𝑝 𝑥𝑝
𝑈2 = 𝑙21 𝑥1 + 𝑙22 𝑥2 + ⋯ + 𝑙2𝑝 𝑥𝑝
…………(3)
…
{𝑈𝑚 = 𝑙𝑚1 𝑥1 + 𝑙𝑚2 𝑥2 + ⋯ + 𝑙𝑚𝑝 𝑥𝑝
where n refers to spatial units, refers to the number of variables, 𝑥𝑝 refers to
the original variables, and 𝑈𝑚 refers to principle components. 𝑈1 , 𝑈2 , …, 𝑈𝑚 𝑚 ≤
𝑝 are linear combinations of 𝑥𝑝 .
Generally, there is a ranking of principle components based on the eigenvalues.
According to the mathematical transformation result, there will be p variables account
for the total variability. The number of principle components is subjective to the study
itself and how much interpretation can be achieved (Srivastava, 2002). SPSS (version
17.0) is used to reorganize multi correlated variables according to varimax rotation,
Kaiser criteria (eigenvalues>1), and stepwise exclusion approach. In the end, the
model will then be tested significance based on Kaiser-Meyer-Olkin (KMO) and
Barlett’s tests.
3. Spatial distribution of damage buildings
Chi-Chi earthquake occurred in 1999 and generated 30 seconds of extremely
strong shaking. The epicenter was at the middle of Taiwan. The Chelungpu Fault, an
active fault trace, breaks along the western edge of the central mountains. The
north-south trending fault ruptured for over 80 kilometers. Tectonic warping, or
folding, associated with the faulting caused additional upward ground deformations of
6 to 7 meters, particularly in the northern reached of the rupture. Near the fault trace
and to the east of the rupture zone had higher ground motions than to the west of the
rupture zone. In addition, Chi-Chi earthquake has caused further extensive ground
deformations such as landslides and liquefaction. The Chelungpu Fault generated
more than 1,800 landslides throughout the central mountain region. Two significant
landslides, one has swept away an entire village, and another one has dammed up a
river and become an artificial lake. Due to soil liquefaction, ground subsidence
contributed to some bridge damage, and ground settlement caused 300 houses damage.
Overall, there were 2,400 people killed, 10,700 people injured, over 8,500 buildings
destroyed, 6,200 buildings seriously damaged, 100,00 people become homeless, and
economic loss estimated to $10 to $12 billion. (See Fig.2)
Fig. 2 Study area
There was total 5,213 buildings damage during the earthquake in the center of
Taiwan. The lower buildings got serious damage for it is popular to live in detached or
attached houses. Relatively, the less high-rise buildings the less damage in this area.
Older buildings, RC buildings and other material (such as brick, wood, corrugated
steel shack) had much serious damage for the previous building technique might not
strong enough to against earthquake. Residential is the most popular land use type so
no doubt it had the highest amount damage. Most buildings did not have
anti-earthquake structure or even they had the resisting frame was still not strong
enough. Arcades is the building with poorly designed columns that failed at the first
floor easily. (See Table 3)
Table 3 Building features of damage buildings
Height (floors)
1-3
4-6 >7
Serious damage
Moderate damage
Slight damage
3,194
794
739
203
94
65
44
38
42
Before
1974
1,814
230
165
Building Year
1974198319901982
1989
1997
579
343
264
272
152
147
240
151
157
After
1997
124
56
72
Table 3 Building features of damage buildings (cont.)
Serious
damage
Moderate
damage
RC
1,782
550
Material
SRC Others
16
1,388
9
313
Residential
2,980
Mixed
247
766
99
Use
Public Use
101
37
Industrial
37
9
Slight
damage
502
5
299
732
65
30
6
Table 3 Building features of damage buildings (cont.)
Serious
damage
Moderate
damage
Slight
damage
Anti-earthquake structure
Resisting
Diagonal Shear Brick
Frame
Frame
Wall
Wall
353
2
77
1,552
Arcade
478
High
Ceiling
39
Facade
No Pillar
Arcade
46
2F
Setback
16
156
1
36
507
253
22
21
4
152
0
30
491
211
18
11
6
In this study, the first step is to explore how damage buildings distributed in the
space. The application of the incremental autocorrelation analysis might help to
determine the cluster pattern at an appropriate scale: z-score returned. In the result,
there are multiple peaks happened represented clustering are most pronounced, and
the first z-score returned is 600 meter. (See Fig.3) 600 meter will then be applied to
buffer range.
The incremental autocorrelation analysis is global statistic for it is a summary
measure of the entire study area. However, the magnitude of spatial autocorrelation
might not necessarily in consistency over the region. The application of local
indicators of spatial autocorrelation (LISA) is able to capture the spatial heterogeneity
of spatial autocorrelation (Anselin, 1995). The local Moran’s I captures how
neighboring values are associated with each other.
