Blockchain Papers

Chia-Hau Chen, Yung-Chi Chen, Wei-Lin Lee, Hock-Kiet Wong · 7 authors

Smart-building structural health monitoring (SHM) requires a unified digital representation capable of integrating heterogeneous sensing devices, continuous structural states, and burst-oriented post-event assessment without embedding device-specific logic throughout the software stack. This study proposes a semantic digital twin architecture in which SensorType, DeviceProfile, and site metadata form a semantic single source of truth and generate W3C Web of Things Thing Descriptions at runtime. The resulting WoT-driven contract governs field mapping, schema-on-write persistence, generic API access, state visualization, and engineering-threshold evaluation. To accommodate heterogeneous temporal behavior, event-driven seismic assessment and state-driven construction tilt monitoring are orchestrated as distinct workflows that share persistence, notification, and observability services while retaining separate timing contracts. Controlled extension experiments required no manual data-layer, backend, ingestion, or frontend modification, with a runtime source-hash difference of zero. Under a ten-building seismic-event burst, continuous write-lag p95 changed by −20 ms from a 969 ms baseline while all event jobs completed without restart or out-of-memory conditions. The ingestion path further sustained 71,040 points/s at 300 sensors with no dropped points. These results demonstrate that WoT-driven semantic interoperability and event–state workflow orchestration can provide an extensible integration foundation for smart-building SHM within a clearly defined configuration boundary.

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.