Sewer Pipelines Research Articles

Design and Implementation of Three-Channel Drainage Pipeline Ground Penetrating Radar Device.

In order to solve the current problems that conventional video inspection can only detect, as an internal pipeline defect and drainage pipeline radar inspection device detects in a single direction and at radar frequency in water pipeline defect detection, a three-channel drainage pipeline ground penetrating radar (GPR) inspection device was designed and developed, the assembly and commissioning of the device prototype were completed, and an actual engineering test application was carried out. Focusing on the problem that the detection direction and depth of the single-channel detection device are limited, a three-channel drainage pipeline GPR inspection device is designed to realize the synchronous detection of the inside of the pipeline, the pipeline body, and the external environment of the pipeline, improving the detection depth and efficiency. According to the design scheme of the three-channel drainage pipeline GPR inspection device, the assembly of the device prototype was completed. The device contains three radar channels, the top of the main frequency of the antenna is 1.4 GHz, the two sides are 750 MHz, the video camera has a pixel count of 4 million, and the positioning accuracy is less than 1 mm, the waterproof grade is IP68, the detection accuracy of pipe deformation (slope) is 0.1°, the detection depth outside the pipe is 1.2 m, and the detection accuracy of corrosion thickness is 15 mm. In a practical application of the device, the Jianguomenqiao sewage pipeline in Beijing, China, was tested, resulting in the discovery of 87 defects, including 39 loose soil areas at the bottom of the pipe exterior, 40 void areas, and 8 cavities.

Cost optimization of sewage pipelines inspection

Sewage pipelines are considered to be the invaluable asset in municipalities. Inspection process plays a vital role in operation and maintenance (O&M) strategies applied by the concerned municipalities. Fault inspection and regular maintenance of sewage pipelines undoubtedly ensure longer service life; however, it could be a costly process in many cases. The objectives of this study are: indicating the significance of sewer pipelines inspection, defining methods of inspection, discussing the economic factors that affect the selection of appropriate methods of sewer pipelines inspection, and optimizing the most suitable method of sewer pipelines inspection in terms of cost effectiveness. A questionnaire survey including a list of factors of inspection of sewer pipelines in Egypt (from previous researches) was used for information collection (this questionnaire targets civil engineers based on their experience). Then, the collected data was analysed using descriptive statistics, to catch on the factors and methods of inspection of sewer pipelines (first examine the survey response using sample size then graph the results of response). Accordingly, the need for cost optimization solutions for such challenges, which is rapidly increasing, with the purpose of prioritizing the maintenance needs and focusing the municipal engineers’ efforts has emerged. The solution requires a systematic approach to diagnose the fault in the network along with regular customized inspection for performance evaluation. This research presents an optimization model for inspection of deteriorated sewage pipelines. The optimization is focused on the overall cost of the inspection process by reaching the optimum approach to diagnose and maintain the sewage pipelines. In this research, Genetic Optimization Algorithm (GA) is applied to various methods of inspects with the intention of achieving the possible optimal cost. The methods of inspection were selected in line with the outcome of a questionnaire completed by field experts. The research revealed that the acoustic inspection method with the minimum work force is the most cost-friendly method.

Development of Comprehensive Rating for the Evaluation of Sewer Pipelines

The Pipeline Assessment and Certification Program (PACP) was developed by the National Association of Sewer Service Companies, the industry-accepted protocol for condition rating sewer pipes in the US. The PACP method relies exclusively on visual inspections performed using closed-circuit television (CCTV), where certified operators observe existing structural and operation and maintenance (O&M) defects limitation of the PACP method is that it does not use pipe characteristics, depth, soil type, surface conditions, pipe criticality, and capacity, nor the distribution of structural defects or history of preventative maintenance to determine the condition rating of the sewer pipe segment. Therefore, this research addresses the limitations of the PACP with the development of a new wastewater pipe rating system known as the comprehensive rating (CR) system. The CR evaluation system is based on a commonly used decision-making approach known as the analytical hierarchy process (AHP) to analyze problems and issues. The new CR model incorporates pipe characteristics (age, corrosion, diameter, and shape), external characteristics (depth, soil type, traffic loading, waste type, seismic zone), and hydraulic characteristics (structural score, O&M score, and repair history). The results showed a below-average validity percentage because linear regression assumes a linear relationship between the input and output variables but the relationship between the response and the predictor is not linear.The CR methodology can be improved by increasing the number and variability of experts who evaluate the condition of existing sewer pipes, enhancing geospatial data for locational accuracy, and establishing evaluation standards for inspectors to minimize human error. More research is needed to determine a suitable nonlinear relation that can be used to model data from this new comprehensive rating system.

