Regional based exposure models to account for local building typologies

Abstract

The development of building inventory is a fundamental step for the evaluation of the seismic risk at territorial scale. Census data are usually employed for building inventory in large scale application and their use requires suitable rules to assign buildings typologies to vulnerability classes, that is an exposure model specific for the considered vulnerability model. Several exposure models are developed proposing class assignment rules that are calibrated on building typological data available from post-earthquake survey data. However, this approach has the drawback of being based on data from specific geographic areas that have been hit by damaging earthquakes. Indeed, the distribution of building typologies can vary greatly for different areas of a country and the diffusion of one building’s typology rather than another one may depend on the availability of construction material in the area, the evolution of construction techniques and the codes in force at the time of construction. This paper aims to improve the exposure modelling at regional scale, investigating the variability of masonry building typologies distribution. It proposes a methodology to recalibrate the exposure models at regional scale and evaluates the influence of the improved characterization of regional vulnerability on damage and risk assessment. The study shows that the analysis of local building typologies may strongly impact on the evaluation of the seismic risk at territorial scale.

Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions

“…More recently, and following the Bayesian method outlined in [39], residential building exposure models of Valparaíso were modelled in [59]. These authors made use of existing expert-based prior assumptions along with only a few (~600) remote building-by-building surveys to ultimately create various posterior distributions that emulated synthetic building portfolios whose variable compositions depended on the relative degree of knowledge of the true proportions associated with each class.…”

“…Then, the number of buildings for each grid cell was estimated through the disaggregation of the redistributed population. This was performed by employing Equation (A1) in Appendix B as proposed in [39]. Figure 5 shows the resulting ranges of building counts obtained for this model.…”

“…The theoretical conceptualisation and assumptions of this model initially proposed in [50] that relied on former approaches [39,59] are presented in the Appendix D. Out of a total of 384 grid cells, 294 grid cells, for which the OSM footprint area was available…”

“…The theoretical conceptualisation and assumptions of this model initially proposed in [50] that relied on former approaches [39,59] are presented in the Appendix D. Out of a total of 384 grid cells, 294 grid cells, for which the OSM footprint area was available (Figure A2), were updated using the Bayesian approach. The resultant probabilistic exposure model gave to each geo-cell the total number of buildings classified into the six subcategories in the range from A to F. In order to complete the exposure information for the remaining 90 grid cells (~23% of the model), a hybrid model was generated by combining the generated information from the Bayesian approach along with the preliminary model that comprised a simple spatial downscaling (Section 3.2.1).…”

“…2022, 11, 113 3 of 38 (e.g., [36][37][38]). However, the integration of such bottom-up data collection within a robust statistical framework is a relatively new type of approach that was first considered in [39]. That study proposed a Bayesian method to probabilistically forecast the composition of the building stock of a given area through expert-based priors (about the expected proportions per class) that are increasingly superseded by the real data that are continuously captured during surveys and used to configure the likelihood term.…”