Kaplan, Z. and Abrishami, S. (2026) Integrating HBIM and big data analytics for disaster risk management in cultural heritage conservation. Smart and Sustainable Built Environment, 15(5), pp. 2038-2064. ISSN 2046-6099
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | big data analytics; cultural heritage conservation; disaster risk management; heritage building information modelling; machine learning |
| Index terms: | effectiveness, Turkey, collaboration, information modelling, post-disaster recovery, disaster management, machine learning, methodology, disaster risk, digital technology, strategy, monitoring, resource allocation, science, heritage building, risk assessment, cultural heritage building, integration, big data, face, documentation, emergency response, conservation, disaster preparedness, recovery, questionnaire |
| Subjects: | construction type, information systems, psychology, specialized education, computing systems, financial risk, environmental policy, operations management, professional development, research methods, digital design, resource management, post-disaster and reconstruction, control systems, organizational analysis, safety engineering, Geography, management, artificial intelligence, performance management, data collection methods |
| Topics: | Education, Business Strategy, Urban Studies, Sustainability, Construction Technology, Cost Management, Health and Safety, Organizational Design, Site Management, Quality Management, Information Management, Geographical Context, Project Management, Digital Applications, Research Practice |
| Descriptive scope: | 4 PCTA |
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