Underground Sewer Pipes Research Articles

Aerial characterization of surface depressions in urban watersheds

Digital elevation models (DEM) are one of the most fundamental inputs for hydrological modeling. It has been a common practice to remove all surface depressions in a DEM as they are assumed to be data errors. The emerging technology of unmanned aircraft systems (UAS) provides an opportunity to re-examine this assumption at the hyperspatial resolution. This study was the first attempt to characterize small surface depressions in urban environments using UAS imagery. Using an urban area in south Texas as the study site, UAS flights were conducted to yield hybrid DEMs at the resolution of 8–14 cm, coupled with comprehensive ground truth collection. Surface depressions identified from the UAS DEMs were first corrected based on the vertical accuracy of DEMs and then validated through field surveys, with comparisons to two existing LiDAR DEMs (1-m and 10-m). The hydrological impacts of different DEM-derived estimates of catchment depression storage were examined using the Curve Number method across different design storms. Results show that the UAS DEMs outperformed the LiDAR DEMs in describing the microtopographic control of urban overland flow and associated hydrological connectivity across built and natural features. The 8-cm UAS DEM revealed 926% more depression storage than the 10-m LiDAR DEM. This demonstrates a compelling correlation between increasing DEM resolution and enhanced quantification of depression volume. Consequently, the increased depression storage reduced surface runoff by 41% under a two-year design storm and 13% under a 200-year design storm. The results suggest a strong relationship between the DEM resolution and the derived depression estimates, aligning with the fractal nature of watershed systems. Also, the results indicate that the centimeter-level UAS DEMs were not immune from problems. They could yield fake depressions caused by factors such as vegetation, temporary street objects, and underground sewer pipes. The findings of this study suggest the need to quantify the relationships between DEM resolution and associated hydrological attributes and develop new digital drainage analysis algorithms that could effectively incorporate UAS data into urban hydrological modeling.

Age-dependent failure probabilities of corroding concrete sewer pipes under traffic loads

Concrete pipes are frequently used in the wastewater infrastructure. These buried structures are prone to ageing and deterioration, as well as damage under excessive stresses from traffic. This study presents the first set of temporal fragility models for underground concrete sewer pipes under the compounding effects of corrosion and truck loads. To address the high computational costs of fragility modeling, Bayesian Additive Regression Trees (BART) – a machine-learning based meta-model – are trained using a high-fidelity computational model of buried concrete pipes. The BART models are subsequently applied to develop age-dependent fragility models for serviceability and collapse of pipes conditional on truck loads. Considered uncertainties include statistically significant concrete and soil properties, truck load, and environmental factors that affect the corrosion of concrete sewer pipes. Results indicate that the direction of traffic stream, the degree of loss of wall thickness due to corrosion, and the maximum allowed truck load are critical factors that must be considered when planning inspection, rehabilitation, and retrofit of this underground infrastructure. According to the results, concrete sewer pipes buried in perpendicular to the direction of traffic flow are more vulnerable than those that are parallel to the direction of traffic flow. The results highlight that the probability of collapse is insignificant when it comes to vehicles weighing less than the design truck weight; however, the probability of collapse significantly increases for the moderate and severe corroded sewer pipes when it comes to the overweight vehicles. The age-dependent fragility models indicate that for sewer pipes buried perpendicular to the direction of traffic stream with service life of less than 25 years, the probability of the first major structural defect is less than 5%. This is also true for sewer pipes that have been in service for less than 45 years if the traffic stream is parallel to the buried pipe. The developed fragility models also constitute a key component of quantitative risk assessment of underground wastewater collection systems.

S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems

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.

Design and Implementation of a Video-Frame Localization System for a Drifting Camera-Based Sewer Inspection System

To reduce the cost of inspecting old sewer pipes, we have been developing a low-cost sewer inspection system that uses drifting wireless cameras to record videos of the interior of a sewer pipe while drifting. The video's data are transmitted to access points placed in utility holes and further transmitted to a video server where each video frame is linked to its capturing position so that users can identify the damaged areas. However, in small-diameter sewer pipes, locating drifting nodes over the full extent of the pipeline using Wi-Fi-based localization is difficult due to the limited reach of radio waves. In addition, there is the unavailability of a GNSS signal. We propose a function to link each video frame to a position based on linear interpolation using landmarks detected by the camera and image processing. Experiments for testing the accuracy of the localization in an underground sewer pipe showed that all utility holes were successfully detected as landmarks, and the maximum location estimation accuracy was less than 11.5% of the maximum interval of landmarks.

