View VULMA: Data Set for Training a Machine-Learning Tool for a Fast Vulnerability Analysis of Existing Buildings

Angelo Cardellicchio, Sergio Ruggieri, Valeria Leggieri and Giuseppina Uva
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Angelo Cardellicchio: Institute for Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy, Via Amendola, 122 D/O, 70126 Bari, Italy
Sergio Ruggieri: DICATECH Department, Polytechnic University of Bari, Via Orabona, 4, 70126 Bari, Italy
Valeria Leggieri: DICATECH Department, Polytechnic University of Bari, Via Orabona, 4, 70126 Bari, Italy
Giuseppina Uva: DICATECH Department, Polytechnic University of Bari, Via Orabona, 4, 70126 Bari, Italy

Data, 2021, vol. 7, issue 1, 1-14

Abstract: The paper presents View VULMA , a data set specifically designed for training machine-learning tools for elaborating fast vulnerability analysis of existing buildings. Such tools require supervised training via an extensive set of building imagery, for which several typological parameters should be defined, with a proper label assigned to each sample on a per-parameter basis. Thus, it is clear how defining an adequate training data set plays a key role, and several aspects should be considered, such as data availability, preprocessing, augmentation and balancing according to the selected labels. In this paper, we highlight all these issues, describing the pursued strategies to elaborate a reliable data set. In particular, a detailed description of both requirements (e.g., scale and resolution of images, evaluation parameters and data heterogeneity) and the steps followed to define View VULMA are provided, starting from the data assessment (which allowed to reduce the initial sample of about 20.000 images to a subset of about 3.000 pictures), to achieve the goal of training a transfer-learning-based automated tool for fast estimation of the vulnerability of existing buildings from single pictures.

Keywords: data set; seismic vulnerability; deep learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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