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Abstract The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters. 

Multi-graph clustering aims to improve clustering accuracy by leveraging information from different domains, which has been shown to be extremely effective for achieving better clustering results than single graph based clustering algorithms. Despite the previous success, existing multi-graph clustering methods mostly use shallow models, which are incapable to capture the highly non-linear structures and the complex cluster associations in multigraph, thus result in sub-optimal results. Inspired by the powerful representation learning capability of neural networks, in this paper, we propose an end-to-end deep learning model to simultaneously infer cluster assignments and cluster associations in multi-graph. Specifically, we use autoencoding networks to learn node embeddings. Meanwhile, we propose a minimum-entropy based clustering strategy to cluster nodes in the embedding space for each graph. We introduce two regularizers to leverage both within-graph and cross-graph dependencies. An attentive mechanism is further developed to learn cross-graph cluster associations. Through extensive experiments on a variety of datasets, we observe that our method outperforms state-of-the-art baselines by a large margin. 

Frost heave can cause serious damage to civil infrastructure. For example, interactions of soil and water pipes under frozen conditions have been found to significantly accelerate pipe fracture. Frost heave may cause the retaining walls along highways to crack and even fail in cold climates. This paper describes a holistic model to simulate the temperature, stress, and deformation in frozen soil and implement a model to simulate frost heave and stress on water pipelines. The frozen soil behaviors are based on a microstructure-based random finite element model, which holistically describes the mechanical behaviors of soils subjected to freezing conditions. The new model is able to simulate bulk behaviors by considering the microstructure of soils. The soil is phase coded and therefore the simulation model only needs the corresponding parameters of individual phases. This significantly simplifies obtaining the necessary parameters for the model. The capability of the model in simulating the temperature distribution and volume change are first validated with laboratory scale experiments. Coupled thermal-mechanical processes are introduced to describe the soil responses subjected to sub-zero temperature on the ground surface. This subsequently changes the interaction modes between ground and water pipes and leads to increase of stresses on the water pipes. The effects of cracks along a water pipe further cause stress concentration, which jeopardizes the pipe’s performance and leads to failure. The combined effects of freezing ground and traffic load are further evaluated with the model.