Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures
- Publication Type:
- Thesis
- Issue Date:
- 2018
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
Underground sewer systems are an important national infrastructure requirement of any country. In most cities, they are old and have been exposed to significant levels of microbial induced concrete corrosion, which is widely regarded as a serious global problem as they pose threats to public health and cause economic repercussions to water utilities. In order to maintain those underground assets efficaciously, it is pivotal for water utilities to estimate the amount of intact concrete left to rebar by predicting the rate of corrosion throughout the sewer network. Existing predictive models incorporate concrete surface temperature and surface moisture conditions as observations. However, researchers and water utilities often use indirect measures like ambient temperature and humidity data as inputs to their models. This is primarily due to unavailability of proven technologies in the state-of-the-art systems and sensing limitations predominantly attributed to the corrosive nature of the sewer environment. Hence, the focus of this dissertation is to provide reliable measures of surface temperature and moisture conditions by developing robust sensor technologies that can facilitate measurements under the hostile sewer conditions.
This dissertation encompasses three main parts:
In the first part, a robust sensor technology using an infrared radiometer sensor for quantifying surface temperature dynamics inside concrete sewer pipes is proposed. In this regard, the sensor was comprehensively evaluated in the laboratory conditions to study the effects of optical window fogging, incident angle, limit of detection, distance, lighting conditions, reproducibility, humidity and increased surface temperature conditions. Thereafter, the sensor was deployed in sewer pipe for real-time continuous measurements. The field study revealed the suitability of the proposed sensor technology for non-contact surface temperature measurements under the hostile sewer environment. Further, the accuracy of the sensor measurements was improved by calibrating the sensor with emissivity coefficient of the sewer concrete.
In the second part of the dissertation, a non-invasive sensing technique to determine the concrete surface moisture conditions is proposed. In this context, laboratory experiments were conducted to study the behaviour of concrete moisture to electrical resistance variations and different pH concentrations. This study led to utilize the Wenner array method to determine the surface moisture conditions based on concrete surface electrical resistivity measurements. Then, the sensor suite was deployed in concrete sewer pipe to measure the surface resistivity for about three months. Upon on-site calibration, surface moisture conditions were determined and thereof, the field campaign exhibited the feasibility of the proposed sensing method. Further investigations were conducted to locate the reinforcing bar embedded in concrete for optimal sensor installation in order to minimize the effects of reinforcing bar during measurements.
In the third part, sensor technologies were combined with smart predictive analytics to develop a diagnostic toolkit that can digitally monitor the health conditions of the sensors is proposed. This toolkit embraces a seasonal autoregressive integrated moving average model with statistical hypothesis testing technique to enable temporal forecasting of sensor data; identify and isolate anomalies in a continuous stream of sensor data; detect early sensor failure and finally to provide reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems.
Overall, this dissertation significantly contributes to ameliorating the way sewer assets are monitored and maintained in Australia and globally by providing information-rich new data to the predictive models for better corrosion prediction.
Please use this identifier to cite or link to this item: