Development of Tsunami Fragility Functions for Ground-Level Roads

Maruyama, Yoshihiro; Itagaki, Osamu

doi:10.20965/jdr.2017.p0131

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…In the study, the total cost of damage was estimated at almost $40 billion, of which the main contribution, $12 billion, was damage to local roads, railways, rivers, and bridges. Maruyama and Itagaki (2017) summarized the report of the Ministry of Land, Infrastructure and Transport related to road network damage by the 2011 Tohoku-oki earthquake tsunami, in which the main damage patterns were roadway cracks, road shoulder collapse, embankment collapse, landslide, joint gaps, and asphalt detachment. Miyagi Prefecture was the most severely affected, with more than 2000 km of road exposed to the tsunami.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

et al. 2020

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Debris scattering is one of the main causes of road/street blockage after earthquakes in dense urban areas. Therefore, the evaluation of debris scattering is crucial for decision makers and for producing an effective emergency response. In this vein, this article presents the following: (1) statistical data concerning the debris extent of collapsed buildings caused by the 2016 Mw 7.0 Kumamoto earthquake in Japan; (2) an investigation of the factors influencing the extent of debris; (3) probability functions for the debris extent; and (4) applications in the evaluation of road networks. To accomplish these tasks, LiDAR data and aerial photos acquired before and after the mainshock (16 April 2016) were used. This valuable dataset gives us the opportunity to accurately quantify the relationship between the debris extent and the geometrical properties of buildings.

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Section: Introductionmentioning

confidence: 99%

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

et al. 2020

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“…It is these studies that initially warn against the general application of TFF models suggesting domain dependence 8,21,27 . The literature covers both historical and contemporary events, from the inception of TFFs between 2005 2 and 2009 1 , some prominent examples are: the 1993 Okushiri Earthquake 11 , the 2004 Indian Ocean earthquake and tsunami 1,2,12,31 , the 2009 Samoa earthquake and tsunami 10,13 , the 2010 Chile earthquake 8 , the 2011 Great East Japan earthquake (GEJE) 3,9,[14][15][16][17][18][19][20][21][22][23][24]26,27,29,32,33 which features extremely detailed survey and supporting data [34][35][36] , and the 2018 Sulawesi earthquake and tsunami 30,31,37 . Due to the lack of classifiable damage data, analytic studies usually define building damage as a function of generalized structural properties (i.e., stress and strain) 25,38 , design standards, and precedent (such as a the impact of a previous disaster).…”

Section: Tsunami Fragility Functionsmentioning

confidence: 99%

“…Due to the lack of classifiable damage data, analytic studies usually define building damage as a function of generalized structural properties (i.e., stress and strain) 25,38 , design standards, and precedent (such as a the impact of a previous disaster). TFFs have not been limited to buildings: indeed, vegetation 15 , road 33 , vessel 16 , and service pole 37 damage has been framed in the same fashion. The TFF research corpus has been reviewed several times: we point to Tarbotton et al 39 and Charvet et al 40 for the latest comprehensive reviews (up to the date of publishing).…”

Section: Tsunami Fragility Functionsmentioning

confidence: 99%

Beyond tsunami fragility functions: experimental assessment for building damage estimation

Vescovo,

Adriano,

Mas

et al. 2023

Sci Rep

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Tsunami fragility functions (TFF) are statistical models that relate a tsunami intensity measure to a given building damage state, expressed as cumulative probability. Advances in computational and data retrieval speeds, coupled with novel deep learning applications to disaster science, have shifted research focus away from statistical estimators. TFFs offer a “disaster signature” with comparative value, though these models are seldom applied to generate damage estimates. With applicability in mind, we challenge this notion and investigate a portion of TFF literature, selecting three TFFs and two application methodologies to generate a building damage estimation baseline. Further, we propose a simple machine learning method, trained on physical parameters inspired by, but expanded beyond, TFF intensity measures. We test these three methods on the 2011 Ishinomaki dataset after the Great East Japan Earthquake and Tsunami in both binary and multi-class cases. We explore: (1) the quality of building damage estimation using TFF application methods; (2) whether TFF can generalize to out-of-domain building damage datasets; (3) a novel machine learning approach to perform the same task. Our findings suggest that: both TFF methods and our model have the potential to achieve good binary results; TFF methods struggle with multiple classes and out-of-domain tasks, while our proposed method appears to generalize better.

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“…Tsunami fragility curves commonly rely on relatively large samples of empirical or modelled impact damage data, yet such quantitative empirical data for infrastructure vulnerability are rare and have only recently been a focus of post-event impact assessment (MLIT, 2012; Paulik et al, 2019Paulik et al, , 2021 and physical modelling studies (C Chen and Melville, 2015; Cheng Chen et al, 2017Chen et al, , 2018Rossetto et al, 2014). For infrastructure networks, transportation components, namely roads, bridges, utility poles and port structures, have been previously analysed for fragility function development from empirical eld surveys and physical modelling (Chua et al, 2020;Eguchi et al, 2013; Kawashima and Buckle, 2013; Koks et al, 2019;Maruyama and Itagaki, 2017;Shoji and Moriyama, 2007;Williams, et al, 2020b;Williams, et al, 2020a). However, the relative importance of network component attribute characteristics in uencing tsunami damage is understudied.…”

Section: Introductionmentioning

confidence: 99%

“…Itagaki, 2017; Williams, et al 2020b; Williams, et al 2020a) that propose, but do not test, this relationship. However, this is inconsistent with a number of studies(Song et al, 2017;Wang et al, 2020) that claim ow depth is not a reliable metric for assessing tsunami hazard and damage relationships.…”

mentioning

confidence: 99%

Infrastructure network component vulnerability to damage from the 2015 Illapel tsunami, Coquimbo, Chile

Williams

Paulik

Aránguiz

et al. 2022

Preprint

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This study presents critical infrastructure network component vulnerability in the 2015 Illapel tsunami at Coquimbo, Chile. We analyse road and utility pole vulnerability to physical damage based on modelled tsunami hazard intensity (depth, velocity and hydrodynamic force) and network component characteristics. A Random Forest Model and Spearman’s Rank correlation test are applied to analyse variable importance and monotonic relationships between tsunami hazards and network component attributes with damage levels. These models and tests revealed scour and debris impacts are highly important and strongly correlated with road and utility pole damage levels, while flow depth correlates higher with damage levels relative to flow velocity and hydrodynamic force. A cumulative link model methodology is used to develop fragility curves, The fragility curves in response to flow depth revealed roads have higher vulnerability than those analysed in previous tsunami events (e.g. 2011 Tohoku, 2018 Sulawesi, 2015 Illapel Tsunami), while utility poles demonstrate lower vulnerability. Although we identify flow depth as the hydrodynamic hazard intensity metric of highest importance for causing road and utility pole physical damage, fragility curves representing multiple hazard intensities provides a holistic curve suite for analysing infrastructure network damage and disruption from future tsunami events.

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Development of Tsunami Fragility Functions for Ground-Level Roads

Cited by 6 publications

References 6 publications

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

Statistical analysis of earthquake debris extent from wood-frame buildings and its use in road networks in Japan

Beyond tsunami fragility functions: experimental assessment for building damage estimation

Infrastructure network component vulnerability to damage from the 2015 Illapel tsunami, Coquimbo, Chile

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