Assessing Building Habitability after an Earthquake Using Building Typology and Damage Grade. Application in Lorca, Spain

Martínez-Cuevas, Sandra; Balsera, Ma Carmen Morillo; Benito, B.; Torres, Yolanda; Gaspar‐Escribano, Jorge M.; Staller, Alejandra; García-Aranda, César

doi:10.1080/13632469.2020.1802370

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Also, the response of the building is affected by the distance between the structure and the epicentre of the earthquake or the geology. Structure typology depends mainly on local building materials, climatic condition, the socioeconomic status, as well as the geology and geography factors [3]. In Guatemala City, it is very important to describe the earthquake, as it is not the same an interphase subduction earthquake than an in-slab subduction or a crustal zone (Benito et al 2012).…”

Section: Vulnerabilitymentioning

confidence: 99%

Seismic vulnerability in Guatemala City considering the urban planning

Migoya¹,

Cabrero²,

García-Diego³

2021

1st Croatian Conference on Earthquake Engineering

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This paper proposes a list between the different variables that affect the vulnerability of a building after an earthquake. The significance of urban planning and other urban factors affecting the vulnerability of buildings have not received special attention either from the government or researchers. The lasts research of the vulnerability of buildings are Benedetti and Petrini (Inf Constr 18:66-78, 1984), the EMS-98, the Risk-EU project (2003) and Martínez-Cuevas (2020). The proposed methodology was applied to a database with information about buildings and hypothetical damage grade in Guatemala City. Guatemala City is in a high seismic hazard in the Caribbean plate and surrounded by the North American and Cocos plates (B. Benito 2012). Three main tectonic features have been distinguished around Guatemala: The subduction zone in the plate boundary Cocos-Caribbean; the local faults situated in the volcanic chain; and the Polochic-Motagua in the North America-Caribbean plate boundary. The Guatemala case is unique due to these three seismic origins: Interphase subduction related to the volcanic chain; In-slab subduction related to the Cocos-Caribbean boundary; and Crustal Zone related to the North America-Caribbean boundary. The most destructive event in the last decades in Central America was the earthquake associated with the Motagua fault, Mw 7.6, causing 22,000 deaths in Guatemala City in 1974. The most recent destructive earthquake in Guatemala was associated with the Cocos-Caribbean boundary in San Marcos, Mw 7.4, causing 45 deaths in Guatemala. The San Marcos earthquake reminds Guatemala of the importance of the earthquake code and the seismic hazard evaluation of the country. The results of the described analysis will provide useful insights for areas with seismic risk.

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Section: Vulnerabilitymentioning

confidence: 99%

Seismic vulnerability in Guatemala City considering the urban planning

Migoya¹,

Cabrero²,

García-Diego³

2021

1st Croatian Conference on Earthquake Engineering

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“…This framework operates in near real time and encompasses three distinct levels of analysis: an empirical formulation, the effective stiffness concept, and the finite element model [11]. Martínez-Cuevas et al performed statistical modeling and found discrimination metrics based on various national criteria and a ranking of features to assess the habitability of houses after earthquakes [12].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

Li,

Jia,

Chen

et al. 2023

Sustainability

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The rapid assessment of post-earthquake building damage for rescue and reconstruction is a crucial strategy to reduce the enormous number of human casualties and economic losses caused by earthquakes. Conventional machine learning (ML) approaches for this problem usually employ one-hot encoding to cope with categorical features, and their overall procedure is neither sufficient nor comprehensive. Therefore, this study proposed a three-stage approach, which can directly handle categorical features and enhance the entire methodology of ML applications. In stage I, an integrated data preprocessing framework involving subjective–objective feature selection was proposed and performed on a dataset of buildings after the 2015 Gorkha earthquake. In stage II, four machine learning models, KNN, XGBoost, CatBoost, and LightGBM, were trained and tested on the dataset. The best model was judged by comprehensive metrics, including the proposed risk coefficient. In stage III, the feature importance, the relationships between the features and the model’s output, and the feature interaction effects were investigated by Shapley additive explanations. The results indicate that the LightGBM model has the best overall performance with the highest accuracy of 0.897, the lowest risk coefficient of 0.042, and the shortest training time of 12.68 s due to its relevant algorithms for directly tackling categorical features. As for its interpretability, the most important features are determined, and information on these features’ impacts and interactions is obtained to improve the reliability of and promote practical engineering applications for the ML models. The proposed three-stage approach can provide a reference for the overall ML implementation process on raw datasets for similar problems.

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Classifying buildings according to seismic vulnerability using Cluster-ANN techniques: application to the city of Murcia, Spain

Meyers-Angulo

Martínez-Cuevas

Gaspar‐Escribano

2023

Bull Earthquake Eng

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The seismic vulnerability of a city is a degree of its intrinsic susceptibility or predisposition to sustain damage or losses stemming from seismic events. In terms of physical vulnerability, one of the most important factors for assessing seismic risk, especially, for estimating losses, is the exposure of structures, particularly those structures intended for residential use. The present article outlines a methodology for classifying residential buildings based on the structural and non-structural components that ultimately determine the building typology and control the seismic performance. The proposed methodology is divided into three steps: first, spatial data are analysed using an official database that is supplemented by remote field work to verify, validate, and identify construction typologies and urban modifiers after incorporating the new observable data. During the second step, machine learning techniques based on Two-Step cluster analysis and neural networks are used to identify building typologies, using a multilayer perceptron to assess the representativeness of the building typologies identified. Finally, each building typology is defined, a vulnerability assessment is carried out, and vulnerability classes are ranked based on the macroseismic scale. The above-mentioned steps were applied to 7631 residential buildings in the city of Murcia, Spain. The methodology is scalable and may be automated, so it may be replicated in other urban areas with similar characteristics or adapted to different urban settings. This may help save time and reduce the cost of carrying out seismic risk studies, providing valuable information for both civil protection and regional and local governments.

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Assessing Building Habitability after an Earthquake Using Building Typology and Damage Grade. Application in Lorca, Spain

Cited by 5 publications

References 15 publications

Seismic vulnerability in Guatemala City considering the urban planning

Seismic vulnerability in Guatemala City considering the urban planning

Machine Learning Assessment of Damage Grade for Post-Earthquake Buildings: A Three-Stage Approach Directly Handling Categorical Features

Classifying buildings according to seismic vulnerability using Cluster-ANN techniques: application to the city of Murcia, Spain

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