City-scale eco-routing and pavement eco-maintenance scheduling for CO2 mitigation

Responsible for more than one-sixth of the world’s CO2 emissions and growing, the road transportation is an unavoidable component in tackling the global carbon reduction challenge. Within the subdivisions of infrastructure management and traffic management, different carbon mitigation approaches have been discussed to contribute to a greener transportation system. However, given the complex interactions that exist between the road users and the infrastructures, certain carbon mitigation proposals are best evaluated within a compre- hensive environment to ensure the correct consideration of the interactions, as well as the comparability of the results. It is the aim of this thesis to develop such a unified framework to reflect interactions and outcomes of different CO2 mitigation approaches from both the road infrastructure and the traffic mobility sides. This allows combining and comparing the effectiveness of a single or multiple carbon mitigation approaches across different perspectives. Specifically, on the traffic operation side, a mesoscopic traffic model is adopted for simulating drivers’ route choices with varying percentages of travellers to choose the eco-friendly routes. From the infrastructure asset management perspective, a city-scale pavement degradation model is built and utilised in testing pavement maintenance scenarios. San Francisco is chosen as the case study area due to the availability of various traffic mobility and infrastructure condition data. In Chapter 3 of the thesis, the traffic simulation module is developed that implements the efficient macroscopic road link-level speed-flow relationship while retaining detailed origin, destination and departure hour information for each individual trip. The traffic simulation model uses a highly detailed network representation for the study area and has the hourly traffic demand informed by Traffic Network Companies (TNC) data. The model is capable of capturing the spatio-temporal variations in traffic distributions. In addition, in Chapter 4, assumptions are also tested as for the extent that the availability of real-time traffic information affects the travellers’ behaviours and model results. As ageing pavements induce additional carbon emissions, in Chapter 5, a city-scale pavement degradation model is proposed based on 20 years of survey data. After comparing three model forms (non-spatial categorical, non-spatial individual road based and spatial hierarchical models) and two independent predictors (pavement age, cumulative traffic load), the spatial model with age as the predictor is found to give the best overall performance in terms of model fitting and complexity. As a result, it is used later in this thesis for degradation forecasting and maintenance planning. The traffic simulation and the pavement degradation models are joined together in Chapter 6 to test the carbon mitigation scenarios, including the eco-friendly route selection (eco- routing) and eco-friendly pavement maintenance scheduling (eco-maintenance). Interactions between the road users and the pavement management occur when: (1) pavement maintenance site selection is based on both pavement roughness and traffic volume (the eco-maintenance case). (2) The renewed pavement condition, with a smoother surface and reduced emission factor, becomes part of the route selection criteria of the drivers (the eco-routing case). It is found that the outcomes of eco-maintenance are sensitive to a variety of factors, including the budget level, the pavement degradation rate as well as the maintenance quality. The eco-routing approach tends to shift travellers to the local network but is effective in reducing the overall emissions. However, the reinforcing interactions between these two strategies are the most noticeable only when both eco-routing and eco-maintenance strategies are enforced to an extreme. Through the simulation of city-scale traffic and infrastructure dynamics, it is able to quantitatively compare carbon mitigation scenarios and understand how an action from one specific perspective ripples through the transportation system. Also, sensitivity tests suggest limiting conditions for each approach to make a difference. This research highlights the need to include combined simulations in certain cases and such results are expected to give confidence to decision makers as for the potential induced demand or other secondary effects whose influences extend beyond a single sub-system.Cambridge International Students Scholarship The Alan Turing Institute Enrichment Studentshi

