Search Results (279)

An entrapped air pocket can induce pressure surges in sewer systems. Previous studies on entrapped air in these systems have focused on analysing its effects under conditions where air is expelled. This research introduces a mathematical model to calculate pressure surges caused by air pocket compression in a sealed manhole (without an orifice size) that may occur at the output of a pumping station. The model is based on the rigid water column theory, the polytropic law, and the continuity equation. The proposed model is validated using a 7.3 m long experimental facility equipped with a sealed chamber simulating a sealed manhole cover. It is demonstrated to accurately predict the peak pressure head of 18.9 metres and the associated pressure oscillations. A sensitivity analysis is also performed to assess variations in model behaviour. Furthermore, the model effectively captures the system’s final conditions. Lastly, a case study illustrates the model’s applicability to a water installation with a length of 250 m. Full article

Aiming to solve the problem of clearing obstacles in narrow and complex sewers, this paper introduces a visually assisted Sewer Cleaning Robot (SCR) for cleaning sewers with diameters ranging from 280 to 780 mm. The main work is carried out as follows: (a) A mobile platform is equipped with a pressing mechanism to press against the pipe walls in different diameters. The arm uses high-load linear actuator structures, enhancing load capacity while maintaining stability. (b) A Detection–Localization–Cleaning mode is proposed for cleaning obstacles. The YOLO detection model is used to identify six types of sewer defects. Target defects are then localized using monocular vision based on edge detection within defect bounding boxes. Finally, cutting is performed according to the localized defect positions. The feasibility of SCR in cleaning operations is validated through a series of experiments conducted under simulated pipeline conditions. These experiments evaluate its mobility, visual detection, and localization capabilities, as well as its ability to clear hard obstacles. This paper provides technical reserves for replacing human labor that use vision algorithms to assist in cleaning tasks within sewers. Full article

Sewer networks are essential in supporting the sustainable development of rural settlements. However, many municipalities face difficulties due to inadequate or missing sanitation systems. Thus, the contamination of municipal water supplies has become a pressing issue. In the present study, the process of the sewer network establishment and its impacts on groundwater was assessed in the case of a Hungarian settlement. It was found that, following the realization of wastewater agglomeration in 2015, 85% of households in the municipality were connected by 2023. Results indicate that uninsulated septic tanks used before the sewer system resulted in a high rate of sewage leakage. 3D models using RockWorks software show a groundwater dome of more than 1 m in the vicinity of the septic tank. The discharge had a significant impact on groundwater quality; high NH₄⁺ concentrations (>90 mg/L) within the vicinity of the tank and above the limit value (0.5 mg/L) in the total area were detected. Na⁺ and NO₃⁻ concentrations above the contamination limit also reflect the severe impact of wastewater discharge. Significant positive changes in groundwater quality have been detected following sewerage. Our results highlight the importance of similar investments and draw attention to the positive changes that can be achieved. However, a very significant decrease in water levels was detected both in the vicinity of the septic tank and at the municipal level, which, in addition to precipitation patterns, is mainly attributable to the cessation of sewage outflow. Between 2013 and 2022, the average groundwater level declined by 3.8 m in the settlement. Therefore, long-term monitoring of the investment is essential. Full article

Rain gardens installed as green infrastructure to divert storm runoff from entering combined sewers also collect dissolved constituents and particulates. An urban rain garden in northwestern Indiana, USA, was continuously monitored from November 2019 to May 2021 to evaluate the fate of dissolved constituents entering the rain garden in runoff. Physical and chemical properties of soils in the rain garden were also monitored, along with underlying groundwater. Linear regression models relating specific conductance to chloride concentration indicated that the 0.0371-ha (3998 square feet) rain garden collected approximately 1490 kg (3285 pounds) of road salt from the surrounding 0.2228 ha (24,500 square feet) of impervious surfaces. Soils and groundwater were seasonally affected by road salt application but carryover from year to year was not indicated. Rain garden soil permeability (5.20 × 10⁻⁵ to 9.72 × 10⁻⁵ m/s) remained unchanged during the study period and soil organic carbon generally increased under native vegetation. The results suggest that a rain garden built on sandy soil can divert substantial quantities of runoff and dissolved constituents from combined sewers; however, chloride is transported to sub-infrastructure groundwater that eventually discharges to adjacent waterways with concentrations lower than those observed in runoff. Full article

The volume of food waste is increasing, and research has highlighted the issues related to its disposal methods. Disposers are emerging as a solution for food waste recycling; they are already used in various countries. Only a limited portion of solid waste discharge has been permitted depending on the infrastructure capacity. Although additional administrative costs are required to adapt the existing food waste disposal system to include disposers, research on consumers’ willingness to pay (WTP) for such changes is lacking. Therefore, this study analyzes consumer WTP to increase the capacity of infrastructure. In this study, contingent valuation methods are employed to evaluate WTP, and data are collected based on a one-and-one-half-bounded dichotomous choice model with 1155 residents. In addition, this study considers the relevant knowledge, satisfaction, and expectations of the service. The results show that the average WTP for additional sewage rates is KRW 6860 (USD 5.2). Covariate models show that knowledge of water quality and awareness of the extent of untreated sewage discharge during rainfall in CSOs significantly influence WTP. Additionally, satisfaction with sewage odor, expectations regarding sewerage fees, and concerns about preventing sewer backflow impact WTP. However, satisfaction with the disposer does not significantly affect WTP. Additionally, a regional analysis is conducted to determine the priority of regional infrastructure improvements. In Incheon and Seoul, where the number of complaints was higher than the average, WTP showed a positive influence. The findings of this study have practical implications for policymakers, as they can be used to determine regional policy priorities. Full article

