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Documentary filmmaking is inherently subjective. The filmmaking team decides when to film, how to angle the camera, how to edit, and what narrative to put forward. At the same time, documentary filmmaking has a capacity for sharing people's experiences, expressing emotion, and foregrounding context through image, sound, and movement. In this paper, we discuss the tensions with using documentary filmmaking as a method for documentation as well as dissemination in design research. We present our approach to creating a series of 12 documentary shorts in the context of the Inner Ear project. The Inner Ear is a data physicalization project that invites participants to capture vibrations in their homes, which are then materialized as porcelain sculptures. We articulate the pressures and uncertainties of filming, and the responsibility of building narrative through editing. Finally, we discuss the generative but conflicting goals of combining research documentation with public dissemination via documentary filmmaking. 

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. 

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.