High accuracy ultrasonic degradation monitoring

This thesis is concerned with maximising the precision of permanently installed ultrasonic time of flight sensors. Numerous sources of uncertainty affecting the measurement precision were considered and a measurement protocol was suggested to minimise variability. The repeatability that can be achieved with the described measurement protocol was verified in simulations and in laboratory corrosion experiments as well as various other experiments. One of the most significant and complex problems affecting the precision, inner wall surface roughness, was also investigated and a signal processing method was proposed to improve the accuracy of estimated wall thickness loss rates by an order of magnitude compared to standard methods. It was found that the error associated with temperature effects is the most significant among typical experimental sources of uncertainty (e.g. coherent noise and coupling stability). By implementing temperature compensation, it was shown in laboratory experiments that wall thickness can be estimated with a standard deviation of less than 20 nm when temperature is stable (within 0.1 C) using the signal processing protocol described in this thesis. In more realistic corrosion experiments, where temperature changes were of the order of 4 C), it was shown that a wall thickness loss of 1 micron can be detected reliably by applying the same measurement protocol. Another major issue affecting both accuracy and precision is changing inner wall surface morphology. Ultrasonic wave reflections from rough inner surfaces result in distorted signals. These distortions significantly affect the accuracy of wall thickness estimates. A new signal processing method, Adaptive Cross-Correlation (AXC), was described to mitigate the effects of such distortions. It was shown that AXC reduces measurement errors of wall thickness loss rates by an order of magnitude compared to standard signal processing methods so that mean wall loss can be accurately determined. When wall thickness loss is random and spatially uniform, 90% of wall thickness rates measured using AXC lie within 7.5 ± 18% of the actual slope. This means that with mean corrosion rates of 1 mm/year, the wall thickness estimate with AXC would be of the order of 0.75-1.1 mm/year. In addition, the feasibility of increasing the accuracy of wall thickness loss rate measurements even further was demonstrated using multiple sensors for measuring a single wall thickness loss rate. It was shown that measurement errors can be decreased to 30% of the variability of a single sensor. The main findings of this thesis have led to 1) a solid understanding of the numerous factors that affect accuracy and precision of wall thickness loss monitoring, 2) a robust signal acquisition protocol as well as 3) AXC, a post processing technique that improves the monitoring accuracy by an order of magnitude. This will benefit corrosion mitigation around the world, which is estimated to cost a developed nation in excess of 2-5% of its GDP. The presented techniques help to reduce response times to detect industrially actionable corrosion rates of 0.1 mm/year to a few days. They therefore help to minimise the risk of process fluid leakage and increase overall confidence in asset management.Open Acces

Detecting Stressful Social Interactions Using Wearable Physiological and Inertial Sensors

Stress is unavoidable in everyday life which can result in several health related short and long-term adverse consequences. Previous research found that most of the stress events occur due to interpersonal tension followed by work related stress. Enabling automated detection of stressful social interactions using wearable technology will help trigger just-in-time interventions which can help the user cope with the stressful situation. In this dissertation, we show the feasibility of differentiating stressful social interactions from other stressors i.e., work and commute.However, collecting reliable ground truth stressor data in the natural environment is challenging. This dissertation addresses this challenge by designing a Day Reconstruction Method (DRM) based contextual stress visualization that highlights the continuous stress inferences from a wearable sensor with surrounding activities such as conversation, physical activity, and location on a timeline diagram. This dissertation proposes a Conditional Random Field, Context-Free Grammar (CRF-CFG) model to detect conversation from breathing patterns to support the visualization. The advantage of breathing signal is that it does not capture the content of the conversation and hence, is more privacy preserving compared to audio. It proposes a framework to systematically analyze the breathing data collected in the natural environment. However, it requires wearing of chest worn sensor. This dissertation aims to determine stressful social interaction without wearing chest worn sensor or without requiring any conversation model which is privacy sensitive. Therefore, it focuses on detecting stressful social interactions directly from stress time-series only which can be captured using increasingly available wrist worn sensor.This dissertation proposes a framework to systematically analyze the respiration data collected in the natural environment. The analysis includes screening the low-quality data, segmenting the respiration time-series by cycles, and develop time-domain features. It proposes a Conditional Random Field, Context-Free Grammar (CRF-CFG) model to detect conversation episodes from breathing patterns. This system is validated against audio ground-truth in the field with an accuracy of 71.7\%.This dissertation introduces the stress cycle concept to capture the cyclical patterns and identifies novel features from stress time-series data. Furthermore, wrist-worn accelerometry data shows that hand gestures have a distinct pattern during stressful social interactions. The model presented in this dissertation augments accelerometry patterns with the stress cycle patterns for more accurate detection. Finally, the model is trained and validated using data collected from 38 participants in free-living conditions. The model can detect the stressful interactions with an F1-score of 0.83 using stress cycle features and enable the delivery of stress intervention within 3.9 minutes since the onset of a stressful social interaction

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Design of a wearable device for conditional neuromodulation of the pudendal nerve