The wastewater conveyance systems in the United States are facing severe structural challenges, with the nation’s overall wastewater infrastructure receiving a critically low grade of D- from the American Society of Civil Engineers (ASCE). Innovative trenchless technologies, such as Cured-in-Place Pipe Renewal Technology (CIPPRT), offer a cost-efficient substitute for traditional open-cut construction methods (OCCM). However, the possibility of a comprehensive life-cycle cost analysis (LCCA) comparing these methods remains unexplored. LCCA examines the comprehensive financial impact, encompassing installation, operation, maintenance, rehabilitation, and replacement expenses, using net present value (NPV) over a set duration. The objective of this study is to systematically review the existing literature to explore advancements in calculating the LCCA for CIPPRT and compare the latter approach to OCCM. A rigorous PRISMA-guided methodology applied to academic databases identified 845 publications (1995–2024), with 83 documents being selected after stringent screening. The findings reveal limited use of artificial intelligence (AI) or machine learning (ML) in predicting CIPPRT costs. A bibliometric analysis using VOSviewer visualizes the results. The study underscores the potential of intelligent, data-driven approaches, such as spreadsheet models and AI, to enhance decision-making in selecting rehabilitation methods tailored to project conditions. These advancements promise more sustainable and cost-effective management of sanitary sewer systems, offering vital insights for decision-makers in addressing critical infrastructure challenges. Full article

In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. Full article

Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing. Full article

Consecutive oscillatory eruptions of a mixture of gas and liquid in urban stormwater systems, commonly referred to as sewer geysers, are investigated using transient three-dimensional (3D) computational fluid dynamics (CFD) models. This study provides a detailed mechanistic understanding of geyser formation under partially filled dropshaft conditions, an area not previously explored in depth. The maximum geyser eruption velocities were observed to reach 14.58 m/s under fully filled initial conditions ($h_w/h_d$ = 1) and reduced to 5.17 m/s and 3.02 m/s for partially filled conditions ($h_w/h_d$ = 0.5 and 0.23, respectively). The pressure gradients along the horizontal pipe drove slug formation and correlated directly with the air ingress rates and dropshaft configurations. The influence of the dropshaft diameter was also assessed, showing a 116% increase in eruption velocity when the dropshaft to horizontal pipe diameter ratio ($D_d/D_t$) was reduced from 1.0 to 0.5. It was found that the strength of the geyser (as represented by the eruption velocity from the top of the dropshaft) increased with an increase in the initial water depth in the dropshaft and a reduction in the dropshaft diameter. Additionally, the Kelvin–Helmholtz instability criteria were satisfied during transitions from stratified to slug flow, and they were responsible for the jump and transition of the flow during the initial rise and fallback of the water in the dropshaft. The present study shows that, under an initially lower water depth in the dropshaft, immediate spillage is not guaranteed. However, the subsequent mixing of air from the horizontal pipe generated a less dense mixture, causing a change in pressure distribution along the tunnel, which drove the entire geyser mechanism. This study underscores the critical role of the initial conditions and geometric parameters in influencing geyser dynamics, offering practical guidelines for urban drainage infrastructure. Full article

Many regions in Europe face increasing issues with flooding and droughts due to changing rainfall patterns caused by climate change. For example, higher rainfall intensities increase urban flooding. Nature-based solutions (NbS) are suggested as a key mitigation strategy for floods. This study aims to address and mitigate the challenges faced in Tivoli natural park in Ljubljana regarding high peak discharges and low-flow issues in the creek entering the sewer system. The study involves setting up, calibrating and validating a Hydrologic Engineering Centre–Hydrologic Modelling System (HEC-HMS) model using available data. This study analyses NbS, such as small ponds, green roofs and permeable paving, to reduce peak discharge. Runoff was reduced by an average of 32.4% with all NbS implemented and peak discharge by 20 L/s. Permeable parking performed best, with an average runoff reduction of 6.4%, compared to 4.8% for permeable streets and 5.9% for green roofs. The ponds reduced peak discharge, although their effectiveness varied between rainfall events. Rainfall events with higher volumes and durations tended to overwhelm the proposed solutions, reducing their effectiveness. The ability of HEC-HMS to model NbS is also discussed. The curve number (CN) parameter and impervious % alterations to simulate NbS provided quantitative data on changes in runoff and discharge. Full article

