Abstract Heritage buildings are highly vulnerable to structural degradation due to aging materials, environmental exposure, and natural disasters, necessitating intelligent and realâtime monitoring solutions. The current study proposes a dew computingâenabled digital twin framework integrated with Explainable Artificial Intelligence (XAI) for structural risk evaluation and health prediction of heritage infrastructure. The framework combines Internet of Thingsâbased sensing, dewâfogâcloud computing architecture, blockchainâbased data security, and a hybrid deep learning model to enable efficient, low latency, and reliable monitoring. Temporal structural data are processed using a Convolutional Neural NetworkâGated Recurrent Unit (GRU) model for feature extraction and timeâseries prediction of the Structural Health Index, while a Random Forest (RF) classifier categorizes structural risk into safe, degraded, and critical states. Shapley Additive explanationsâbased XAI is incorporated to enhance interpretability and support expert decisionâmaking. Experimental evaluation on a simulated dataset of 45,212 instances demonstrates the effectiveness of the proposed approach. The GRUâbased model achieves high prediction performance with an accuracy of 94.42%, sensitivity of 94.85%, specificity of 97.01%, and F1âscore of 94.43%. Regression analysis shows low prediction errors (Mean Absolute Error: 0.0158, Root Mean Squared Error: 0.0198) and a high coefficient of determination (), indicating strong agreement between predicted and actual structural states. The RF classifier further achieves 94.64% accuracy in structural risk classification. The framework exhibits low latency (~0.000195 s per sample), high reliability under noisy conditions (up to 99%), and strong scalability across increasing dataset sizes. Overall, the proposed system provides a robust, scalable, and interpretable solution for proactive Structural Health Monitoring and riskâaware maintenance of heritage buildings, significantly improving realâtime decisionâmaking and longâterm conservation strategies.
Muhammad Hadi Mustafa, Choy Poh Keong, Chua Wen Xuan, Zulkiflee Abdul-Samad ¡ 5 authors
The challenges in heritage building documentation and management in Malaysia highlight the need for a more secure and integrated digital framework. Although Heritage Building Information Modelling (HBIM) has improved digital documentation and lifecycle management, current practices remain constrained by data fragmentation, limited transparency, and inefficient stakeholder collaboration. While blockchain technology offers considerable potential to enhance data security and trust, its integration with HBIM in heritage conservation projects remains underexplored. This paper aims to develop a conceptual framework in exploring the interrelated themes influencing the implementation of blockchain integration with HBIM in Malaysian projects. Using a qualitative approach, semi-structured interviews were conducted with industry professionals, and the data were analysed using thematic analysis. The proposed conceptual framework is empirically informed by practitioner interviews, allowing the study to capture both technological possibilities and implementation realities within the Malaysian heritage project context. The findings suggest that blockchain-enabled HBIM has the potential to strengthen data integrity, transparency, traceability, stakeholder accountability, and lifecycle decision-making, although these benefits require further validation through pilot implementation. Future research should further validate the proposed framework through pilot projects, and blockchain governance guidelines tailored to the Malaysiaâs heritage conservation sector.