Search Results (17)

Sanitary sewer pipelines frequently experience blockages, structural failures, and overflows, underscoring the dire state of U.S. wastewater infrastructure, which has been rated a D-, while America’s overall infrastructure scores only slightly better at C-. Traditional open-trench excavation methods or excavation technology (ET) for replacing deteriorated pipes are notoriously expensive and disruptive, requiring extensive processes like route planning, surveying, engineering, trench excavation, pipe installation, backfilling, and ground restoration. In contrast, trenchless technologies (TT) provide a less invasive and more cost-effective alternative. Among these, cured-in-place pipe technology (CIPPT), which involves inserting resin-impregnated fabric into damaged pipelines, is widely recognized for its efficiency. However, a comprehensive life cycle cost analysis (LCCA) directly comparing ET and TT, accounting for the net present value (NPV) across installation, maintenance, and rehabilitation costs, remains unexplored. This study aims to establish an LCCA framework for both CIPPT and ET, specifically for sanitary sewer pipes ranging from 8 to 42 inches in diameter. The framework incorporates construction, environmental, and social costs, providing a holistic evaluation. The key costs for ET involve pipe materials and subsurface investigations, whereas TT’s costs center around engineering and design. Social impacts, such as road and pavement damage, disruption to adjacent utilities, and noise, are pivotal, alongside environmental factors like material use, transportation, project duration, and equipment emissions. This comprehensive framework empowers decision makers to holistically assess economic and environmental impacts, enabling informed choices for sustainable sewer infrastructure renewal. Full article

Blockages in sewers and drains often result in overflows and flooding that cause significant environmental pollution and public health risks, particularly in hospitals, where the consequences can be catastrophic. Due to their low “visibility”, sewers and drains are inherently difficult to monitor and maintain, resulting in a reactive management approach whereby maintenance or repair is carried out only after a system failure has occurred. This paper investigates the feasibility of applying the reflected wave technique, a unique sonar-like monitoring approach capable of identifying changes in the geometry of closed-pipe conduits, as a means of proactive system monitoring. The technique uses a 10 Hz sinusoidal air pressure wave which is transmitted into the drainpipe. When the pressure wave encounters a system boundary, a reflection is generated which alters the measured test pressure response. Analysis of the reflections generated by a changed system boundary, such as the formation of a blockage, can provide information related to the location of that boundary within the system. An experimental setup was developed to simulate a horizontal drain using standard pipework of 100 mm diameter and 70 m length. The technique was able to detect applied blockages with cross-sectional coverage of 30% and 75%, and lengths ranging from 30 mm to 3000 mm. Accuracy was improved when the pressure sensor was positioned closer to the blockage. When the sensor was 3.4 m from the blockage, location estimates were very accurate (−2% to 3% error). At a 14 m distance from the blockage, the error increased to between 4% and 33%. The accuracy of blockage detection and location improved with increasing blockage cross-sectional area and length. Overall, the reflected wave technique could provide a potentially continuous monitoring solution for blockage detection in sewers and drains. Full article

The current study presents experimental results on how discrete top blockages alter the upstream flow depth in a gravity sewer. A full-scale experimental circular open-channel system (DN150, 30 m length) was constructed to simulate a gravity sewer. Discrete top blockages with various heights (80, 90, 100 mm) were tested with various flow rates and channel slopes. For various scenarios, the flow depths just upstream of the blockage were measured and analysed to reveal the impact of the blockages. The measured flow depths consistently exceeded those predicted by a reference formula from the literature, underscoring the difficulty in developing generalisable models. Full article

As byproducts generated by commercial and domestic food-related processes, FOGs (fats, oils, and grease) are the leading cause of sewer pipe blockages in the US and around the world. Grease trap waste (GTW) is a subcategory of FOG currently disposed of as waste, resulting in an economic burden for GTW generators and handlers. This presents a global need for both resource conservation and carbon footprint reduction, particularly through increased waste upcycling. Therefore, it is critical to better understand current GTW handling practices in the context of the urban food–energy–water cycle. This can be accomplished with firsthand data collection, such as onsite visits, phone discussions, and targeted questionnaires. GTW disposal methods were found to be regional and correspond to key geographical locations, with landfill operations mostly practiced in the Midwest regions, incineration mainly in the Northeast and Mid-Atlantic regions, and digestion mainly in the West of the US. Select GTW samples were analyzed to evaluate their potential reuse as low-cost feedstocks for biodiesel or renewable diesel, which are alternatives to petroleum diesel fuels. Various GTW lipid extraction technologies have been reviewed, and more studies were found on converting GTW into biodiesel rather than renewable diesel. The challenges for these two pathways are the high sulfur content in biodiesel and the metal contents in renewable diesel, respectively. GTW lipid extraction technologies should overcome these issues while producing minimum-viable products with higher market values. Full article

