Blockchain Papers

Vijay Kumar, Munish Bhatia

Abstract Heritage buildings are highly vulnerable to structural degradation due to aging materials, environmental exposure, and natural disasters, necessitating intelligent and real‐time monitoring solutions. The current study proposes a dew computing‐enabled digital twin framework integrated with Explainable Artificial Intelligence (XAI) for structural risk evaluation and health prediction of heritage infrastructure. The framework combines Internet of Things‐based sensing, dew–fog–cloud computing architecture, blockchain‐based 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 Network–Gated Recurrent Unit (GRU) model for feature extraction and time‐series prediction of the Structural Health Index, while a Random Forest (RF) classifier categorizes structural risk into safe, degraded, and critical states. Shapley Additive explanations‐based XAI is incorporated to enhance interpretability and support expert decision‐making. Experimental evaluation on a simulated dataset of 45,212 instances demonstrates the effectiveness of the proposed approach. The GRU‐based model achieves high prediction performance with an accuracy of 94.42%, sensitivity of 94.85%, specificity of 97.01%, and F1‐score 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 risk‐aware maintenance of heritage buildings, significantly improving real‐time decision‐making and long‐term conservation strategies.