Abstract
Abstract. This paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. To describe the geometric shape of a building we apply Shape-DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80 %, only relying on the Shape-DNA. The roof type of a building thereby serves as an example of a relevant feature for predicting GEM attributes, which cannot easily be identified and described by using traditional methods for shape analysis of buildings. Further research is necessary in order to explore the usability of Shape-DNA on real building data. In a second experiment we use real-world data of buildings located in the Groningen region in the Netherlands. Here we can automatically predict six GEM attributes, such as the type of lateral load resisting system, with accuracies above 75 % only by taking a buildings footprint area and year of construction into account.
Highlights
Knowledge about the seismic vulnerability of existing building stock is of vital importance in seismic risk management, e.g. for the design and development of seismic retrofit strategies
Tried to incorporate the shape features gained by Shape-DNA in the presented seismic building structural type (SBST) classification
Shape-DNA was not able to improve the classification performance in our experiments. Local shape features, such as the footprint area or the number of roof segments led to better results, provided these features can be extracted
Summary
Knowledge about the seismic vulnerability of existing building stock is of vital importance in seismic risk management, e.g. for the design and development of seismic retrofit strategies. Traditional methods to gather this information, such as building-by-building inspections, are costly and highly time-consuming, making them unfeasible for assessing large building inventory. For this reason the use of remote sensing data and ancillary geo-information has been proposed to allow a fast acquisition of SBST information on urban and regional scale. Machine learning algorithms may be used to analyse the gathered information, e.g. to classify a building stock into groups with similar SBSTs (Borzi et al, 2011, Christodoulou et al, 2017, Geiß et al, 2015, Lugari, 2014, Pittore and Wieland, 2013, Sarabandi, 2007). Existing approaches for such a workflow often deliver highly aggregated results in terms of their spatial or typological granularity, and prevent a precise seismic assessment
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