Failure analysis of the sewage pipeline on a shale gas platform

The wall thickness of the sewage pipeline on a shale gas platform is seriously reduced. In order to explore the specific reasons for the pipeline failure, this paper carried out analysis by means of macroscopic inspection, mechanical property test, chemical composition analysis of base metal and corrosion product analysis. The analysis results show that the site of this pipeline corroded most seriously is at 5 o ′clock and 12 o ′clock near the outlet, and the corrosion of liquid accumulation containing CO2 is the main reason for the serious failure of the inner wall of this pipeline. This paper explores the specific reasons for the failure of the sewage pipeline on a shale gas platform, and provides a reference for the corrosion prevention of the sewage pipeline.

Predicting the performance of existing pre-cast concrete pipes using destructive and non-destructive testing techniques

One of the most significant and critical urban assets for a sustainable community is the sewer pipeline network and water distribution system. Water sewer networks and distribution systems have a definite service life span to provide continuous facilities to end users. Therefore, it is pertinent to continuously evaluate the condition of water and sewer concrete pipelines to ensure the reliable, sustainable, and cost-efficient transport of water and sewerage for the safety of society. The condition assessment is commonly carried out by visual observations followed by some non-destructive testing methods. However, it is the need of the hour to shift assessment methods to advance assessment techniques to save time and money for our community. Currently, in this project, the condition assessment of pre-cast concrete pipes was carried out by destructive and non-destructive methods. Different test trials i.e., ultra-sonic pulse velocity, Schmidt hammer also known as rebound hammer test, visual inspection, three edge bearing test, and core cutting test on the old buried and new concrete pipes were performed. It was observed that concrete used for the construction of existing precast concrete pipes still has better quality indices after 20 years as compared to that of concrete of new pipes. However, steel has deteriorated with time and clear corrosion of steel was identified in existing pre-cast concrete pipes. At the same time, it was observed that there should be an automated mechanism to continuously asses the condition of pre-cast existing pipes which will address the sustainable development goals (SDG 6, 9, 11). Consequently, it can be said that condition assessment of pre-cast concrete pipes will lead to sustainable societies and infrastructure.

Multi-channel capacitive sensing system for cross bore detection and classification by machine learning

Cross bore is an intersection of an existing underground pipeline such as a sewer pipeline by a second utility such as a gas pipeline installed using the trenchless technique, which could lead to severe incidents like explosions. There are hundreds of thousands of existing cross bores in the U.S., at an estimated average rate of 0.4 per mile of pipeline reported by Cross Bore Safety Association, which is at high risk and a significant threat to public safety if not well detected, characterized, and mitigated. The research on high-accuracy nondestructive evaluation (NDE) methods that can identify cross bore areas more effectively and efficiently under challenging field environment is of vital importance. This paper proposes a multi-channel capacitive sensing system for cross bores detection and classification, which passes through the gas pipe to inspect the dielectric property changes of surrounding materials and indicates the existence of cross bores. A 3-D simulation modeling tool was developed to optimize the footprint of multi-channel sensor, and the effect of four cross bore types on the received capacitance values was investigated. The lab-scale experiments were performed using the developed multi-channel capacitive sensing system through a soil box setup, and the experimental results indicated that the developed system can identify the four types of cross bores. To achieve an automated decision making for the cross bores detection and classification, machine learning (ML) algorithms were developed through the experimental dataset, and it was found that the subspace k-nearest neighbors (SKNN) performed better with a high classification accuracy. Finally, field test validation was performed at the pipe farm (Gas Technology Institute, IL, USA) and the superior capability of the developed sensing system in identifying cross bores as well as other key parts in gas pipelines including butt fusion and saddle fitting with coil was demonstrated.

Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data

Frequent inspections of reinforced concrete sanitary sewer pipelines (RCSSPs) are crucial for performing a proper life cycle management strategy; however, due to the large inventory of SSPs, only limited inspection data is typically available. In this work, a data-driven method for condition assessment of RCSSPs is developed in a probabilistic framework, wherein the pipe wall erosion is evaluated using LiDAR inspection data. Results indicate that the deteriorated inner concrete wall geometry of RCSSPs is best characterized by the half-normal probability density function (PDF). The final product of the proposed condition assessment algorithm is a probabilistic estimate of remaining service life using the inspection LiDAR point cloud data (PCD). The effect of different reliability methods are compared with the proposed methodology. The results are validated using available closed-circuit television (CCTV) images, previous research that employs the same inspection data, and Monte Carlo Simulation (MCS) method. The proposed algorithm provides an automated framework that can be utilized with any PCD associated with non-destructive inspections.

A Deterioration Model for Sewer Pipes Using CCTV and Artificial Intelligence

Sewer pipeline failures pose significant threats to the environment and public health. To tackle these repercussions, many deterioration models have been developed to predict the conditions of sewer pipes, most of which are based on CCTV inspection reports. However, these reports are prone to errors due to their subjective nature and human involvement. More importantly, there are insufficient data to develop prudent deterioration models. To address these shortcomings, this paper aims to develop a CCTV-based deterioration model for sewer pipes using Artificial Intelligence (AI). The AI-based model relies on the integration of an unsupervised, multilinear regression technique and Weibull analysis. Findings derived from the Weibull deterioration curve indicate that the useful service life for concrete and vitrified clay pipes are 79 years and 48 years, respectively. The regression models show that the R2 value for vitrified clay sewer pipes, concrete sewer pipes, and ductile iron sewer pipes are 71.18%, 71.47%, and 81.51%, respectively, and 73.69% for concrete stormwater pipes. To illustrate the impact of various factors on sewer pipes, sensitivity analyses under different scenarios are conducted. These analyses indicate that pipe diameter has a significant influence on sewer pipe deterioration, with little impact on stormwater pipes. These findings would guide decision makers in identifying critical pipes and taking necessary precautionary measures. Further, this provides a sound basis for prioritizing maintenance actions, which would pave the way for designing sustainable urban drainage systems for cities.

Attention‐guided multiscale neural network for defect detection in sewer pipelines

AbstractSanitary sewer systems are major infrastructures in every modern city, which are essential in protecting water pollution and preventing urban waterlogging. Since the conditions of sewer systems continuously deteriorate over time due to various defects and extrinsic factors, early intervention in the defects is necessary to prolong the service life of the pipelines. However, prior works for defect inspection are limited by accuracy, efficiency, and economic cost. In addition, the current loss functions in object detection approaches are unable to handle the imbalanced data well. To address the above drawbacks, this paper proposes an automatic defect detection framework that accurately identifies and localizes eight types of defects in closed‐circuit television videos based on a deep neural network. First, an effective attention module is introduced and used in the backbone of the detector for better feature extraction. Then, a novel feature fusion mechanism is presented in the neck to alleviate the problem of feature dilution. After that, an efficient loss function that can reasonably adjust the weight of training samples is proposed to tackle the imbalanced data problem. Also, a publicly available dataset is provided for defect detection tasks. The proposed detection framework is robust against the imbalanced data and achieves a state‐of‐the‐art mean average precision of 73.4%, which is potentially applied in realistic sewer defect inspections.

Time-Variant System Reliability Analysis of Concrete Sewer Pipes under Corrosion Considering Multiple Failure Modes

This paper presents a framework for time-variant system reliability analysis of concrete sewer pipes considering multiple modes of failure. Sewer pipelines are among critical infrastructure systems, designed to collect and transport wastewater away from communities. Such pipes are typically subjected to corrosion and subsequent damage because of continuous contact with corrosive materials. Corrosion is here modeled in terms of the reduction in the pipe wall thickness. In the proposed framework, prevailing uncertainties in the characteristics of wastewater fluid, geometric properties of fluid cross section, and corrosion-related parameters of concrete are quantified as random variables. Next, various modes of failure under bending, shear, excessive cracking, and cover loss are formulated as multiple limit-state functions. This leads to a general system reliability problem defined as a series system of parallel subsystems, i.e., cut sets. Further, by introducing the corrosion model into the limit-state functions, a time-variant system reliability problem is established in which the integration domain is conditioned to the corrosion level at each point in time. By integrating over the conditional probabilities through a rejection sampling method, the failure probability is computed given the corrosion level over the desired time span. The results show that the failure probability increases sharply with time and corrosion level. For instance, the failure probability exceeds 80% for pipes over 50 years and at corrosion levels of over 40%. Moreover, the sensitivity analysis shows that the acid-consumption capacity of the pipe material, the pH-related factor, and the proportion of S2− dissolved highly influence the failure probability. The maximum effects of these parameters occur within 0%–10% corrosion. The results of this study can be used to evaluate the failure probability of sewer pipes given their age and current corrosion level.

