Underground Pipelines Research Articles

Mechanical Analyses of Underground Pipelines Subjected to Ground Subsidence Considering Soil-Arching Effect

Mechanical Analyses of Underground Pipelines Subjected to Ground Subsidence Considering Soil-Arching Effect

A research on data integration and application technology of urban comprehensive pipe gallery based on three‐dimensional geographic information system platform

AbstractThree‐dimensional Geographic Information System (3D GIS) technology is often used in geology, mining, hydrology and other fields. It is also used in intelligent management of urban comprehensive pipe gallery projects, because the real environment with the sky, ground, and underground can be intuitively simulated in the virtual reality environment by 3D GIS platform. Through the research on data integration and application technology of urban comprehensive pipe gallery, the 3D model of comprehensive pipe gallery project data can be created based on Building Information Modelling and GIS technologies, and a comprehensive underground pipelines network management information system has been developed to realise the integration of various data, including 3D data of comprehensive pipe gallery model and underground pipe network model, 2D or 3D GIS data of the ground and underground etc. On this basis, the functions of intelligent planning and approval management, the safety analysis and disaster prevention planning and decision‐making of urban comprehensive pipe gallery projects can be further realised. The research results have been applied to the planning and construction process management of the comprehensive pipe gallery project in the sub‐centre of Beijing.

Using digital technologies to plan and manage the pipelines network in city

AbstractPromoting the intelligent level of urban pipelines network planning and management is an important way to improve the rational development and utilisation of urban underground space and ensure the safe operation of urban infrastructure systems. The research on using the rapid and visual expression technology of 3D pipelines network model data, which has the functions of techno‐economic indexes calculation and automatic comparison of underground pipeline construction projects, effectively avoids possible errors in manual calculation and comparison and improves the efficiency and accuracy of the digital declaration process for project planning. In the 3D Geographic Information Systems (GIS) platform environment, this research help to develop many analysis and approval functions based on the data of the underground pipelines network model and the integrated pipes gallery Building Information Modelling, such as evaluating the compliance, feasibility and scientific of the planning scheme of the underground pipeline construction project, comparing the completion data with the data approved by the administrative departments of the project, evaluating the consistency of them and judging the compliance of the project completion data. The application results of some actual projects cases show that this research can improve the efficiency of planning declaration and approval analysis.

Methods of increasing the corrosion resistance of underground water supply pipelines

The main causes of premature destruction of the pipeline network of water supply and drainage within the territory of Ukraine are the following: factory (metallurgical) defects of pipes, assembly defects (as a rule, they occur during installation work directly on the object), corrosion damage and destruction of the pipe body and welded connection, natural and deformational aging of metal, external loads and internal excess stresses, lack of high-quality anti-corrosion protection, long (exceeding normative) service life of pipelines that are in direct contact with a corrosive-aggressive environment, etc. Analysis of the general condition of centralized water supply and centralized water drainage systems for 2021. Carried out for 19 regions and the city of Kyiv. Based on the analysis, the following conclusions were drawn: centralized water supply covers 98.7% of cities (absent in 4 cities), 90.3% of rural settlements (absent in 46 rural settlements), and 23.5% of rural settlements, 67.4% of rural settlements and 1.5% of rural settlements. To ensure the development and reconstruction of systems of centralized drinking water supply and centralized water drainage in populated areas of Ukraine and to provide the population with quality drinking water in sufficient quantity, the Ministry of Regions has developed the State-wide target program "Drinking water of Ukraine", which provides for the implementation of more than 1,747 infrastructure projects in the regions during 2022-2026 years In addition, the following measures are recommended for the development of water supply and drainage enterprises: the creation of effective response systems in case of emergency situations, in particular, a logistics chain for the supply of the necessary welding and assembly resources (materials, equipment, labor, etc.); attraction of investments for the modernization of water supply and treatment facilities of the central drainage system; carrying out scientific and research works for the purpose of modernization of engineering facilities for wastewater treatment; modernization of pipeline structures and treatment facilities with the involvement of modern welding and assembly technologies and materials. In addition, in order to increase the reliability of water supply and sewage networks, it is now necessary to carry out certification and inspection of pipelines. Knowing the degree of wear and tear of the pipe wall and the service life, you can calculate the nominal pressure of the pipeline and make a decision about its replacement or rehabilitation. Using the failure criterion for "critical" points, it is possible to predict the complications of network operation, model redundancy for them, and optimize their operation. It is proposed to equip specialized mobile laboratories on the basis of water utilities, which allow network diagnostics and emergency cleaning of pipelines and water disinfection. Specialists of the Ministry of Development of Communities and Territories of Ukraine carried out systematic work on assessing the technical condition of water supply networks. The materials officially provided by the Ministry of Health of Ukraine and the Ministry of Environmental Protection and Natural Resources of Ukraine served as the basis for such works. The curator of this work was the State Enterprise "Scientific-Research and Design-Technological Institute of Urban Economy" (Kyiv).