GPR surveys in enclosed underground sewer pipe space

To identify voids around underground concrete sewer pipes, a few studies have introduced conducting in-pipe Ground Penetrating Radar (GPR) inspection systems along the pipes. However, less research has focused on studying the implications of emission, propagation, and reception of electromagnetic waves in the enclosed environment of sewer pipes and their subsequent impact on the resulting radargrams. We defined simulation scenarios where we examine the influence of longitudinal and rotational survey types, antenna frequency, air–gap between antenna and sewer wall, antenna separation, and pipe wall’s mirroring effect on the resulting radargrams. Our results encourage practical considerations for designing in-pipe GPR surveys and outline the subsequent steps for automating the in-pipe GPR surveys.

A framework for synthetic image generation and augmentation for improving automatic sewer pipe defect detection

Sewer pipes are essential infrastructure for discharging wastewater. Regular pipe inspection is necessary to prevent malfunction of sewer systems, for which closed-circuit television (CCTV) crawlers are commonly used to capture images of the pipe interior. As manual assessment of pipe condition is labor-intensive and time-consuming, automated defect detection using computer vision and deep learning has been increasingly studied in recent years. However, deep learning approaches require large amount of annotated data for model training. Data collection in underground sewer pipes is expensive and difficult since they are inaccessible without the use of an inspection robot. Meanwhile, ground-truth annotation needs to be accurate and consistent, requiring massive time and expertise. This paper proposes a framework for synthetic data generation and augmentation to address the data shortage problem for sewer pipe defect detection. First, synthetic images of sewer pipes are generated by 3D modeling and simulation in virtual environment. The quality of the generated images is then enhanced using style transfer with reference to real inspection images. In addition, a contrastive learning module is developed to further improve the deep learning process for defect detection. Experiment results show that the average precision (AP) of the defect detection model is improved by 2.7% and 4.8% respectively after adding style-transferred synthetic images and applying the contrastive module. When both methods are applied, the AP of the model is boosted by 7.7%, from 22.22% to 23.92%, indicating the effectiveness of our proposed approaches. This study is expected to alleviate the burden on data collection and annotation for applying deep learning models in defect detection.

Towards an automated condition assessment framework of underground sewer pipes based on closed-circuit television (CCTV) images

There is a growing trend of using computer vision techniques for interpreting Closed-Circuit Television (CCTV) inspection videos of sewer pipes. Previous studies mainly focus on detecting defect types and locations in CCTV images, yet limited systematic approaches are available for automatically evaluating defect severity and sewer condition with references to existing standards. This study proposes a framework for evaluating defect severity and sewer condition automatically from CCTV images using computer vision methods, which includes (1) required information definition for sewer condition assessment, (2) pipe joint detection and fitting by image processing techniques to obtain cross section area, (3) defect detection and segmentation to obtain defect type, location and area, and (4) evaluation of defect severity and sewer condition. Particularly, three deep learning -based defect detection models are developed, among which the model based on Faster R-CNN (regional convolution neural network) outperforms others with higher accuracy and is used for detecting defect type and location in the image. Meanwhile, an innovative semantic segmentation model is applied for segmenting defects to extract defect area in the image. In the validation, our framework performs well in defect detection with an average precision, recall and F1 of 88.99%, 87.96%, and 88.21% respectively. More importantly, the framework evaluates Operation and Maintenance (O&M) defects more accurately by precise calculation and generates the overall condition gradings that are mostly consistent with professional inspectors, only with an average deviation of 3.06%. Our framework can assist the review of inspection videos and lays the basis for fully automated sewer assessment and maintenance planning in the future. Without constraints on the assessment codes and computer vision methods, the framework is adaptable to evaluating sewer condition in different regions and can achieve better performance by integrating with cutting-edge vision techniques.

A high-fidelity computational investigation of buried concrete sewer pipes exposed to truckloads and corrosion deterioration