Fig. 3 Spatial autocorrelation by distance
Fig. 4 Result of local indicators of
spatial association
The result of LISA shows that 470 spatial units are High-High (high values of
damage clustered), 346 spatial units are High-Low (high values of damage
surrounded by low values of damage), 152 spatial units are Low-High (low values of
damage surrounded by high values of damage), and 833 spatial units are Low-Low
(low values of damage clustered). Other damage buildings are distributed randomly in
the study area. (See Fig. 4 and Table 4)
Table 4 The spatial distribution of damage buildings according to LISA
High-High
High-Low
Low-High
Low-Low
Serious damage
470
346
0
0
Moderate damage
0
0
46
262
Slight damage
0
0
106
571
4. Results
Principle component analysis is used to categorize variables in physical
environment and building feature. The results show that Kaiser-Meyer-Olkin (KMO)
and Barlett’s tests are significant in High-High, High-Low, and Low-Low category
but except Low-High category.
4.1 Physical environment
For the High-High category, the PCA of twelve indicators extracted four
components that explained 69% of the variance and 0.678 of the KMO value in the
data. The indicators “high landslide risk”, “moderate landslide risk” and “colluvial”
show high positive correlation in HH_PC1 and explained 32% of the variance.
HH_PC1 is renamed “serious landslide prone area.” The second principle component
HH_PC2 explains 14% of the variance with the indicators “fault distance” and
“mudslide stream distance.” HH_PC2 is renamed “close to fault and mudflow.” The
indicators “vegetation” is highly positive in HH_PC2 and explained 12% of the
variance. HH_PC3 is renamed “soft ground surface.” The fourth principal component
HH_PC4 explains 12% of the variance with the indicators “uncovered” and “mudslide
stream distance.” HH_PC4 is renamed “mudslide prone area.” (See Fig. 5)
(a) HH_PC1: Serious landslide prone area
(b) HH_PC2: Close to fault and mudflow
(c) HH_PC3: Vegetated ground surface
(d) HH_PC4: Mudslide prone area
Fig. 5 Principle components of physical environment in High-High category
For the High-Low category, the PCA of twelve indicators extracted three
components that explained 74% of the variance and 0.669 of the KMO value in the
data. The first principle component shows high positive correlation with “high
landslide risk”, “moderate landslide risk”, “low landslide risk”, “colluvial” and
“mudslide stream distance” explained 40% of the variance. HL_PC1 is renamed
“serious landslide prone area”. The indicators “fault distance” and “river distance”
show high positive correlation in HH_PC2 and explained 21% of the variance.
HH_PC2 is renamed “close to fault and river.” HH_PC3 explained 13% of the
variance with the indicators “moderate landslide risk” and “alluvial.” HL_PC3 is
renamed “moderate landslide prone area.” (See Fig. 6)
(a) HL_PC1: Serious landslide prone area
(b) HL_PC2: close to fault and river
(c) HL_PC3: Moderate landslide prone area
Fig. 6 Principle components of physical environment in High-Low category
For the Low-Low category, the PCA of twelve indicators extracted three
components that explained 67% of the variance and 0.610 of the KMO value in the
data. LL_PC1 explains 32% of the variance with the indicators “high landslide risk”,
“moderate landslide risk”, “low landslide risk”, “colluvial” ,and “mudslide stream
distance.” LL_PC1 is renamed “serious landslide prone area.” The second principle
component explains 20% of the variance with the indicators “high landslide risk”,
“low landslide risk” and “vegetation.” LL_PC2 is renamed “moderate landslide prone
area.” The indicators “vegetation” and “alluvial” are positive correlated and explains
15% of the variance. LL_PC3 is renamed “vegetated ground surface.” (See Fig.7)
(a) LL_PC1: Serious landslide prone area
(b) LL_PC2: Moderate landslide prone area
(c) LL_PC3: Vegetated ground surface
Fig. 7 Principle components of physical environment in Low-Low category
4.2 Building feature
For the High-High category, the PCA of six indicators extracted two components
that explained 47% of the variance and 0.640 of the KMO value in the data. HH_PC1
represents 29% of the total variance, and the indicators “year” and “anti-earthquake
structure” show a high positive significance. In order to investigate how “year” affect
High-High category, the PCA of five indicators extracted two components that
explained 54% of the variance and 0.210 of the KMO value in the data. Although the
KMO value is too low, the two components reveal the building feature in “1974-1982”
and “1983-1989.” HH_PC2 represents 17% of the total variance, and the indicator
“material” is highly positive. In order to investigate how “material” affect High-High
category, the PCA of three indicators extracted one component that explained 61% of
total variance and 0.448 of the KMO value. Although the KMO value is not
significant enough, the component reveal the building feature in “reinforced concrete.”