A Modular Method for GPR Hyperbolic Feature Detection and Quantitative Parameter Inversion of Underground Pipelines

Ground penetrating radar (GPR) is widely used to inspect underground pipelines because it is non-destructive. When the scan line of GPR is perpendicular to the pipe, it will exhibit hyperbolic features in GPR B-scan images, which have no intuitive relationship with the geometric and physical parameters of the pipeline, making the interpretation of GPR images difficult. This paper proposes a modular detection and quantitative inversion method for the hyperbolic features in GPR B-scan images, which is divided into two steps. In the first step, the YOLOv7 object detection network is used to automatically detect the hyperbolic features in GPR images. In the second step, a two-stage curve fitting method is proposed based on the characteristics of the detection model. It uses a few key point annotations of the hyperbolic pattern and some parameters of the GPR system to quantitatively invert the depth and radius of pipes. Using the same hardware and data set, YOLOv7 achieves an 11.1% improvement in detection accuracy and an 18.2% improvement in speed compared to YOLOv5. The relative errors of the proposed method for the depth and radius of the synthetic data in homogeneous media are 0.6% and 4.4%, respectively, and 4.8% and 15% in non-homogeneous media. The relative error of the depth inversion of the measured data TU1208 is less than 10%. The results show that the method can effectively invert the depth and radius of underground pipelines and reduce the difficulty of GPR data interpretation.

Procedural Modeling Method of Pipe Network Based on Spline Grammar

The shape grammar is usually used to describe the three-dimensional model with certain regularity, but in the process of constructing the three-dimensional model of industrial pipeline network or urban underground pipeline network, due to the complex wiring characteristics of the pipeline, the pipeline with different changing trajectories is often unable to be described by fixed shape. Therefore, the construction process for 3D pipe networks often requires art developers to spend a lot of time and effort to develop models. Arbitrarily shaped pipes can be effectively described in procedural modeling construction processes by incorporating splines that are inherently variable in nature and combining them as terminator shapes into shape grammar. Based on this theory, this paper proposes that the procedural modeling method based on spline grammar can efficiently complete the procedural construction of pipeline networks, and this method can quickly complete the splicing of pipeline networks. Experimental results show that our method is suitable for pipe network construction projects in different fields.

Employing Robotics and Deep Learning in Underground Leak Detection

Leaks in water distribution networks (WDNs) produce significant economic losses. These leaks from underground pipelines affect the surrounding environment in different ways that can be detected using various technologies. This article introduces an unmanned ground vehicle (UGV) equipped with an infrared temperature sensor to remotely detect thermal anomalies on the surface caused by underground leaking pipelines. An MLX90614 low-cost thermopile infrared sensor was proposed to trace the surface temperature above leaking pipelines and record the corresponding position of each reading. Two positioning techniques were tested independently: a satellite navigation system and odometry using a magnetic encoder. Raw data from the temperature sensor along with the corresponding locations were recorded on a MicroSD card using an Arduino Uno Board through analog-to-digital converters (ADCs) and serial peripheral interface (SPI) bus. Experimental work showed the ability of the robot in detecting thermal anomalies caused by leaks accurately with the required trace resolution. Finally, a 2-D convolutional neural network (CNN) solution is introduced for distinguishing true detections from false alarms.