As wastewater systems are reaching critical levels of aging and deterioration, simulation of realistic pipe behaviors is ever more needed to analyze and manage the associated risks. Developing such models is challenging, in part due to the complexity of aging and deterioration impacts on the structural and functional performance of pipes. This study investigates the impacts of a number of design and uncertain variables on the serviceability performance of concrete sewer pipes throughout their service life. This is facilitated by a high-fidelity three-dimensional Finite Element (FE) model of concrete sewer pipes, surrounding soil, and pavement layers in ANSYS platform. The FE model is capable of capturing nonlinear behaviors of concrete, soil, and pipe-soil interactions, in addition to simulating crack formation and crushing of concrete pipes. Corrosion impacts are simulated by sequentially removing concrete layers inside the pipe so that corrosion effects from previous years are taken into account. The influence of linear versus nonlinear assumptions for soil and pipe-soil interactions on the performance of the pipes especially crack width are investigated for cases where the models are subjected to external truckloads. Moreover, impacts of most common trench installations in terms of bedding, side-fill configuration, and soil properties are analyzed for the entire service life of the pipe. Results reveal that the soil surrounding the pipe needs to be modeled as a nonlinear material regardless of the soil grain size. It was observed that the error associated with modeling soil and soil-pipe interactions as linear increases as the pipe ages and the extent of corrosion increases. Results of this investigation can guide the development of reliable and computationally efficient numerical models for deteriorating underground concrete sewer pipes.

A Preliminary Study on Leakage Detection of Deteriorated Underground Sewer Pipes Using Aerial Thermal Imaging

Due to less visibility, underground facilities such as sewer pipelines are subjected to degradation until a major failure occurs. So, leakage detection of deteriorated underground sewer pipes is necessary to prevent expensive rehabilitation costs. The major causes of the frequent occurrence of ground subsidence and sewer leakage are excessive use of groundwater, reckless exploitation of natural resources, etc. In this study, the aerial thermal imaging (ATI) technique has been used to identify the leakage points in sewer pipelines by analyzing surface thermal diffusion behavior of the ground surface. Leakage is suspected at points of anomalous temperature variation. Then, ground-penetrating radar and closed-circuit television surveys have been carried out to strengthen the suspicion of sewer leakage. Irregular, erratic parabolic-shaped lines are observed at the points where the leakage is suspected through ATI. Finally, the test-pit excavation and simple cone penetration tests have been carried out which confirms the sewer leakage points. The leakage points are confirmed by a greater depth of penetration. The results of the cone penetration test carried out in E-21 and 23-24 sewer pipelines show that the depth of penetration at the leakage points is about 35 cm, which is greater than the depth of penetration at other points. So, it can be concluded that the aerial thermal imaging technique is an effective method to predict leakage in deteriorated underground sewer pipelines.

A Combined Probabilistic Approach for Natural Hazards Assessment of Soil-Sewer Pipes (S-SP) Systems

The structural failure prediction of underground sewer pipes systems seems very complicated due to the natural hazards of soils in which these elements are buried. The apparition of first cracks and notches in sewer pipes parts is governed by the interaction model of soil-sewer pipes system (S-SP)parameters mainly, the constitutive material laws of soil and sewer pipe materials. The detection of critical sections where the structural damages are highly probable is the focus point of this present study. Based on probabilistic analysis of stochastic modelling results (Monte Carlo Method) of random soil properties, the mechanical behaviour of a part of sewer pipe is analysed in terms of settlements and flexural stress distribution fluctuations. A parametric study is performed to quantify the effect of correlation length (Lc) and soils types on the structural reliability of underground sewer pipes. This current structural analysis offers to engineers and researchers a useful numerical tool in order to allow them the well understanding of the structural behaviour of buried sewer pipes by considering the spatial variability of soil geo-mechanical characteristics which reflects the soil natural process of formation, its aggregation and heterogeneity. The obtained numerical results show that the probabilistic analysis of the spatial variability of soil properties into structure numerical modelling of sewer pipes presents an accurate approach for the prediction of structural responses of waste water transportation infrastructures particularly, if the sewer pipe lengths are relatively significant and buried into several classes of soils along sewer pipe networks.

Underground sewer pipe condition assessment based on convolutional neural networks

Surveys for assessing the condition of sewer pipeline systems are mainly based on video surveillance or CCTV, which is a time-consuming process that relies heavily on human labor because an operator has to watch videos, looks for defects and decides the defect's type manually. Previous research required suitable handcrafted features that were inefficient in analyzing sewer pipeline condition, so a robust and efficient framework is crucial as it eliminates the time-consuming tasks and helps the operator access condition of sewer systems more efficiently. This study proposes a defect classification system on CCTV inspection videos based on convolutional neural networks (CNN). The dataset was manually constructed and validated by extracting the images from CCTV videos, and the images were labeled according to six predefined defects. The CNN model was fine-tuned before training, and trained on a total of 47,072 images (256 × 256 pixels). The highest recorded accuracy was at 96.33%. As a result, the presented framework will motivate the finding of a more robust model that automatically and precisely evaluates the condition of sewer pipeline systems using CCTV and encourages the integration of the proposed model in real applications.

