TY - GEN
T1 - Interpretable AI for Built Environment Digital Twins
T2 - 2025 IEEE 16th International Conference on Cloud Computing Technology and Science, IEEE CloudCom 2025
AU - Kuruppuarachchi, Pasindu Manisha
AU - Dabarera, Arosha
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Digital Twin (DT) is a growing concept in industries such as manufacturing, energy, smart cities, and built environments. To bridge the digital and physical worlds, DTs often rely on state-of-the-art Machine Learning (ML) models. However, many ML models, like Neural Networks, are considered black boxes, making their decision-making processes difficult to interpret. To address this, the explainable AI paradigm has introduced methods like SHAP and LIME. Despite their effectiveness, interpreting model outputs remains challenging. This paper explores the potential of using Tsetlin Machines in built environment DTs to explain the detection of structural properties. The Tsetlin Machine has demonstrated strong performance even with limited training data while offering both local and global interpretability. Its rule-based approach provides transparent decision-making, making it a promising alternative to traditional black-box models in DT applications.
AB - Digital Twin (DT) is a growing concept in industries such as manufacturing, energy, smart cities, and built environments. To bridge the digital and physical worlds, DTs often rely on state-of-the-art Machine Learning (ML) models. However, many ML models, like Neural Networks, are considered black boxes, making their decision-making processes difficult to interpret. To address this, the explainable AI paradigm has introduced methods like SHAP and LIME. Despite their effectiveness, interpreting model outputs remains challenging. This paper explores the potential of using Tsetlin Machines in built environment DTs to explain the detection of structural properties. The Tsetlin Machine has demonstrated strong performance even with limited training data while offering both local and global interpretability. Its rule-based approach provides transparent decision-making, making it a promising alternative to traditional black-box models in DT applications.
KW - Built Environment
KW - Explainable AI
KW - Interpretable AI
KW - IoT
KW - Structural Property Detection
KW - Tsetlin Machine
UR - https://www.scopus.com/pages/publications/105034664985
U2 - 10.1109/CloudCom67567.2025.11331527
DO - 10.1109/CloudCom67567.2025.11331527
M3 - Conference contribution
AN - SCOPUS:105034664985
T3 - Proceedings - 2025 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2025
BT - Proceedings - 2025 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2025
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 14 November 2025 through 16 November 2025
ER -