PIPE TRAVERSING ROBOT

As technology advances, the demand for automated systems has increased exponentially. The development of robotic systems has made it possible to explore and operate in remote and hazardous environments. One such application is the inspection of underground pipelines, which requires a mobile robot that can traverse inside the pipeline, inspecting and repairing any damage that may occur. For the rover, the diameter of the pipe is required minimum diameter of the pipe is 274mm and the maximum diameter of the pipe is 324 mm. The rover is equipped with advanced sensor cameras, and communication systems that allow it to transmit real-time data to operators. The system operates on a track-driven mechanism that provides stability and agility to traverse through the pipeline. The rover's design also includes a modular and lightweight structure that allows for easy deployment and transportation. The system can operate autonomously or can be controlled remotely by operators, providing greater flexibility in the inspection process.

Research on Underground Pipeline Detection Method Based on Background Matrix Subtraction and Three-Dimensional Velocity Spectrum

Ground-penetrating radar (GPR) can detect urban underground pipelines and image their spatial distribution. However, due to the interference of direct wave and ground reflected wave in radar profile, the detection accuracy of underground pipeline depth is low. In order to improve the reliability and accuracy of the interpretation of ground penetrating radar data, it is necessary to suppress the noise, and then to detect underground pipelines. Firstly, on the basis of data collection, a background matrix subtraction (BMS) method is proposed to suppress noise signals, and the Noise reduction effect is compared and analyzed by two groups of simulation data and two groups of measured data examples with the method of reducing average channel and singular decomposition. Then, a three-dimensional velocity spectrum (3DVS) method is proposed to estimate the buried depth of underground pipeline in radar profile, and the propagation velocity of electromagnetic wave in underground media is calculated by automatically scanning the hyperbolic reflection signal. The estimated velocity is used to carry out back-propagation migration (BPM) processing on the ground penetrating radar profile, and the underground pipeline is accurately detected. Finally, the underground pipeline detection method based on BMS and 3DVS is applied to a residential area in Nanjing, Jiangsu, China. The detection results show that the effect of BMS is obviously better than that of mean-reducing method and singular decomposition method, which can suppress the noise well on the basis of ensuring that the effective signal is not lost. It is helpful to identify the characteristics of target signal in ground penetrating radar profile, and improve the accuracy and reliability of data interpretation. The error between the velocity value estimated by the 3DVS method and the real value is less than 3.8%, and the buried depth error of pipeline target in the obtained data is 1.4%, which indicates that the algorithm has practical application value and can realize the accurate positioning of underground pipelines.

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.

Rapid on-site detection of underground petroleum pipeline leaks and risk assessment using portable gas chromatography-mass spectrometry and solid phase microextraction