Logistic regression model for sinkhole susceptibility due to damaged sewer pipes

The occurrence of anthropogenic sinkholes in urban areas can lead to severe socioeconomic losses. A damaged underground sewer pipe is regarded as one of the primary causes of such a phenomenon. This study adopted the best subsets regression method to produce a logistic regression model that evaluates the susceptibility for sinkholes induced by damaged sewer pipes. The model was developed by analyzing the sewer pipe network as well as cases of sinkholes in Seoul, South Korea. Among numerous sewer pipe characteristics tested as explanatory variables, the length, age, elevation, burial depth, size, slope, and materials of the sewer pipe were found to influence the occurrence of sinkhole. The proposed model reasonably estimated the sinkhole susceptibility in the area studied, with an area value under the receiver-operating characteristics curve of 0.753. The proposed methodology will serve as a useful tool that can help local governments to choose a cavity inspection regime, and to prevent sinkholes induced by damaged sewer pipes.

Needs and potential of unmanned vehicles in sewers

Sewer systems are complex and large structures located under the ground. Their good functioning is a strategic issue which requires regular inspections and maintenance operations. These interventions involve important risks and constraints regarding health and security of operators as well as accessibility to structures and cost of these operations. In this context, unmanned vehicles offer new opportunities to reduce or simplify human interventions in the sewers. As an example, Suez Water Spain has developed a first prototype of unmanned aerial vehicle dedicated to inspection of underground retention tanks and big sewer pipes. The information collected by the drone are automatically integrated into the information system and the decision-making process. Even if the first tests performed with this drone have been successful, there are still a number of pending technological issues that needs to be solved for operational use: positioning, data transmission, trajectory, etc.

A New Image Model for Predicting Cracks in Sewer Pipes based on Time

Sewer overflows may cause communities to be vulnerable to various health problems and other monetary losses. This puts a lot of burden on responsible to minimize end user complaints. Therefore, crack prediction would be helpful to facilitate decision makers to control sewer overflow problems and prioritize inspection and rehabilitation needs .The accurate prediction of current underground sewer pipe cracks must be done before any pipe crashing with enough period of time to enable rehabilitation and replacement intervals, appropriate remedial methods and preventing sewer pipes crashing. Unfortunately, traditional technologies and models approaches have been limited to predict the development of sewer pipe cracks. In this paper, we address the problem of crack prediction of such cracks. This paper provides a proposed model which predict crack and cracks developments in any period of time that may occur in weak areas of a network of pipes.. We evaluate our results by comparing them with those obtained by many other models. The accuracy percentage of this model exceeds 90% and outperforms other approaches.

Exposure to Airborne Endotoxins among Sewer Workers: An Exploratory Study

Exploratory bioaerosol sampling was performed in order to assess exposure to airborne endotoxins during sewer work. Personal samples were collected in underground sewer pipes using 37-mm closed-face cassettes containing fibreglass filters (CFC-FG method) or polycarbonate filters (CFC-PC method). Endotoxins were quantified using the limulus amoebocyte lysate assay. Concentrations of airborne endotoxins at sewer workplaces (16-420 EU m(-3)) were higher than those measured outside the sewer network (0.6-122 EU m(-3)). Sewer worker exposure to airborne endotoxins depended on the workplace and on the tasks. Exposure levels were the highest for tasks involving agitation of water and matter, especially for 'chamber cleanup' and 'pipes cleanup' with a high-pressure water jet. Airborne endotoxin levels at the workplace tended to be higher when CFC-FG was used as the sampling method rather than CFC-PC. The adjusted mean of the measured concentrations for CFC-PC represents 57% of the mean observed with CFC-FG. The number of samples collected in the descriptive study was too low for drawing definitive conclusions and further exposure investigations are needed. Therefore, our exploratory study provides new exposure data for the insufficiently documented sewer working environment and it would be useful for designing larger exposures studies.

