Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.083954
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

A Blockchain-Assisted BIM–IoT Digital Twin Architecture for Trusted Operational Risk Prediction in Smart Buildings

Yuh-Shihng Chang1, Hsuan-Chao Huang2,*
1 Department of Information Management, National Chin-Yi University of Technology, Taichung, Taiwan
2 Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
* Corresponding Author: Hsuan-Chao Huang. Email: email
(This article belongs to the Special Issue: Secure and Scalable Blockchain–IoT Architectures for Next-Generation Distributed Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083954

Received 14 April 2026; Accepted 05 June 2026; Published online 03 August 2026

Abstract

The integration of Building Information Modeling (BIM), Internet of Things (IoT), digital twins, and artificial intelligence (AI) has enabled advanced smart building operations with real-time monitoring and data driven decision-making capabilities. However, existing systems still face critical challenges in ensuring trusted data provenance, secure cross-layer data exchange, and robust access control in distributed IoT environments. These limitations significantly affect the reliability and trustworthiness of data driven analytics and system-level decision-making. To address these challenges, this study proposes a blockchain-assisted BIM–IoT digital twin architecture for trusted operational risk prediction in smart buildings. The proposed framework integrates KNX-based sensing systems, BIM models, and a microservice-enabled data processing platform to support real-time bidirectional synchronization between physical assets and their virtual representations. A blockchain-assisted trust layer is introduced to provide tamper-resistant event logging, trusted data provenance, and secure coordination across IoT devices, digital models, and analytical modules. Within this architecture, a Random Forest model is employed to perform operational risk classification based on environmental, energy, and temporal features. Feature importance analysis is further applied to enhance model interpretability and provide insights into the influence of key variables on system behavior. The framework is validated using over six months of real-world data collected from a smart building environment, demonstrating stable prediction performance and identifying temperature as the most influential factor. The results indicate that the proposed architecture not only achieves robust predictive performance but also supports trustworthy system operation by ensuring data integrity, traceability, and secure interaction across distributed subsystems. The proposed framework provides a secure, scalable, and practically deployable solution for intelligent smart building management, bridging trusted sensing, BIM-based modeling, and explainable AI-driven decision-making.

Keywords

BIM; digital twin; blockchain-IoT; trusted data provenance; IoT security; smart buildings; access control; random forest; operational risk prediction

  • 84

    View

  • 9

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link