Locating underground pipeline leaks can be challenging due to their hidden nature and variable terrain conditions. To sample soil gas, solid-phase microextraction (SPME) was employed, and a portable gas chromatography/mass spectrometry (GC/MS) was used to detect the presence and concentrations of petroleum hydrocarbon volatile organic compounds (pH-VOCs), including benzene, toluene, ethylbenzene, and xylene (BTEX). We optimized the extraction method through benchtop studies using SPME. The appropriate fibre materials and exposure time were selected for each BTEX compound. Before applying SPME, we preconditioned the soil vapour samples by keeping the temperature at around 4 °C and using ethanol as a desorbing agent and moisture filters to minimize the impact of moisture. To conduct this optimisation, airbags were applied to condition the soil vapour samples and SPME sampling. By conditioning the samples using this method, we were able to improve analytical efficiency and accuracy while minimizing environmental impacts, resulting in more reliable research data in the field. The study employed portable GC/MS data to assess the concentration distribution of BTEX in soil vapour samples obtained from 1.5 m below the ground surface at 10 subsurface vapour monitoring locations at the leak site. After optimization, the detection limits of BTEX were almost 100 µg/m3, and the measurement repeatabilities were approximately 5% and 15% for BTEX standards in the laboratory and soil vapour samples in the field, respectively. The soil vapour samples showed a hotspot region with high BTEX concentrations, reaching 30 mg/m3, indicating a diesel return pipeline leak caused by a gasket failure in a flange. The prompt detection of the leak source was critical in minimizing environmental impact and worker safety hazards.

Theoretical foundations of the construction of the operation of heat flow devices

Numerous studies show that non-destructive testing methods satisfy most of the requirements of technical diagnostics of heating networks and technological facilities. Methods of non-destructive testing are based on the observation and automated registration of the temperature state of processes. The developed device is designed to analyze the state of thermal insulation of underground pipelines. The development and research of devices for measuring heat flow requires mandatory consideration of the temperature field of the sensing element, i.e. solutions of the differential equation of thermal conductivity for a body of a certain shape under given boundary conditions. In general, the sensing elements are multi-layered: black coating, calorimetric load, heat-sensitive elements, alternating lacquer and adhesive layers, i.e., the sensing elements are heterogeneous, both in the direction perpendicular to the irradiated surface and in parallel. The heterogeneity in the first case is due to the multi-layering of the sensing element. The article describes solutions to the thermal conductivity equation describing the temperature field of a sensitive element in the form of a hemisphere and a spherical zone, due to the nonequivalence of heat losses during irradiation and calibration by electric current. Taking into account this systematic error makes it possible to increase the accuracy of measuring the energy parameters of radiation. These solutions of the equations formed the basis of the design of the device for measuring heat flow.

Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis

We propose deep learning-based models for the risk detection of underground pipelines. To build effective diagnosis models, we construct two types of deep neural network frameworks. First, we propose an image segmentation model with two parallel encoder structures for analyzing RGB and thermal images to detect risk-suspected regions due to pipeline rupture. In addition, for pipeline risk-stage recognition, we compare the performance of various image classification models and select the optimal model. Then, by integrating the image segmentation and classification models, we propose a fused model that performs risk-suspected region detection and risk-level detection using a single inference. The proposed image segmentation model achieves IoU and Dice coefficient values of 0.8373 and 0.9024, respectively, indicating higher performance in suspected-region detection compared with other competing models and demonstrating robust performance against false recognition. In risk-level detection, we confirm that the DenseNet-based model has the highest performance among the classification models, scoring 94.10% and 95.65% in terms of heat and leak accuracy, respectively. Finally, we validate that the fusion model, which recorded 0.8160, 0.8711, 93.90%, and 95.05% in terms of IoU, Dice coefficient, heat accuracy, and leak accuracy, respectively, can process 30 frames of video per second in real-time at the cost of a slight drop in performance.

Numerical Simulation of Assembly Process and Sealing Reliability of T-Rubber Gasket Pipe Joints