Methane (CH₄) emissions from urban sewer systems represent a significant contributor to greenhouse gases, driven by anaerobic decomposition processes. This review elucidates the mechanisms underlying CH₄ production in sewers, which are influenced by environmental factors such as the COD/SO₄²⁻ ratio, temperature, dissolved oxygen, pH, flow rate, and hydraulic retention time. We critically evaluated the effectiveness of empirical, mechanistic, and machine learning (ML) models in predicting CH₄ emissions, highlighting the limitations of each. This review further examines control strategies, including oxygen injection, iron salt dosing, and nitrate application, emphasizing the importance of balancing CH₄ reduction with the operational efficiency of wastewater treatment plants (WWTPs). An integrated approach combining mechanistic and data-driven models is advocated to enhance prediction accuracy and optimize CH₄ management across urban sewer systems. Full article

The maintenance and inspection of sewer pipes are essential to urban infrastructure but remain predominantly manual, resource-intensive, and prone to human error. Advancements in artificial intelligence (AI) and computer vision offer significant potential to automate sewer inspections, improving reliability and reducing costs. However, the existing vision-based inspection robots fail to provide data quality sufficient for training reliable deep learning (DL) models. To address these limitations, we propose a novel multi-sensor robotic system coupled with a DL integration concept. Following a comprehensive review of the current 2D (image) and 3D (point cloud) sewage pipe inspection methods, we identify key limitations and propose a system incorporating a camera array, front camera, and LiDAR sensor to optimise surface capture and enhance data quality. Damage types are assigned to the sensor best suited for their detection and quantification, while tailored DL models are proposed for each sensor type to maximise performance. This approach enables the optimal detection and processing of relevant damage types, achieving higher accuracy for each compared to single-sensor systems. Full article

Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers. The framework encompasses positioning, defect detection, model deployment, 3D reconstruction, and the measurement of realistic pipelines. A lightweight Sewer-YOLO-Slim model is introduced, which reconstructs the YOLOv7-tiny network by adjusting its backbone, neck, and head. Channel pruning is applied to further reduce the model’s complexity. Additionally, a multiview reconstruction technique is employed to build a 3D model of the pipeline from images captured by the sewer robot, allowing for accurate measurements. The Sewer-YOLO-Slim model achieves reductions of 60.2%, 60.0%, and 65.9% in model size, parameters, and floating-point operations (FLOPs), respectively, while improving the mean average precision (mAP) by 1.5%, reaching 93.5%. Notably, the pruned model is only 4.9 MB in size. Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. The model is deployed on edge devices with the aid of TensorRT for acceleration, and the detection speed reaches 15.3 ms per image. For a real section of the pipeline, the maximum measurement error of the 3D reconstruction model is 0.57 m. These results indicate that the proposed sewer inspection framework is effective, with the detection model exhibiting advanced performance in terms of accuracy, low computational demand, and real-time capability. The 3D modeling approach offers valuable insights for underground pipeline data visualization and defect measurement. Full article

Under the dual pressures of climate change and urbanization, cities in China are experiencing increasingly severe flooding. Using the Yushan Lake area in Ma’anshan City, Anhui Province, as a case study, we employed the InfoWorks Integrated Catchment Management (ICM) hydraulic model to analyze the drainage and flood prevention system of the region and assess the current infrastructure for drainage and flood control. There are 117 pipelines with a return period lower than one year for stormwater and combined sewer systems, accounting for 12.3% of the total number of pipelines. The number of pipelines meeting the one-year but not the three-year return period standard is 700, representing 70.2%. Only 17.5% of the pipelines are capable of handling events exceeding the one-year standard. In simulating a 24 h, 30-year return period rainfall event, the results indicate that floodwater accumulation in the study area is predominantly between 0.15 m and 0.3 m. Most risk areas are classified as low risk, covering an area of 36.398 hectares, followed by medium and high-risk areas, which cover 8.226 hectares and 3.087 hectares, respectively. The Ma’anshan Yushan Lake area has, overall, certain flood control capabilities but faces flood risks during storms with return periods exceeding three years. This research offers valuable insights for improving urban flood management in Ma’anshan City through the development of a stormwater management model for the Yushan Lake area. Full article

Due to the impact of climate change, the importance of urban flood analysis is increasing. One of the biggest challenges in urban flood simulations is the complexity of storm sewer networks, which significantly affects both computational time and accuracy. This study aimed to analyze and evaluate the impact of sewer network simplification on the accuracy and computational performance of urban flood prediction by comparing different rainfall runoff methods. Using the hyper-connected solution for urban flood (HC-SURF) model, two rainfall runoff methods, the SWMM Runoff method and the Surface Runoff method, were compared. The sewer network simplification was applied based on manhole catchment areas ranging from 10 m² to 10,000 m². The analysis showed that the computation time could be reduced by up to 54.5% through simplification, though some accuracy loss may occur depending on the chosen runoff method. Overall, both methods produced excellent results in terms of mass balance, but the SWMM Runoff method minimized the reduction in analytical performance due to simplification. This study provides important insights into balancing computational efficiency and model accuracy in urban flood analysis. Full article