The intrinsic problems in EQ damage prognosis: the experience from a systematic evaluation of L’Aquila databases and different levels of data availability

Within the European TURNkey project, a knowledge-based exposure-modelling framework was developed, enabling the consideration of different levels of investigation and data availability. In particular, the proposed framework recognizes various levels and origins of uncertainties, as well as the completeness of a building stock catalogue. Despite substantial efforts, the main question still needs to be answered: How reliable can the developed tools and instruments be if they are not tested and validated by actual events? The L’Aquila 2009 earthquake has been the subject of several analytical strategies to enrich earthquake engineering knowledge. In this study, the information provided by the Italian Observed Damage Database is analyzed, explicitly focusing on the seismic sequence of the L’Aquila 2009 earthquake within the delimited area of the city’s historical center. A second dataset, where the European Macroseismic Scale (EMS-98) was used as a reference, is integrated into the study, and the results are compared. A methodology is implemented for a systematically evaluating the database based on the EMS-98. From the data analysis, a proposal is made to define a comparable EMS-98 building typology and to assign vulnerability classes considering optimistic, pessimistic and most likely criteria. The reliability of the sample is then explored using the knowledge-based exposure modelling framework established by the TURNkey Project. Accuracy is then evaluated through an empirical inspection of frontal (lateral) views available in Google Street View (2022). Images before and after the event are collected and compared with the available data. Intrinsic problems encountered during the process are then listed and discussed, particularly regarding the use of the database, the joint between the studied datasets, and the post-processing required to use the data for damage prognosis. This paper intends to demonstrate how reliable datasets for the building stock, including structural types and corresponding vulnerability classes, can be elaborated. Not least, exposure modelling has to transform the available data into a descriptive form that can be linked directly with the Fragility or Vulnerability Functions, expecting that these assignments are the best suited or representative ones. The data layers provided by the study enable the testing and training of exposure modelling techniques for the selected event and target region.