Underground pipelines are vital parts to urban water supply, gas supply, and other lifeline systems, affecting the sustainable development of cities to a great extent. The pipeline joint, which is a weak link, may be seriously damaged during natural disasters such as earthquakes. The failure of pipe joints can cause leakage accidents, resulting in system failure and interruption, and even some secondary disasters. Herein, based on uniaxial and plane tensile test results of a T-rubber gasket material, the assembly process and sealing performance of a T-rubber gasket joint of a ductile iron pipe are numerically simulated using the Ogden third-order strain energy density function to fit the material constant. The simulation accounts for severe nonlinearities, including large deformations, hyperelasticity, and complex contacts. The effects of the assembly friction coefficient, assembly depth, and radial clearance deviation of the socket and spigot on the seal contact pressure are analyzed. The results suggest that the entire history of the deformation and stress variations during assembly can be clearly visualized and accurately calculated. For the different friction coefficients, the assembly depth corresponding to the sliding friction condition of the spigot pipe was 74 mm, while the minimum pushing force required to assemble the T-rubber gasket joint of a DN300 ductile iron pipe was 6.8 kN at the ideal situation with a friction coefficient of 0. The effective contact pressure of the rubber gasket seepage surface under various operating conditions is much higher than the normal pressure of municipal pipelines, thus indicating that the rubber gasket joint exhibits the ideal sealing performance. Furthermore, a certain deviation, which is about 20 mm, is allowed for the assembly depth of the rubber gasket joint such that the axial displacement of the pipe joint can be adapted under an earthquake or ground displacement.

Dynamic Response Analysis of Buried HDPE Pipes under Vibration Compaction Considering the Influence of Buried Depth and Filling Modulus

During pavement construction, the gravity load and vibration excitation from vibratory rollers can seriously affect the safety of underground pipelines. However, research on the dynamic response of buried pipelines under the action of large vibration rollers is rarely reported. Therefore, the dynamic response of a high-density polyethylene (HDPE) double-wall corrugated pipe under a vibratory roller was studied via field testing and a three-dimensional nonlinear finite-element model was established. This model was used to analyze the influence of filling property and vibration frequency on the dynamic response of an HDPE pipe under a vibration load from a vibratory roller. The results reveal that with the increase of compaction times, the backfill soil keeps changing between over-compaction (loose) and compaction states, and the pipe top pressure also keeps changing. Moreover, at a shallow burial, the pipe top pressure is more obvious when the compaction degree changes. The elastic modulus of filling soil within a certain range can effectively reduce the stress deformation of the pipeline under vibration compaction. However, when the elastic modulus of filling soil exceeds 10 times the initial elastic modulus, the deformation of the pipeline becomes greater than the initial value.

Detecting pipeline leakage using active distributed temperature Sensing: Theoretical modeling and experimental verification

This paper presents a feasibility study on leak detection of buried pipelines using the active distributed temperature sensing (ADTS) method. The proposed solution involves the use of actively heated fiber optic (AHFO) cables arranged parallel to the pipelines, allowing for changes in the thermal properties of the surrounding soil to be captured and correlated with leakage accidents. This problem was modeled as an infinite cylindrical or linear heat source buried in porous media with seepage flowing inside. AHFO sensors were designed to verify the proposed method through scale-reduced model tests. The fiber optic temperature sensing technology based on optical frequency domain reflectometry (OFDR) was used to continuously monitor temperature profiles. The test results demonstrate that the ADTS solution has greatly improved the accuracy of passive temperature sensing methods. The feasibility of the solution was evaluated regarding different levels of leakage estimation, namely, identification, location, and quantification. This paper presents the first attempt to monitor the infiltration process associated with pipeline leaks using the AHFO-OFDR technique, which is a reliable method for structural health monitoring of underground pipelines.

Long-Range Wireless Power Drive Using Magnetic Extender

This article proposes and implements a long-range wireless power drive (WPD) system using a magnetic extender, which can be promisingly applied in underground pipeline transportations and in-pipe robots. Generally, the underground pipeline transportation networks usually rely on the power grid and control system to regulate the flow rate. Nonetheless, such wired pipeline networks mainly suffer from increasing the construction cost, maintenance difficulty, and system complexity. By using pulse frequency modulation, the development of the proposed long-range WPD enables a wireless pipeline network for flow rate regulation, which has the advantages of robust structure and reduced maintenance requirements. With the addition of a designed magnetic extender, the circuit network can offer an excellent fault tolerance capability for underground sensor-free WPD while maintaining a high transmission efficiency. Significantly, the use of a magnetic extender will cause no inconvenience thanks to its underground deployment. Accordingly, the system efficiency of a full-scale prototype can reach around 89.1% for the meter-range WPD. Theoretical analysis, computational simulation, and prototype experimentation are given to verify the feasibility of the proposed long-range WPD using a magnetic extender.

