Linked Data for the Categorization of Failures Mechanisms in Existing Unreinforced Masonry Buildings

Abstract

Assessing the structural integrity of unreinforced masonry structures is a complex and time-consuming process that necessitates the knowledge of various experts and meticulous cross-referencing of diverse data to achieve a comprehensive understanding of the building. In recent years, the Architecture and Construction Industry has witnessed a digital transformation, largely driven by Building Information Modeling (BIM). BIM has proven immensely valuable in the conservation of historic buildings. However, while it excels in new construction projects, its full potential is not fully realized when dealing with existing structures. A clear example of this limitation can be observed in the Industry Foundation Classes (IFC) format, which lacks instances necessary for accurately representing existing building features. This research contribution aims to advance the process of semantic enrichment of BIM for existing buildings, building upon findings from existing literature. Leveraging the Linked Data Approach and utilizing both existing ontologies and newly proposed domain ontologies, the objective is to facilitate the identification of vulnerabilities and potential local failure mechanisms. The geometric information of the building is represented in the IFC STEP format and enriched semantically by establishing new relationships between classes that are not present in the standard IFC. This approach is applied to a case study in the historical center of Castelnuovo di Porto, Italy. The results of this work demonstrate how the proposed model, enhancing the BIM representation of existing buildings and enabling better identification of potential weaknesses, contributes to improved preservation and seismic resilience of historic structures