Integrating HBIM and Big Data analytics for disaster risk management in cultural heritage conservation

Purpose: Disaster risk management (DRM) of cultural heritage faces significant challenges, requiring tailored, multidisciplinary approaches to effectively protect heritage assets. This study aims to develop a comprehensive disaster management framework integrating Heritage Building Information Modelling (HBIM) and Big Data analytics, enhancing preparedness, response and recovery capabilities for cultural heritage buildings and sites.

Design/methodology/approach: A systematic disaster management framework was established following an extensive review of existing HBIM practices, Big Data analytics methodologies and international guidelines for DRM of cultural heritage. To validate its practical effectiveness and applicability, a structured questionnaire was administered to heritage conservation experts affiliated with ICOMOS Turkey.

Findings: The proposed framework demonstrated the value of an interdisciplinary approach, effectively combining advanced digital technologies with traditional conservation practices. It provided robust tools, including precise 3D digital documentation, predictive analytics for risk assessment and real-time monitoring capabilities. This integration delivered actionable insights, enabling enhanced disaster preparedness, informed emergency responses and efficient post-disaster recovery planning.

Practical implications: This technology-integrated framework enables heritage professionals to optimise disaster preparedness strategies, streamline emergency interventions and improve post-disaster conservation decisions. Its adaptable structure ensures broad applicability across diverse heritage contexts, promoting improved risk assessments, efficient resource allocation and effective stakeholder collaboration.

Originality/value: This research advances beyond conventional heritage conservation methods by innovatively integrating HBIM and Big Data analytics within a multidisciplinary context, incorporating heritage conservation principles, structural engineering insights and data science techniques. The resulting framework represents a significant progression in cultural heritage disaster management, aligning closely with contemporary international conservation standards and practices.