E3S Web of Conferences 415, 05025 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341505025

Development of Debris flow Vulnerability Curve for Data-driven
MethodChang-Ho Song1, Ji-Sung Lee1, Ho-Hong Duy Nguyen1, Yong-Soo Ha1 and Yun-Tae Kim1*
1Department of Ocean Engineering, Geo-systems Engineering Laboratory, Pukyong National University, Busan 48513, Republic ofKorea

Abstract. Various landslide disasters due to abnormal climate are increasing all over the world, and theinflow of debris flow is causing great damage to human life and social infrastructure. As such, in order topreemptively respond to debris flow, it is necessary to conduct a vulnerability assessment of the hazardous
area based on the vulnerability curve. Therefore, in this study, the hazard intensity (depth, velocity, andimpact pressure of debris flow) was analysed by performing back analysis using the DAN3D numericalmodel for 27 debris flow disaster areas in Korea from 2011 to 2020. And the vulnerability curve for thebuilding was developed through the relationship between the degree of damage to the building and theimpact pressure obtained through the case analysis of debris flow disasters.

1 Introduction
Due to recent climate change, the probability of slope
disasters is increasing due to many typhoons andtorrential rains. In particular, in Korea, most of therainfall is concentrated between June and September,and a lot of debris flow occurs. Therefore, in order toreduce damage caused by debris flow and to take apreemptive response, it is necessary to analysis thehazard intensity of debris flow with high reliability anddevelop a vulnerability curve.
In general, in order to evaluate the vulnerability to asediment disaster, an area with a high probability ofoccurrence of a sediment disaster is identified, andsediment flow analysis and vulnerability curve are used.
Currently, many researchers have conducted research
related to the vulnerability curve through risk intensity
analysis based on debris flow disaster cases [1-3].
In the case of Korea, the proportion of mountainous
areas is high and the population is concentrated along
the mountain boundaries. In addition, from the past to
the present, many developments have been made in thevicinity of mountainous areas to solve the housing
shortage and transportation, which is more exposed to
the risk of landslides. In particular, in 2020, manylandslide disasters occurred nationwide due to intensive
rainfall between July and August. Of these, in August,accumulated rainfall of about 300 to 500 mm nationwide
caused many landslides, and many houses exposed to
the risk of landslides suffered damage. Therefore, in thisstudy, As shown Figure. 1, a risk intensity analysis wasperformed targeting 27 debris flow from 2011 to 2020,
and a vulnerability curve was developed by analysing
the relationship with the damage level of the damagedbuilding.

Fig. 1. Location map of study area

2 Methodology
This study is divided into 3 steps. First, perform spatial
data analysis of the debris flow disaster area. And by
performing the numerical method through topographic
analysis, back analysis of the debris flow disaster area
and the hazard intensity(flow depth, flow velocity,impact pressure) of the disaster area are analysed.
Second, the degree of damage to the damaged building
and the type of building(RC / Non-RC) are analysedthrough Field survey or photos of the debris flow
disaster. Finally, through the relationship analysis ofstep 1 and step 2, a vulnerability curve according to thetype of building is derived. Also develop Vulnerability
function.

* Corresponding author: yuntkim@pknu.ac.kr© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 05025 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341505025

and depth of the debris flow actually observed wereverified.
When a debris flow disaster occurred in Mt. Umyeon in
2011, the velocity was 28 m/s and the depth was 3 to 4m in the Raemian apartment, and the velocity was 18m/s and the depth was 2 to 3 m in the Shindong-aapartment. As a result of comparative analysis of theobserved values and the intensity of debris flow
analysed through inverse analysis, both Raemian
Apartments and Shindonga Apartments gave similarinterpretations to the measured values. Therefore, thevelocity and depth of the debris flow were analysedthrough the back analysis.

Fig. 2. Method of this study.

2.1 Topographic analysis
The topographic characteristics were produced with aresolution of 5m by obtaining a 1:5,000 numerical map
provided by the National Geographic Information
Institute (NGII). First, the DEM data were produced
from the numerical map, and the slope was produced
through topographical analysis. And the soil depth wasconstructed by extracting it through the universally used
S-model. And the initiation volume, final volume,
runout distance, propagation zone was establishedthrough comparative analysis of aerial images before
and after the occurrence of debris flow.

Fig. 3. Numerical method results in Umyeon Mt..

Finally, an important hazard intensity in theVulnerability curve is the impact pressure. The impact
pressure is usually calculated using (3) equation. [4]suggested an empirical coefficient through anexperiment as shown in the figure, and calculated theimpact pressure through the following (4) equation.
𝑃𝑠 = α𝜌𝑠 𝑣 2

2.2 Numerical method

α = 5.3𝐹𝑟(3)(4)

where 𝑃𝑠 impact pressure(kPa), α isempiricalcoefficients, 𝜌𝑠 is the density of the debris flow
(kg/𝑚3 ), 𝑣 is debris flow velocity.