Full‐scale shaking table tests on soil liquefaction‐induced uplift of buried pipelines for buildings

AbstractThe uplift effect induced by soil liquefaction on buried pipelines that are critical to the serviceability of buildings, including potable water, natural gas, and sewage pipelines, was experimentally investigated. A full‐scale physical model of pipelines buried in liquefiable ground comprising distribution mains and service lines that connect distribution mains and buildings was built. Shaking table tests were conducted on this model with actual earthquake records utilized as the input motions. The performance of gravel as pipe ditch backfill for mitigating the liquefaction‐induced uplift was also evaluated. During the tests, accelerometers and piezometers were installed in free‐field (FF) soil and around the pipelines to reveal the pipeline–soil interaction and the soil liquefaction development. Wire potentiometers were also utilized to monitor any uplift or subsidence of the pipelines. Results indicate that the distribution mains for natural gas and sewage experienced an uplift during liquefaction of ground specimen. The excess porewater pressure buildup level around the uplifted pipelines was generally lower than that in FF soil and was even lower on top of the pipeline circumference, which may be attributed to the pipeline–soil interaction. Furthermore, the practicability of an existing simplified model for anti‐uplift stability assessment and its corresponding uplift force estimation were examined based on aforementioned observations. The findings indicate that the uplift effect for large‐diameter pipelines may be underestimated by simply using this simplified model. This research can be used as a reference for the seismic design of buried pipelines, which is beneficial for disaster prevention and mitigation.

Shifts in the sources and fates of nitrate in shallow groundwater caused by agricultural intensification intensity: Revealed by hydrochemistry, stable isotopic composition and source contribution

Groundwater nitrogen (N) pollution in intensive agricultural regions is increasing globally, and understanding the effects of increasing agricultural intensity on the sources and fates of N is critical for the effective control of groundwater pollution. Using multiple stable isotopes (δ15N-NO3—, δ18O-NO3—, δ18O-H2O, δD-H2O), hydrochemistry and the Bayesian stable isotope mixing model, the sources and fates of N were expounded, and their contributions were quantified in shallow groundwater (SG, n = 325) from an intensive agricultural region, a facility agricultural region and a cropland fallow region. We found that NO3— accounted for 60% of total N, and 52% of SG samples exceeded the WHO NO3— drinking water threshold (10 mg N L−1). Each N form concentrations and NO3— exceeding the standard rate in the intensive and facility agricultural regions were significantly higher than those in the cropland fallow region. Soil N, N fertilizer and manure and sewage were the dominant NO3— contributors in the SG of intensive agricultural regions, contributing to 66%, 21% and 10% of NO3—, respectively. The highest contributions of manure and sewage occurred in the cropland fallow region (46%), followed by the soil N (33%) and N fertilizer (13%), while the N sources in the SG from the facility agricultural region were dominated by manure and sewage (35%), soil N (31%) and N fertilizer (25%). Denitrification was the primary N biogeochemical cycle process in SG from the intensive and facility agricultural regions, whereas nitrification mainly occurred in the cropland fallow region. These results indicated that the shifts in the sources and fates of N in SG were controlled by agricultural intensification intensity, and soil N and manure and sewage, rather than N fertilizer, may become the most dominant N sources in SG in intensive agricultural regions. The N contamination control in SG should pay attention to improving septic tanks and sewage pipelines, the scientific and reasonable stacking of manure, and reducing the soil N pool in intensive agricultural regions.