(See Fig. 8)
(a) HH_PC1: Structure feature
(b) HH_PC2: Material
Fig. 8 Principle components of building feature in High-High category
For the High-Low category, the PCA of six indicators extracted two components
that explained 45% of the variance and 0.636 of the KMO value in the data. HL_PC1
represents 27% of the total variance, and the indicators “year” and “anti-earthquake
structure” show a high positive significance. Another PCA is applied to the indicator
“year,” and two components that explained 53% of the variance and 0.127 of the
KMO value. Although the KMO value is too low, the two components reveal the
building feature in “before 1974” and “after 1997.” HL_PC2 represents 17% of the
total variance, and the indicator “use” is highly positive. The PCA of four indicators
extracted one component that explained 45% of total variance and 0.232 of the KMO
value. Still, the KMO value is not significant enough, but the component reveal that
building feature in “mixed use.” (See Fig. 9)
(a) HL_PC1: Structure feature
(b) HL_PC2: Use type
Fig. 9 Principle components of building feature in High-Low category
For the Low-Low category, the PCA of six indicators extracted two components
that 41% of the variance and 0.581 of the KMO value in the data. LL_PC1 represents
19% of the total variance, and the indicator “Year” is highly positive. In addition, the
PCA of five indicators extracted two components that explained 47% of total variance
and 0.105 KMO value in the data, and the two components reveal the building feature
in “1994-1997” and “after 1997.” LL_PC2 represents 22% of the total variance, and
the indicators “material” and “anti-earthquake structure” are moderately positive. The
PCA of three indicators extracted one component that explained 64% of variance and
0.442 KMO value, and the component reveal the building feature in “other material
(brick, wood, corrugated steel shack).” (See Fig. 10)
(a) LL_PC1: Building year
(b) LL_PC2: Structure feature
Fig. 10 Principle components of building feature in Low-Low category
5. Comparative study of physical environment and building feature
According to the PCA results, there are similar physical environment features
including “serious landslide prone area”, “moderate landslide prone area”, “close to
fault/river/debris flow steam”, and “vegetated ground area”. In “serious landslide
prone area”, the High-High area locates in the eastern area and aggregated
significantly in the northern and southern part. Comparing to “serious landslide prone
area,” “moderate landslide prone area” is located in the western area. In addition,
“Low-Low” area is located in the periphery of “High-Low” area. Due to building
damage in “High-Low” area is quite serious (see Table 4), both “serious landslide
prone area” in High-High area and “moderate landslide prone area” in High-Low area
appear similar building damage feature. Although the newest buildings are not as
many as the oldest category, the serious damage indicates that the poor design or
inappropriate development might be the main reason. Therefore, both “serious
landslide prone area” in High-High areas and moderate landslide prone area“ in
High-Low areas should be implemented building investigation on both oldest and
newest buildings. In addition, the future development should require geology
investigation and structure engineering report before approving building permission.
(See Fig. 11 and 12)
Fig. 11 Serious landslide prone area
Fig. 12 Moderate landslide prone area
The north-south trending faults ruptured over 80 kilometers, and both the
hanging wall of the fault moved westward and upward by 1 to 2 meters and serious
ground deformation of 6 to 7 meters impacted seriously in the east of the fault
(Chi-Chi Reconnaissance Team, 2000). Due to the serious surface rupture and ground
deformation, the east becomes much sensitive than the west. Therefore, “vegetated
ground surface” is way beyond serious damage in High-High area than in Low-Low
area. In addition, the distance to the fault indeed results in serious building damage,
and the distance might be varied. Currently, the fault zone area in 15 meters of each
side of fault trace might not be enough, and it is necessary to have a complete review
on adequate fault zone area in the future.
Fig. 13 Close to fault or river
Fig. 14 Vegetated ground surface
In the end, this study overlays “serious landslide prone area” in both High-High
areas and High-Low areas and “close to fault” latest built environment. The results
show that there are large amount of existing buildings located in these earthquake
prone areas in the northern area. In order to prevent next serious earthquake hit this
area, it is necessary to implement building investigation and a retrofit program is also
urgent. In addition, the future development should be more deliberate in such area.
(See Fig. 15)
Fig. 15 Built area in 2010
6. Conclusion
Earthquake is infrequent but unpredictable disaster, and even larger magnitude
has outpaced the ability of human beings to mitigate. Land use regulation and zoning
have been discussed quite a while as nonstructural engineering measures, and they
have been implemented mostly in fault zone area to prevent the surface rupture
disaster. However, earthquake induced disaster are more than surface rupture such as
liquefaction, landslide, land subsidence and so on. The results in this study show that
there are similar physical environment features and way beyond the fault itself.
Besides, the newest structure followed the newest Building Code and Regulations but
defeated in the end. The continued approval development in such sensitive geological
or earthquake prone areas might result in another fatalness disaster in the future.
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