This paper introduces a novel method for enhancing underground pipeline inspection, specifically addressing limitations associated with traditional closed-circuit television (CCTV) systems. These systems, commonly used for capturing visual data of sewer system deformations, heavily rely on subjective human expertise, leading to limited accuracy in detection. Furthermore, their inability to perform quantitative analyses of deformation extent hampers overall inspection effectiveness. Our proposed method leverages laser point cloud data and employs a 3D scanner for objective detection of geometric deformations in underground pipe corridors. By utilizing this approach, we enable a quantitative assessment of blockage levels, offering a significant improvement over traditional CCTV-based methods. The key advantages of our method lie in its objectivity and quantification capabilities, ultimately enhancing detection reliability, accuracy, and overall inspection efficiency. Full article

In artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems. Full article

Urban sprawl has resulted in great losses of vegetation areas, an increase in impervious surfaces, and consequently the direct flow of stormwater into stream channels (i.e., the immediate flow of stormwater into stream channels, in comparison to the indirect flow that is represented by practices aiming to retain stormwater for a certain period of time and treat the polluted stormwater prior to flowing into the stream channels such as detention/retention basins, among others). Stormwater management systems such as catch basins (CBs) are needed to reduce the effect of stormwater runoff. Preventative maintenance, repair, and replacement of CBs are critical to achieve stormwater management best practices. Those practices prevent the blockage of the stormwater system, limit the pollutants in storm sewers, and reduce the risk of flooding. However, no preceding research studies have been conducted to model and simulate the serviceability of CBs and to determine optimal strategies for operating CBs. To that extent, this study establishes a framework to develop and validate an optimal and adaptive maintenance, repair, and overhaul (MRO) strategy for CBs. In relation to that, an agent-based model (ABM) integrated with Monte Carlo simulation was developed for all 560 CBs in New York City’s District 5 and was statistically validated using 99% confidence intervals. The MRO parameters were optimized to minimize the total cost of the system and attain the desired level of serviceability of CBs. Sensitivity analysis was conducted to guide the maintenance planning process of CBs and reveal the effect of the input parameters on the model’s behavior. In addition, ten thousand Monte Carlo iterations were simulated to derive the distributions of the defined parameters. The results proved that in order to minimize the overall cost of repair, maintenance, and replacement of CBs and attain a minimum serviceability threshold of 80%, the following optimal MRO policy needs to be implemented: having seven service crews (where service crews are human resources (i.e., MRO teams) needed to perform the required maintenance, repair, and replacement work), implementing a replacing policy, and replacing CBs after five maintenance periods. The findings revealed that the service crews represent the most critical parameter in affecting the total cost and serviceability of CBs. This research contributes to the existing literature by offering a better knowledge of the management process of CBs and devising optimal MRO strategies for properly operating them. Ultimately, this research helps decision-makers and engineers increase the lifespan of CBs and limit their risks of breakdown, increase their efficiency, and avoid unnecessary costs. The proposed model is flexible and can be implemented to any geographical area and with other model/system parameters, which makes it adaptive for any scenario and area presented by the user. Finally, maintaining stormwater management practices helps in protecting the environment by decreasing the demand on stormwater systems, reducing flooding, protecting people and properties, promoting healthier rivers, and consequently creating more sustainable communities. Full article

Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work. Full article

Sewer blockages delay sewage discharge or cause it to overflow, which pollutes the environment and is a public health hazard. This necessitates the quick and accurate identification and positioning of sewer blockages. Following a sewer blockage, the sewage is intercepted and the liquid level at the upstream and downstream of the blocking point changes. This study established a method for identifying sewer blockages by analyzing the range and rate of the liquid level change at the upstream and downstream of the blocking point. Through pilot-scale and full-scale experiments, this study summarized the threshold values of the liquid level change rate and the liquid level fluctuation range of the drainage pipeline in normal operation, as well as the threshold values of the liquid level change rate and the liquid level fluctuation range of the upstream and downstream of the sewer blocking point. Moreover, the sewer blockage identification matrix was completed. Sewer blockage in rural areas can be identified and positioned using mathematical tools such as the data-driven model. This research method allows for real-time monitoring and timely warning of the sewer status, thereby reducing the labor and material consumption and unnecessary earthwork excavation to ensure the stable operation of the drainage pipeline. Full article