Deep Learning Based Underground Sewer Defect Classification Using a Modified RegNet

The sewer system plays an important role in protecting rainfall and treating urban wastewater. Due to the harsh internal environment and complex structure of the sewer, it is difficult to monitor the sewer system. Researchers are developing different methods, such as the Internet of Things and Artificial Intelligence, to monitor and detect the faults in the sewer system. Deep learning is a promising artificial intelligence technology that can effectively identify and classify different sewer system defects. However, the existing deep learning based solution does not provide high accuracy prediction and the defect class considered for classification is very small, which can affect the robustness of the model in the constraint environment. As a result, this paper proposes a sewer condition monitoring framework based on deep learning, which can effectively detect and evaluate defects in sewer pipelines with high accuracy. We also introduce a large dataset of sewer defects with 20 different defect classes found in the sewer pipeline. This study modified the original RegNet model by modifying the squeeze excitation (SE) block and adding the dropout layer and Leaky Rectified Linear Units (LeakyReLU) activation function in the Block structure of RegNet model. This study explored different deep learning methods such as RegNet, ResNet50, very deep convolutional networks (VGG), and GoogleNet to train on the sewer defect dataset. The experimental results indicate that the proposed system framework based on the modified-RegNet (RegNet+) model achieves the highest accuracy of 99.5 compared with the commonly used deep learning models. The proposed model provides a robust deep learning model that can effectively classify 20 different sewer defects and be utilized in real-world sewer condition monitoring applications.

Моделирование процесса инфильтрации грунтовых вод в самотечные трубопроводы с использованием программного комплекса ANSYS CFX

The article is devoted to the development of models of groundwater infiltration processes into sewer pipelines using the ANSYS CFX program. Using the finite element method, the program allows implementing a simulation of the filtration process through a slit hole in the pipeline wall. Various cases of the location of the hole and the inflow of groundwater to it are regarded, as well as possible options for setting the porous domain. The models obtained make it possible to visualize the process of groundwater inflow, evaluate quantitative indicators of the inflow of infiltration waters into drainage systems, and assess the speeds and flow rates of the infiltered liquid.

Optimization Design and Motion Control of an Inchworm-Like Soft Pipe Robot for Sewer Pipeline Detection

Optimization Design and Motion Control of an Inchworm-Like Soft Pipe Robot for Sewer Pipeline Detection

Design of Municipal Sewer Pipe Jacking under Complex Geological Conditions in Plateau Regions

The drainage and sewage interception project hi Zhaoyang District involved the installation of a DN1000 municipal sewer pipeline with a total length of 1541 m. Considering factors such as geological conditions, pipe diameter, buried depth, groundwater level, as well as the impact of nearby underground and aboveground structures and traffic, the trenchless construction method of pipe jacking was selected for implementation. The pipeline followed the route of Weiba Road, crossed Jinying Avenue, and then proceeded along the Second Ring East Road until it reached the bank of Yaowan River. The project design took into account practical considerations, including the sewer pipe diameter, material selection, pipeline routing, construction of pipe jacking starting shafts, and the design and modification of arriving shafts, in line with the specific engineering requirements.

Hydraulic characteristics of sewer pipelines with internal deposits

This article focusses on the review of formulas for the hydraulic calculation of gravity drainage networks with internal deposits. In the existing practice of calculating gravity flow networks, there are methods of hydraulic calculation only for new pipes. At the same time, on the walls of pipelines, under certain conditions, layers of deposits with different structure can be formed, which affect the values of hydraulic characteristics of the system. The aim of the work is to identify the calculation dependencies for pipelines with deposits in gravity drainage networks. Hypotheses and formulas for the pipeline calculation, as well as the existing provisions of regulatory documentation in the field of wastewater disposal are used. Calculation dependences for pipelines with deposits are revealed, and the concept of reduced diameter of pipes is introduced. The directions of further study of influence of thickness of a layer of deposits in a flume part of pipes on values of their hydraulic characteristics and necessity of development of tables for hydraulic calculation of pipes of gravity drainage networks with internal deposits are allocated.