After spinal cord injury, the normal functions of the lower urinary tract may be disrupted. Namely, incontinence and concurrent voiding problems may ensue. The troublesome side effects of the drugs, infection due to catheterisation, and the costs and risks associated with more invasive treatments indicate the need for alternative forms of treatment. The pudendal nerve neuromodulation may provide such an alternative. The unique aspect of this technique is that depending on the stimulus frequency it may result in micturition-like or continence-like reflexes. Also, the stimulus current can be applied trans-rectally, meaning that a minimally-invasive wearable solution may be developed. The major limitation of such a solution is the high level of the required stimulus current to activate the nerve trans-rectally. The efficacy of the trans-rectal neuromodulation of the pudendal may be increased by only applying the stimulus when needed, when employed to tackle incontinence. The electromyogram signal from the external anal sphincter may be used to detect the onset of hyper-reflexive contractions of the bladder. The ability of recording this signal can be readily incorporated in the neuromodulation device due to the proximity of the structures. However, the recording electrodes should be designed for an efficacious and chronic recording. Thus, the main objective of this thesis was to design and optimise the neuromodulation and recording electrodes on the said device. A volume conductor model of such a device in situ was developed and used in tandem with a double layer cable model of nerve fibres to minimise the stimulus current. It was demonstrated that a considerable reduction in the stimulus current may be achieved even when the variations of the nerve trajectory in different individuals are considered. Using computational models and experimental measurements, a recording assembly was identified for an efficacious recording of the electromyogram from the external anal sphincter

Acoustic Monitoring for Leaks in Water Distribution Networks

Water distribution networks (WDNs) are complex systems that are subjected to stresses due to a number of hydraulic and environmental loads. Small leaks can run continuously for extended periods, sometimes indefinitely, undetected due to their minimal impact on the global system characteristics. As a result, system leaks remain an unavoidable reality and water loss estimates range from 10\%-25\% between treatment and delivery. This is a significant economic loss due to non-revenue water and a waste of valuable natural resource. Leaks produce perceptible changes in the sound and vibration fields in their vicinity and this aspect as been exploited in various techniques to detect leaks today. For example, the vibrations caused on the pipe wall in metal pipes, or acoustic energy in the vicinity of the leak, have all been exploited to develop inspection tools. However, most techniques in use today suffer from the following: (i) they are primarily inspection techniques (not monitoring) and often involve an expert user to interpret inspection data; (ii) they employ intrusive procedures to gain access into the WDN and, (iii) their algorithms remain closed and publicly available blind benchmark tests have shown that the detection rates are quite low. The main objective of this thesis is to address each of the aforementioned three problems existing in current methods. First, a technology conducive to long-term monitoring will be developed, which can be deployed year-around in live WDN. Secondly, this technology will be developed around existing access locations in a WDN, specifically from fire hydrant locations. To make this technology conducive to operate in cold climates such as Canada, the technology will be deployed from dry-barrel hydrants. Finally, the technology will be tested with a range of powerful machine learning algorithms, some new and some well-proven, and results published in the open scientific literature. In terms of the technology itself, unlike a majority of technologies that rely on accelerometer or pressure data, this technology relies on the measurement of the acoustic (sound) field within the water column. The problem of leak detection and localization is addressed through a technique called linear prediction (LP). Extensively used in speech processing, LP is shown in this work to be effective in capturing the composite spectrum effects of radiation, pipe system, and leak-induced excitation of the pipe system, with and without leaks, and thus has the potential to be an effective tool to detect leaks. The relatively simple mathematical formulation of LP lends itself well to online implementation in long-term monitoring applications and hence motivates an in-depth investigation. For comparison purposes, model-free methods including a powerful signal processing technique and a technique from machine learning are employed. In terms of leak detection, three data-driven anomaly detection approaches are employed and the LP method is explored for leak localization as well. Tests were conducted on several laboratory test beds, with increasing levels of complexity and in a live WDN in the city of Guelph, Ontario, Canada. Results form this study show that the LP method developed in this thesis provides a unified framework for both leak detection and localization when used in conjunction with semi-supervised anomaly detection algorithms. A novel two-part localization approach is developed which utilizes LP pre-processed data, in tandem with the traditional cross-correlation approach. Results of the field study show that the presented method is able to perform both leak-detection and localization using relatively short time signal lengths. This is advantageous in continuous monitoring situations as this minimizes the data transmission requirements, the latter being one of the main impediments to full-scale implementation and deployment of leak-detection technology