Water scarcity as well as social and economic damages caused by the increasing amounts of non-revenue water in the water distribution networks (WDNs) have been prompting innovative solutions. A great deal of potable water is wasted due to leakage in the WDNs all over the world. Hence, various leak detection approaches have been explored, including the promising application of acoustic devices. Exploiting the benefits of technological advances in acoustic devices, signal processing, and machine learning (ML), this study aimed to develop a sophisticated system for leak detection in WDNs. Different from laboratory-based studies, this study was conducted on real WDNs in Hong Kong and lasted for about two years. Utilizing acoustic emissions acquired using wireless noise loggers, various ML algorithms were explored to develop inspection models for in-service and buried WDNs. ML classification algorithms can identify patterns in the acquired signals for leak and no-leak statuses. Thus, a combination of features describing acoustic signals in time and frequency domains was utilized to facilitate the development of ML models. Separately for metal and non-metal WDNs, ten well-known ML algorithms were used to develop leak detection models. The validation results demonstrate the promising application of noise loggers and ML for leak detection in real WDNs. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Learning (DL) leak detection models demonstrated a largely stable performance and a very good accuracy, particularly for new unlabelled cases. GRAPHICAL ABSTRACT

Fiber-reinforced polymer (FRP) confinement is an effective way of enhancing the mechanical properties of concrete. However, currently, a model that can predict the mechanical response of eccentrically loaded FRP-confined predamaged concrete has not been satisfactorily established. This may be primarily due to the complications and nature of the problem. An experimental and analytical investigation hitherto unavailable is undertaken to examine the mechanical response of eccentrically loaded FRP-confined predamaged concrete specimens. This analytical study intends to examine and identify the key parameters that affect the mechanical response of eccentrically loaded FRP-confined predamaged concrete. Eccentric compression tests were performed on 72 short concrete cylinders with wide variation of degree of damage and load eccentricity, but with the same FRP confinement stiffness. A new model for the stress–strain relationship of FRP-confined predamaged concrete with eccentric loading is developed. The new model thus established is capable of predicting the mechanical response of the eccentrically loaded FRP-confined predamaged concrete specimens with good accuracy.