Implementing a Knowledge-Based Seismic Risk Assessment Approach for an Existing School in Italy

Abstract

When dealing with seismic risk assessment of existing buildings, the technical complexity arising from limited building knowledge often represents a primary obstacle, potentially leading to high levels of uncertainty. This issue is critically emphasised in large-scale applications, where a statistical characterization of the building stock is typically assumed based on a few building data. Yet, recent research in the literature has pointed out that various types of information on the structure, such as structural details and material mechanical properties, can have significant impacts on the seismic performance assessment. This would suggest that the diagnosis phase could be conducted through an incremental procedure enabling the gradual acquisition of significant information on the seismic performance of the structure, even in scenarios where data collection is limited. Moreover, when coupled with an ad-hoc data collection form, a knowledge-based seismic risk assessment approach could serve as an important step toward achieving a building-to-building characterization of the national building stock. To this end, standardised, adaptive and updatable methodologies and tools for knowledge-based seismic risk assessment of buildings are essential. As part of a wider ReLUIS research project, this paper discusses the ongoing developments in the definition and validation of a multi-knowledge seismic assessment methodology based on the SLaMA (Simple Lateral Mechanism Analysis) method. Particular focus is given to an application of the SLaMA-based procedure to an existing reinforced concrete school located in a high seismic hazard zone in Italy. To simulate a realistic “incremental” diagnosis phase, two different Research Units (RUs) are involved in the investigation: RU-1 and RU-2. Operatively, RU-1, owning relevant building data, progressively shares information with RU-2. Subsequently, RU-2 conducts a knowledge-based seismic risk assessment for each data collection scenario. The results are finally returned to RU-1, which analyses and compares them with the existing documentation. The preliminary results of the ongoing investigation are herein presented and discussed. The SLaMA-based procedure enables the identification of the expected range/domain of capacity curves and seismic risk classes from the first diagnosis phases. This information can support the decision-making in localised in-situ inspections and/or possible retrofitting solutions.