CFD Analysis of Pressure Variation inside Iron-Ore Slurry Pipelines

Abstract: Slurries, basically regarded as two – phase (solid – liquid) mixtures of ore and the carrier medium, generally water, are transported through underground pipelines. The rheological properties of the slurry also play an important role in determining the salient parameters of hydraulic transport such as head loss and pressure drop in commercial slurry pipelines. CFD has provided an upper hand in mathematical modelling and flow simulation, as it provides accurate and dependable flow simulation results. This article primarily focuses on determining the pressure drop across the ends of the pipeline, as slurry flows through it, and to establish a graphical depiction of the various simulations made using different set of conditions. The pressure drop for R500 radius of curvature of bends was found to be lying between 4.27% to 6.53%, as the bend angles increased from 3° to 18°, while the same was found to be lying between 3.49% to 6.2% for R2500 radius of curvature of bends. The pressure drop for R500 radius of curvature of bends was found to be lying between 4.91% to 8.3%, as the inlet velocity of slurry increased from 0.5 m/s to 3m/s, while the same was found to be lying between 5.05% to 8.63% for R2500 radius of curvature of bends.

Environmental risk of oil pipeline accidents

Oil spills from pipeline accidents, caused by either material degradation or improper operation, can result in long-lasting environmental damage to soil and water. Assessing the potential environmental risks of these accidents is crucial for effective pipeline integrity management. This study calculates the accident rate using Pipeline and Hazardous Materials Safety Administration (PHMSA) data and estimates the environmental risk of pipeline accidents by factoring in the cost of environmental remediation. Results show that crude oil pipelines in Michigan pose the highest environmental risk, while Texas has the highest environmental risk for product oil pipelines. On average, crude oil pipelines have a higher environmental risk (56,533.6 US dollars·times·mile−1·year−1) compared to product oil pipelines (13,395.6 US dollars·times·mile−1·year−1). Factors affecting pipeline integrity management are also analyzed, including diameter, diameter-thickness ratio, and design pressure. The study finds that larger pipelines with higher pressures receive more attention during maintenance and thus pose a lower environmental risk. Furthermore, underground pipelines pose a much higher environmental risk than pipelines in other environments, and pipelines are more vulnerable in the early and mid-stages of operation. The leading causes of environmental risk in pipeline accidents are material failure, corrosion, and equipment failure. By comparing environmental risks, managers can better understand the strengths and weaknesses of their integrity management efforts.

Prediction of the probabilistic water leak location in underground pipelines using Monte Carlo simulation

Abstract The water loss rates in Brazil are still very concerning and cause huge financial, social, and environmental damages. Although great advances have been made in acoustic leak detection methods, some of them are intrusive or at least demand convenient access points to the pipe's surface. Furthermore, some methods are expensive and require a highly experienced and qualified operator. Thus, the goal of this paper is to establish a benchmark using a simplistic analytical model for the ground surface analysis for leaks location, simplifying the search process carried out in water networks, and requiring minimal system interference. For leak location prediction, the proposed method considered two seismic wave paths – the compressional body waves and the Rayleigh superficial waves. The traveltime curves of the compressional waves are calculated as a function of the surface wave's experimental information. Monte Carlo Simulations were carried out to test each model's precision considering uncertainties related to the input parameters of the problem. At last, through a parametric study, the two seismic events were combined to obtain better accuracy in predicting the leakage epicenter location. In the results, the maximum absolute error found was 15 cm, and the minimum improvement obtained with the parametric study was 27.4%.