There are various models as commercial programs
used for the analysis of debris flow. However, in thisstudy, the 3D topography and entrainment action can beconsidered, and the DAN3D program that provides 5
rheological models was selected. In DAN3D, in order toconsider the entrainment (1) equation. it is assumed thatthe volume increases exponentially according to thedistance the debris flow. And the frictional model isused, and two parameters are required: friction angle
and pore water pressure ratio as shown in the following(2) equation. Input variables were calculated based onfield surveys and data provided by the National Instituteof Agricultural Sciences.
̅ = 1n(vf ∕v0)Es̅
𝜏𝑧𝑥(𝑧=𝑏) = −𝜎𝑧(𝑧=𝑏) (1 − 𝛾𝑢 )(1 − 𝛾𝑢 )(𝑧=𝑏) 𝑡𝑎𝑛∅32−(1)(2)Fig. 4. Relationships between Froude number and empiricalcoefficients(Cui et al., 2015).

2.3 Hazard intensity analysis
In order to secure the reliability of the risk intensity
analysis of debris flow through reverse analysis, Fig. As
shown in Fig. 4, reverse analysis was performed on thearea where the debris flow occurred, and the velocity

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E3S Web of Conferences 415, 05025 (2023)
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2.4 Analysis of building damage

3.2 Vulnerability curve

The evaluation of the physical vulnerability of abuilding due to a debris flow disaster can be evaluatedthrough a vulnerability curve that contains information
on factors that cause debris flow and the degree ofdamage to the building. The vulnerability index for abuilding means the degree of damage to the building,and has a value between 0 and 1 depending on the degreeof damage to each building.
For the value of the vulnerability index corresponding
to the damage to the building, the relationship of thevulnerability index to the degree of damage to thebuilding was referred to in Table 1.

In general, debris flow disaster vulnerability curve isexpressed in the form of an S-shaped sigmoid function
by applying a regression model. In many previousstudies, the vulnerability curve was developed using
linear and nonlinear regression models [1-3, 8-9]. In thisstudy, vulnerability curve was developed using theregression model with the highest coefficient ofdetermination by comparing a total of six linear andnonlinear regression models
Overall, for each structural system, the same highcoefficient of determination was derived in the order of
Avrami equation, Logistic, Weibull, Exponential,
Power law, and Univariate. Based on these results, theAvrami equation, which has the greatest statisticalsignificance, was used in the development of the debris
flow vulnerability curve in this study(Fig. 6).

Table 1. Classification of damage to building caused bydebris flow [5-6].
Damage class(Vulnerability index)
Complete(0.8-1.0)
Extensive(0.6-0.8)
Moderate(0.3-0.6)
Slight(0.1-0.3)

Damage description
Partly or totally destructed,
evacuation necessary, complete
reconstruction
Partly destructed, loss of parts ofexternal and internal walls,
evacuation necessary, reconstructionof destructed parts
Cracks in the wall, stability notaffected, reparation not urgent,
flooding of the internal rooms anddamage to the furnishing
Slight non-structural damage,stability not affected, furnishing or
fitting damaged

3 Results and discussion
3.1 Hazard intensity

Fig. 6. Debris flow vulnerability curve.

The hazard intensity (flow depth, flow velocity)
analysed through DAN3D was analysed. In order toapply the empirical coefficient proposed by Cui et al.,2015, Froude-numbers for 27 debris flow disaster werecalculated. Froude-number calculated through the backanalysis was calculated in the range of 0.6 ~ 4.74, and it
was found that it was included within the range as aresult of fitting with the existing cases [7]. Therefore,
the impact pressure of 79 buildings was calculated
through the empirical coefficients.

4 Conclusions
The frequency of occurrence of landslides around theworld is increasing, and the scale is gradually increasing
accordingly. Therefore, in order to prevent damage
caused by landslides, it is necessary to preemptively
respond to technology.
In this study, 27 cases of location data that caused
damage to buildings due to landslides from 2011 to 2020
were collected to develop a vulnerability curve througha data-based method.
First, topographic and physical characteristics wereestablished for the analysis of debris flow disaster, andthe hazard intensity of debris flow was analysed throughback analysis. And from the analysis of the correlation
between the analysed impact pressure and the degree ofdamage to the building, a vulnerability curve wasderived through a data-driven method.
Based on these results, it is judged that it will bepossible to prepare an advanced-level response systemby providing accurate information such as vulnerability
determination, improving response velocity, andpreparing a disaster response system by establishing its
own evaluation index.

Fig. 5. Relationships between Froude number and empiricalcoefficients.

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E3S Web of Conferences 415, 05025 (2023)
DFHM8

https://doi.org/10.1051/e3sconf/202341505025

This work is supported by the Korea Evaluation
Institute of Industrial Technology(KEIT) funded by the
Ministry of Public Administration and Security grant(No. K_G012001951201).

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