Author(s):
Shanmuga Sundari M, Sireesha Vikkurty, Naga Satish, Jadala Vijaya Chandra, T.Lakshmi Praveena, Kbks Durga
Email(s):
sundari.m@bvrithyderabad.edu.in
DOI:
10.52711/2231-3915.2026.00003 
Address:
Shanmuga Sundari M, Sireesha Vikkurty, Naga Satish, Jadala Vijaya Chandra, T.Lakshmi Praveena, Kbks Durga
Department of Computer Science and Engineering, BVRIT Hyderabad College of Engineering for Women, Hyderabad, India.
*Corresponding Author
Published In:
Volume - 16,
Issue - 1,
Year - 2026
ABSTRACT:
Abstract: Through the application of digital sensors, automation and communication technologies, today's infrastructure systems are getting more complicated. These systems need to be monitored all the time to guarantee their dependability and to prevent costly or hazardous outages. The paper suggests the use of an anomaly detection system relying on Temporal Convolutional Networks (TCNs) which would support the prediction of real-time data and the detection of faults in intelligent infrastructural environments at an early stage. The framework suggested will examine multivariate time-series data of various sensing nodes installed throughout transportation, water distribution, and structural facilities. Through the use of dilated causal convolutions, the network is capable of capturing short and long-term time patterns without using recurrent feedback. Comparative analysis of experiments conducted with recurrent models, including LSTM and GRU, shows that the TCN-based method is much faster to train, more accurate in detection, and requires less computation. Experiments using publicly available infrastructure datasets indicate a steady increase in accuracy and recall, which in turn proves the potential of the model to predict the early anomalies and sustain active maintenance policies in intelligent urban systems.
Cite this article:
Shanmuga Sundari M, Sireesha Vikkurty, Naga Satish, Jadala Vijaya Chandra, T.Lakshmi Praveena, Kbks Durga. Temporal Convolutional Network-Based Anomaly Detection Framework for Intelligent Infrastructure Systems. International Journal of Technology. 2026; 16(1):24-4. doi: 10.52711/2231-3915.2026.00003
Cite(Electronic):
Shanmuga Sundari M, Sireesha Vikkurty, Naga Satish, Jadala Vijaya Chandra, T.Lakshmi Praveena, Kbks Durga. Temporal Convolutional Network-Based Anomaly Detection Framework for Intelligent Infrastructure Systems. International Journal of Technology. 2026; 16(1):24-4. doi: 10.52711/2231-3915.2026.00003 Available on: https://www.ijtonline.com/AbstractView.aspx?PID=2026-16-1-3
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