Satellite Imaging Techniques for Ground Movement Monitoring of a Deep Pipeline Trench Backfilled with Recycled Materials

The damage to pipeline infrastructures caused by reactive soils has been a critical challenge for asset owners. Sustainable backfilling materials have recently gained interest to stabilize highly reactive zones as a pre-emptive approach towards sustainability. In this study, two adjacent sections of a sewer pipeline trench in Melbourne, Australia were backfilled with two blends of 100% recycled aggregates. The sites were monitored for ground deformations during October 2020–February 2022 (17 months) using surveying techniques. Interferometric synthetic aperture radar (InSAR) techniques and algorithms were also employed to estimate the ground movements of the sites and surrounding regions. The cross-validation of deformation results achieved from both techniques enabled an in-depth analysis of the effectiveness of the recycled aggregates to address reactive soil issues in urban developments. Observational deformation data and their spatiotemporal variation in the field were satisfactorily captured by the InSAR techniques: differential InSAR (DInSAR), persistent scatterer interferometry (PSI), and small baseline subset (SBAS). The SBAS estimations were found to be the closest to field measurements, primarily due to the analysis of zones without well-defined geometries. This study’s contribution to existing knowledge defines the spatiotemporal influence of sustainable backfill in areas with reactive soil through field data and satellite imaging. The relationship between InSAR techniques and actual field behavior of sustainable backfill can be a baseline for the growing construction that may be challenging to perform field monitoring due to resource constraints.

The Estimation of Domestic Greenhouse Gas Potential from Sewer Pipeline and the Review on Reducing Technologies

The recent accelerating global warming and its consequent disasters call for the immediate greenhouse gas (GHG) reduction. In this regard, the time has arrived to consider applying global warming potential (GWP) value of 20 years’ time-scale rather than 100 years’ one, and the importance of reducing methane emissions has rapidly increased due to its short life span. Sewer pipeline is the source of emitting methane through biological anaerobic conversion. However, it is not recognized as the official source from IPCC due to lack of data and investigation. In Korea, to our knowledge, there has been no study on GHG emissions from sewer pipeline. In the present work, at first, the amount of methane potential from domestic sewer pipeline was calculated considering various parameters. Depending on the organic concentration of sewage and its degradation rate, the potential amount ranged 3.2-13.4 m ton CO<sub>2</sub> eq./yr, when GWP100 value was considered. By using GWP20, this amount could reach up to 40.2 m ton CO<sub>2</sub> eq./yr, which exceeds the current total domestic methane emissions (27.8 m ton CO<sub>2</sub> eq./yr in 2019). The calculation results clearly tells the importance of developing technologies for reducing GHG from sewer pipeline. Later on, the former technologies were largely divided into chemical and physical methods, and reviewed by each. In addition, the limitation of current technologies were mentioned with addressing future works. In Korea, this is the first study regarding on GHG from sewer pipeline, which will be used for estimating the exact amount and establishing carbon neutrality.

Forensic Analytical Approach for Hydrocarbon Fingerprinting in Soil Vapor Samplings: Example of a Residential Neighborhood in Brazil

Conventional analytical methods determine the concentration of compounds used to estimate the presence of environmental contamination capable of posing risk to human and ecological receptors (Murphy and Morrison, 2007). While these target analytes help regulators and environmental professional identify and manage potentially harmful conditions, these compounds can be generated by many natural and anthropogenic (man-made) sources and conventional methods alone fail to compositionally differentiate subsurface sources of contamination in complex environments (Morrison, 2000; Wang and Fingas, 2003; Gouvêa et al, 2019; Douglas et al, 2007). This case study demonstrates the advantage of using both conventional and forensic testing methods to accurately identify the source(s) of volatile hydrocarbon contamination in soil vapor samples for the development of an accurate conceptual site model in a neighbourhood near industrial facilities in Rio de Janeiro State, Brazil. The goal of this study was to differentiate potential impacts from a local steelmaking plant from fugitive hydrocarbons associated with more generic human activity in urban settings. This case study demonstrated the advantage of constructing chemical fingerprints from conventional volatile organic compound (VOC) method TO 15 and a forensic volatile hydrocarbon method using an enhanced version of method TO 15 (PIANO). The chemical fingerprints of vapor samples collected from subsurface soil gas and sewers were analysed and compared to laboratory reference samples. These data determined that the VOCs detected in neighbourhood soil vapor samples were associated with fugitive petroleum products migrating in the sewer pipelines and not with the steelmaking wastes emplaced near the residential area. This article discusses the forensic data and chemical signatures that support these findings, and the use of environmental forensic techniques to evaluate environmental data associated with complex scenarios, involving multiple contamination sources.