Impervious surfaces and sewer pipe effects on stormwater runoff temperature

Summary The warming effect of the impervious surfaces in urban catchment areas and the cooling effect of underground storm sewer pipes on stormwater runoff temperature are assessed. Four urban residential catchment areas in the Cities of Guelph and Kitchener, Ontario, Canada were evaluated using a combination of runoff monitoring and modelling. The stormwater level and water temperature were monitored at 10 min interval at the inlet of the stormwater management ponds for three summers 2009, 2010 and 2011. The warming effect of the ponds is also studied, however discussed in detail in a separate paper. An artificial neural network (ANN) model for stormwater temperature was trained and validated using monitoring data. Stormwater runoff temperature was most sensitive to event mean temperature of the rainfall (EMTR) with a normalized sensitivity coefficient (Sn) of 1.257. Subsequent levels of sensitivity corresponded to the longest sewer pipe length (LPL), maximum rainfall intensity (MI), percent impervious cover (IMP), rainfall depth (R), initial asphalt temperature (AspT), pipe network density (PND), and rainfall duration (D), respectively. Percent impervious cover of the catchment area (IMP) was the key parameter that represented the warming effect of the paved surfaces; sensitivity analysis showed IMP increase from 20% to 50% resulted in runoff temperature increase by 3 °C. The longest storm sewer pipe length (LPL) and the storm sewer pipe network density (PND) are the two key parameters that control the cooling effect of the underground sewer system; sensitivity analysis showed LPL increase from 345 to 966 m, resulted in runoff temperature drop by 2.5 °C.

Study of a double subsurface snow-water utilization system for the melting of snow using the waste heat of urban sewage

This study describes technology that melts snow using the waste heat of urban sewage and reclaims snowmelt on site using a double subsurface constructed snow-melting system, allowing the organic integration of snow utilization and landscape design in cities with long winters. This system highlights the thermal energy cycle, in which underground urban sewer pipes and the considerable amount of low-temperature waste heat that they contain are utilized along with a small-temperature-rise heat pump to ultimately melt snow. The temperature of sewage in the winter usually ranges from 4 to 10°C in cities, representing a high-value resource that can be developed and utilized. In this system, an indirect heat exchanger heat pump, which uses sewage as its heat source, is employed, and the heat is exchanged between the sewage and intermediary water in the heat exchanger. This is followed by heat removal and the emission of radiation into a working substance in the condenser and evaporator, respectively. Then, the water temperature in the assemblies of the heat pipes is increased, thereby increasing the coefficient of performance (COP) to 10 and achieving the concentrated reuse of the distributed waste heat. The double subsurface snow-melting system consists of snow-melting wells and double subsurface tanks. The surface plant residues and frozen layer constitute a natural insulation layer to maintain the water-purification activity of the lower-layer, low-temperature anaerobes. The removed snow is dumped into the snow-melting wells by workers and heated by the hot grate and pipes on the sidewalls and at the bottom of the wells. The snowmelt water flows into the pretreatment tank and grill, and then, it enters the lower layer of the double subsurface tanks. The lower-layer gravel fills are colonized by low-temperature oligotrophic bacteria. The removal of samples for the analysis of BOD5, COD, and SS is efficient, and the quality of the output thereby satisfies the standards for municipal water use.

Design and Development of Vision Based Blockage Clearance Robot for Sewer Pipes

Robotic technology is one of the advanced technologies, which is capable of completing tasks at situations where humans are unable to reach, see or survive. The underground sewer pipelines are the major tools for the transportation of effluent water. A lot of troubles caused by blockage in sewer pipe will lead to overflow of effluent water, sanitation problems. So robotic vehicle that is capable of traveling at underneath effluent water determining blockage using ultrasonic sensors and clearing by means of drilling mechanism is done. In addition to that wireless camera is fixed which acts as a robot vision by which we can monitor video and capture images using MATLAB tool. Thus in this project a prototype model of underground sewer pipe blockage clearance robot with drilling type will be developed DOI: http://dx.doi.org/10.11591/ijra.v1i1.192

Innovative method for assessment of underground sewer pipe condition

The accurate evaluation of current underground infrastructure must be done before any crucial decisions regarding lifecycle analysis, rehabilitation and replacement intervals and appropriate remedial methods can be made. Unfortunately, traditional technologies and management approaches have been limited by the use of insufficient data in the evaluation of the structural integrity of an aged infrastructure. This paper describes an innovative method for assessment of underground pipe, utilizing Ground Penetrating Radar (GPR) and Digital Scanning and Evaluation Technology (DSET) to collect accurate information about the condition of a buried pipeline. Prediction modeling, using logistic regression, to measure the extent of deterioration of the infrastructure system, is described. A case study applying this innovative technology to sections of large diameter PVC lined concrete pipe in the City of Phoenix, Arizona, is presented.

Repairing the Damage of Hurricane Ivan

This article reports on the efforts of the Emerald Coast Utilities Authority (ECCUA) in Escambia County, Florida, (including Pensacola), to restore water service in the badly damaged resort and beach areas after Hurricane Ivan roared through in September 2004, damaging underground water and sewer pipes, service lines, and residential plumbing fixtures. The emergency planning that was in place prior to the hurricane worked fairly well, but modifications will likely be made.