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Sewer flooding incidents in the UK are being increasingly associated with the presence of blockages. Blockages are difficult to deal with as although there are locations where they are more likely to occur, they do occur intermittently. In order to manage sewer blockage pro-actively sewer managers need to be able to identify the location of blockages promptly. Traditional CCTV inspection technologies are slow and relatively expensive so are not well suited to the rapid inspection of a network. This is needed if managers are to be able to address sewer blockages pro-actively. This thesis reports on the development of low-cost, rapidly deployable acoustic base sensor that will be able to survey live sewer pipes. The sensor emits short coded acoustic signals which are reflected from any defects of the wall of the underground pipes and recorded for future processing. The processing algorithms are based on the temporal windowing, deconvolution, Fourier, and intensity analysis so that the response can be linked directly to the location and property of the of the pipe deformation. The sensor was tested in a full scale sewer pipe in the laboratory and in few sites in UK, Austria and Netherlands and it was shown that it is able to discriminate between blockages and structural aspects of a sewer pipe such as a manhole and lateral connection. The anticipated cost is orders of magnitude lower than any current technique.

Following large rain events, excess flow in sanitary sewers from inflow and infiltration (I/I) cause sanitary sewer overflows (SSO), resulting in significant problems for Pinellas County and the Tampa Bay area. Stormwater enters the sanitary sewers as inflow from improper or illegal surface connections, and groundwater enters the system as infiltration through cracks in subsurface infrastructure. This pilot study was designed to develop methods to separate and quantify the components of I/I and to build a predictive model using flowmeter and rainfall data. To identify surface inflow, daily wastewater production and groundwater infiltration patterns were filtered from the flow data, leaving a residual signal of random variation and possible inflow. The groundwater infiltration (as base infiltration, BI) was calculated using the Stevens-Schutzbach method, and daily wastewater flow curves were generated from dry weather flow (DWF) data. Filtered DWF values were used to construct a range of expected residuals, encompassing 95% of the variability inherent in the system. Filtered wet weather flows were compared to this range, and values above the range were considered significant, indicating the presence of surface inflow. At all 3 flow meters in the pilot study site, no surface inflow was detected, and the I/I was attributed to groundwater infiltration (as BI). Flow data from 2 smaller sub-sewersheds within the greater sewershed allowed analysis of the spatial variability in BI and provided a method to focus in on the most problematic areas. In the sub-sewershed with the shallowest water table and most submerged sanitary sewer infrastructure, an average of 56% of the average daily flow consisted of groundwater, compared to 44% for the entire study site. Cross-correlation analysis suggests that rain impacts the water table for up to 9 days, with the highest impact 1 to 3 days after rain events, and the water table, in turn, impacts infiltration for up to 6 days. The highest correlation between rainfall and infiltration occurs 3 to 5 days after a rain event, which corroborates observations from Pinellas County that severe flows to the reclamation facility continue for 3 to 5 days after severe storms. These results were used to build a linear regression model to predict base infiltration (per mile of pipeline) during the wet season using the previous 7 days of daily rainfall depths. The model tended to under-predict infiltration response to large storm events with a R2 value of 0.52 and standard error of regression of 5.3. The results of the study show that inflow can be detected using simple time series analysis instead of traditional smoke and dye testing. In this study site, however, groundwater infiltration is the only significant source of I/I. Additionally, water table and sewer invert elevations serve as useful indicators of potential sites of groundwater infiltration. Infiltration can be modeled as a function of the previous 7 days of rainfall, however simple linear regression cannot fully capture the complexity of the system response.

Following large rain events, extraneous freshwater contributions known as inflow and infiltration (I/I) bypass the storm sewer and enter the sanitary sewer system. In areas with a high water table, like Pinellas County and the surrounding Tampa Bay area, a majority of the wastewater infrastructure is submerged year round exacerbating the rate of groundwater infiltration. This excess flow overloads the existing wastewater infrastructure leading to sanitary sewer overflows (SSOs). These SSOs result in serious problems for municipalities and utilities across the country. This study was performed in order to assist Pinellas County Utilities in rehabilitating their southern sewer system. To do this, 59 sub-basins across 8 sewer zones were monitored through Pinellas County’s Phase 1 Flow Monitoring Program accounting for over 150 miles of gravity pipe. For each sub-basin, a flow meter was utilized to measure the flow from May to October, 2017. This data was analyzed to separately quantify the amount of infiltration and inflow in each sub-basin, respectively. Once quantified, a Severity Index (SI) was developed in order to give each sub-basin a score from 1-100 as it relates to the condition of the gravity mains in the sub-basin. The SI was a function of locational features available with the use of a Geographic Information System (GIS), such as the distance to water bodies and the soil hydrologic group (SHG), as well as intrinsic pipe properties including the type of pipe material and the age of pipe. Once validated with additional flow monitoring data, the developed SI framework can serve as an additional tool utilized by Pinellas County Utilities to identify areas in need of sanitary sewer rehabilitation. Being that the model only requires easily attainable information, this approach is less time consuming and is inexpensive as compared to traditional flow monitoring efforts. The study also examined the required monetary investment by Pinellas County Utilities in order to abate the 17 sub-basins observed in the study with an infiltration rate greater than the marginal threshold put forth by the Environmental Protection Agency (EPA). The study indicated that gravity pipe rehabilitation does not make a significant impact on groundwater infiltration until at least 30% of the gravity pipes in the sub-basin are lined. This is due to the groundwater table submerging a majority of the wastewater infrastructure. Once this threshold is met, lining was observed to abate groundwater infiltration linearly. The results found that $4.4 million will be required to rehabilitate the affected sub-basins to a marginal rate of infiltration and reduce the flow to South Cross Bayou Water Reclamation Facility (SCBWRF) by an average of 0.72 mgd (million gallons per day). On an annual basis, this reduction in flow will result in approximately $650,000 in treatment costs savings.

Sewer flooding incidents in the UK are being increasingly associated with the presence of blockages. Blockages are difficult to deal with as although there are locations where they are more likely to occur, they do occur intermittently. In order to manage sewer blockage pro-actively sewer managers need to be able to identify the location of blockages promptly. Traditional CCTV inspection technologies are slow and relatively expensive so are not well suited to the rapid inspection of a network. This is needed if managers are to be able to address sewer blockages pro-actively. This thesis reports on the development of low-cost, rapidly deployable acoustic base sensor that will be able to survey live sewer pipes. The sensor emits short coded acoustic signals which are reflected from any defects of the wall of the underground pipes and recorded for future processing. The processing algorithms are based on the temporal windowing, deconvolution, Fourier, and intensity analysis so that the response can be linked directly to the location and property of the of the pipe deformation. The sensor was tested in a full scale sewer pipe in the laboratory and in few sites in UK, Austria and Netherlands and it was shown that it is able to discriminate between blockages and structural aspects of a sewer pipe such as a manhole and lateral connection. The anticipated cost is orders of magnitude lower than any current technique.