City Research Online

Abstract

Aging water and sewage pipelines require effective monitoring as leakage and burst incidents pose environmental, economic, and public safety risks. Many existing studies rely on laboratory or simulated data from electrical sensors installed in harsh sewer environments, which require regular cleaning and calibration. The novelty of this work lies in presenting a systematic, real-world evaluation of machine learning models for pipeline leak detection using fiber Bragg grating-based pressure sensors deployed in an operational pipeline test facility for the first time. The models evaluated include supervised models, such as Random Forest and Extreme Gradient Boosting (XGBoost), and unsupervised models, such as Long Short-Term Memory (LSTM) and LSTM Autoencoder. These models are compared under identical experimental conditions and were evaluated under both serial (single sensor) and parallel (multi-sensor) configurations, using sliding windows of varying lengths. Weighted precision and F1-scores were used to address strong class imbalance across leak sizes. Results show that XGBoost achieved the best overall performance, reaching 77% precision and 52% F1-score at a 45-point window length. The results obtained are better than those reported in previous work, most of which rely on simulations or laboratory datasets and critically do not evaluate models under real operational pipeline conditions, as is done here. The findings demonstrate the robustness of supervised XGBoost when applied to small, noisy real-world datasets and they highlight the feasibility of integrating ML-based FBG sensors into cloud-enabled pipeline monitoring systems.

Publication Type:	Article
Additional Information:	Copyright © 2025 IEEE. This accepted manuscript is made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Fiber Bragg grating (FBG) sensor, leak detection, machine learning, pipeline monitoring
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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