This study aims to model the performance of sewage networks under diverse blockage situations in terms of overflow occurrence using internet-of-things-based technologies in Hong Kong. To this end, a multi-stage methodological approach is employed, starting from collecting required data using smart sensors, utilizing novel data mining techniques, and using a case study simulation. From the results obtained, the following conclusions are drawn: (1) several sites under investigation are imbued with partial blockages, (2) the overall performance of the sewer network has a nonlinear relationship with the blockages in terms of the remaining time to overflow, (3) in cases of complete blockages, the sewer only takes few minutes to reach the manhole cover level that causes the system to experience overflow, and (4) cleaning work significantly improve the performance of the sewage network by 86%. The outcomes of this study provide a solid foundation for the concerned environmental engineers and decision-makers towards reducing the magnitude of sewer overflow and improving different aspects of our environment. Full article

The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909. Full article

Water demand management often focuses on quantifying the benefits of water efficiency rather than the potential impact of reduced flows on the sewer network. This study assessed the impact of a high-density deployment of ultra-low flush toilets (ULFT). A pre-installation washroom survey was carried out in July 2018. Water demand and sewer network condition were assessed ahead of the installation of 119 ULFTs and a real-time monitoring system across seven buildings on the University of Exeter campus. ULFTs were flushed 257,925 times in 177 days saving an estimated 2287 m³ per annum (compared to traditional 6 litre WCs). The annual cost saving of this reduction is approximately £12,580/annum, assuming a volumetric cost of £5.50/m³ of water. Mean discharge to the sewer network reduced by 6 m³/day. In the six-month period, 95 maintenance issues were reported, equating to 1 in 2700 flushes (0.037%). However, the frequency of incidents decreased after an initial commissioning period. There is no evidence, from blockage reports or photographs of manhole flow conditions, that the risk of blockage in the sewer network increased as a result of the ULFT installation programme. Full article

Combined sewer overflow structures (CSO) play an important role in sewer networks. When the local capacity of a sewer system is exceeded during intense rainfall events, they act as a “safety valve” and discharge excess rainfall run-off and wastewater directly to a natural receiving water body, thus preventing widespread urban flooding. There is a regulatory requirement that solids in CSO spills must be small and their amount strictly controlled. Therefore, a vast majority of CSOs in the UK contain screens. This paper presents the results of a feasibility study of using low-cost, low-energy acoustic sensors to remotely assess the condition of CSO screens to move to cost-effective reactive maintenance visits. In situ trials were carried out in several CSOs to evaluate the performance of the acoustic sensor under realistic screen and flow conditions. The results demonstrate that the system is robust within ±2.5% to work successfully in a live CSO environment. The observed changes in the screen condition resulted in 8–39% changes in the values of the coefficient in the proposed acoustic model. These changes are detectable and consistent with observed screen and hydraulic data. This study suggested that acoustic-based sensing can effectively monitor the CSO screen blockage conditions and hence reduce the risk of non-compliant CSO spills. Full article

Sediment and flow depth monitoring in sewers is important for informing flow models and for predicting and mitigating against sewer blockage formation and surcharge. In this study, a novel sensor based on conductance measurement has been developed and tested under a laboratory environment and validated by a finite-element model. The relative conductance is measured between pairs of adjacent electrodes to provide a conductance profile along the sensor length. A piecewise linear relationship between conductance and electrode length was derived and the interface positions between sediment, water, and air can be determined from the profile. The results demonstrated that the root mean square error of the model and the measured interface level are within 1.4% and 2.6% of sensor’s measurement range. An error distribution of interface height shows that all anticipated errors are within the resolution of the electrode length increments. Furthermore, it was found that the conductivity of the measured medium is proportional to the gradient of the linear relationship of conductance and electrode length. It could therefore prove a valuable new tool for the accurate quantification of sediment and flow levels in sewer conduits, coastal environments, drainage systems for transport networks, and other industrial or academic applications. Full article

The hydraulic performance of sewers is a major public concern in industrialised countries. In this study, fibre optic distributed temperature sensing (DTS) is used to monitor the discharge of wastewater for three months to assess the performance of a long underground foul sewer in a village in the UK. DTS cables were installed in the invert of sewer pipes to obtain distributed temperature change data along the sewer network. DTS generates a series of two-dimensional data sets (temperature against distance) that can be visualised in waterfall plots to help identify anomalies. The spatial and temperature resolutions are 2 m and 0.2–0.3 °C, respectively. The monitoring data clearly identify high-temperature plumes, which represent the flow of household wastewater in the sewer. Based on the analysis of the waterfall plots, it is found that the flow velocity is about 0.14 m/s under normal conditions. When continuous moderate rain or heavy rain occurs, water backs up from the water treatment plant to upstream distances of up to 400 m and the water flow velocity in the sewer decreases sharply to about 0.03 m/s, which demonstrates the ability of the DTS to localise anomalies in the sewer network. Full article