Introduction:
Adaptive reuse of historic buildings is essential for sustainable urban development but requires a systematic way to balance structural viability with heritage constraints. This paper presents a formal ontology to address these complex engineering challenges.
Purpose:
The purpose is to provide a “logical source of truth” for multidisciplinary design teams, allowing for the validation of renovation plans against explicit engineering rules and heritage regulations within an HBIM (Historic Building Information Modelling) framework.
Scope:
The scope focuses on physical building components (Foundations, Masonry Walls), material conditions, observed defects (Findings), and proposed interventions (Strengthening, Moisture Mitigation).
Intended Users & Intended Use:
Users: Multidisciplinary design teams, including structural engineers, heritage consultants, and conservation specialists. Use: The ontology is used for automated consistency checking, structural defect prioritization, and as a reasoning engine to support complex decision-making in digital twin environments.
Class Hierarchy:
The hierarchy is decomposed into physical components (BuildingComponent), abstract results from inspections (Finding), constraints (HeritageConstraint), and remedial actions (ProposedIntervention).
Ontograf (Logic & Properties):
The Ontograf view displays the visual ontology network, mapping the relationships between materials, components, and their specific defects or functions.
Table01: Table of Axioms
Engineering Examples:
- Automated Defect Prioritization: The system automatically classifies a 6.5 mm crack as a CriticalStructuralDefect because it exceeds the 5.0 mm engineering threshold, immediately flagging the wall for mandatory repairs.
- Guided Moisture Diagnosis: By identifying active damp as a HighPriorityHeritageRisk, the ontology provides a structured checklist of mitigation methods (e.g., roof repair or repointing) to help specialists find the root cause.
- Heritage Compliance Checking: The reasoner prevents damaging mistakes by flagging logical inconsistencies, such as an attempt to repair a historic facade with unapproved modern cement instead of historic mortar.
Conclusion:
The report concludes that formalizing expert knowledge into a logical model transforms the ontology from a simple description into an active reasoning engine, forming the semantic core for a true digital twin in heritage engineering.
References:
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