Leakage Point Research Articles

SDCSN: A Hierarchical parallel Localisation Method for Pipeline Leakage Based on Vibration Signals

Abstract In applying deep learning methods to detecting and localising pipeline leaks, improving the fitness of deep learning methods to leak signals is an important task. We propose a novel detection model called Stacked dilated convolutional shrinkage network (SDCSN). This model incorporates a Stacked Dilated Convolution (SDC) module specifically designed for vibration signals, enabling the extraction of rich multi-scale local features. Moreover, implementing the Residual Shrinkage Building Unit (RSBU) module for noise reduction in the network architecture. Building upon this foundation, we introduce a new concept centred around hierarchical leakage discrimination and parallel prediction positioning. This approach enables accurate assessment of leakage levels and precise identification of multiple leakage points. Finally, the performance of the proposed method is verified in real experiments and the optimal settings for the dilated rate are determined. The results demonstrate a maximum classification accuracy rate reaching 98.94%.

Automatic detection of water pipeline leakage via unsupervised learning applied on ground penetrating radar data

ABSTRACT Aging urban water supply pipelines are facing significant leakage issues. Ground penetrating radar (GPR) is an effective non-destructive method for leakage localization, but its image analysis mainly relies on interpreter experience, leading to uncertainty and inefficiency. To address this issue, an unsupervised learning method based on GPR data attributes is proposed for automatic leakage detection. First, velocity and energy attributes are extracted from B-Scan profiles to differentiate wet areas caused by leakage from normal areas. Then, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is applied to automatically classify these two types of areas. The proposed method was evaluated on an experimental platform with constant water pressure and pre-drilled leakage points. Experiments show that the velocity and energy attributes decrease by 25.2 and 26.6% after leakage. Using velocity attributes yields better results, and suggestions for DBSCAN hyperparameters are provided. Feature importance analysis indicates that two-way-travel-time with a score of 46.1 and energy attribution index with a score of 32.7 significantly influence leakage identification. This method can also estimate affected leakage areas with an error not exceeding the spacing of three measurement lines, and is available across various underground conditions without needing well-labeled training data.

Pipeline leak detection and localization based on the flow balance and negative pressure wave methods

The traditional water supply pipeline leak detection and location method has problems of false alarms, low positioning accuracy, and low stability. This paper focuses on the pipeline leak detection and location experimental platform to investigate water supply pipeline leaks, followed by simulation and experimental validation. This study integrates the mass flow balance method and pressure change inflection points to determine pipeline leaks and introduces an improved negative pressure wave method for locating leakage points. The results demonstrate that the system accurately detects pipeline leaks. The average positioning error of the improved negative pressure wave method for the leakage point is 4.76%, which has good stability and high positioning accuracy.

In-site experimental study on the effects of infrared thermal imaging technology on levee leakage detection

Excessive seepage significantly affects the stability of levees during flood seasons. Recently, infrared thermal imaging technology has been proposed to detect the seepage leakage of levees for its simplicity, high efficiency and non-contact. However, previous studies mainly focused on theoretical and laboratory tests to verify its effect, which involve many assumptions and simplification. This paper aims to study the practical application effects of infrared thermal imaging technology on levee leakage detection. A typical embankment in Xiangyin County, Hunan Province is selected as the studied area, where a specific point containing water is considered as the potential leakage point. Then, DJI M300RTK UAV equipped with Zenith H20T gimbal is adopted to collect the visible and infrared images of the embankment’s leaking point. The visible and infrared image data collected at different times and at different measurement heights are analyzed to investigate the detection accuracy of infrared thermal imaging technology. Results show that the infrared thermal imaging technology can accurately identify the potential seepage leakage point of levees. Compared with the visible light recognition method, the infrared thermal imaging technology can identify the location and scope of leakage hazards more clearly and accurately at night. During the daytime, infrared detection is less effective than visible light method. Moreover, the measurement altitude between the drone and the target leakage points significantly affects the detection accuracy of infrared thermal imaging technology.

Research on the Loss Rule of the Leakage Problem in Residential Construction Based on Water Spray and Storage Tests

Leakage issues have received increasing attention as the most common and significant source of complaints in residential construction quality problems. In this study, based on the classification of residential construction leakage problems, 1947 water spray tests and 2333 water storage tests were conducted on 18 construction projects. An empirical analysis of 432 leakage cases was conducted to determine the loss law for a single leakage point as well as the loss laws for different grades of leakage problems. Through empirical analysis, it can be concluded that more than 90% of the leakage problems are third-level. To better understand the quantitative law of the residential construction leakage problem, a total loss law model was developed. Finally, the law of leakage loss in residential construction is summarized, and measures to reduce leakage loss in residential construction are proposed. This research can provide a theoretical basis and model tools for inherent defect insurance and help insurance companies control insurance risks and drive the promotion of inherent defect insurance.

Detection of underground natural gas pipeline micro-leakage based on UAV hyperspectral remote sensing and GIS

ABSTRACT Detection of underground natural gas pipeline micro-leakage based on unmanned aerial vehicle (UAV) hyperspectral remote sensing and GIS. Hyperspectral images can indirectly detect underground natural gas pipeline micro-leakage through spectral and spatial variation characteristics of surface vegetation. However, most of existing studies were based on ground-mounted platforms, which could only perform small-range single-point detection and might occur misidentifications. UAV hyperspectral remote sensing can allow wide-range detection of surface vegetation. Moreover, underground pipeline distribution GIS data can provide prior knowledge about leakage points to exclude misidentifications. Therefore, a natural gas pipeline micro-leakage experiment was set up. UAV hyperspectral images of grasslands and pipeline distribution vector data were obtained, which led to a proposed new wide-range multi-points detection methodology of underground natural gas micro-leakage. Firstly, the vegetation identification index (WVI) R 470 + R 674 / R 555 + R 750 was designed based on WOA–VMD (whale optimization algorithm – variational modal decomposition) to segment and extract the vegetation stress zones. Then, UAV- and GIS-based natural gas micro-leakage vegetation stress identification model was constructed to obtain the leak points of natural gas micro-leakage. Finally, the recognition ability was evaluated by comparing with three stress vegetation identification indices proposed in previous studies. The result showed that there was no missing or false detection in identification results; the identification and positioning effect was better than other indices, which could meet the practical application requirements.

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector

Methane emissions from leak sources can have a negative climate impact, in addition to contributing to the risk of explosions in urban environments. These risks can be minimized by developing systems that provide for an accurate and timely detection and localization of a gas leakage point. This research used a swarm of robots to detect and locate a leakage point. The localization algorithm derives from further optimization of the gradient climbing algorithm using fireflies acting as opportunistic agents. Firefly agents are characterized by their bioluminescent communication which guides them to dynamically adjust their positions and intensities based on the quality of the gradient information available to them. The proposed research focuses on enhancing gas leak detection through the development of a hybrid gradient climbing algorithm. This algorithm integrates gradient climbing techniques with swarm intelligence, utilizing the strengths of both approaches. This simulation resulted in the hybrid algorithm leading to a reduced convergence time and path lengths when compared to the swarm without opportunistic agents. The suggested approach can be important especially in gas distribution systems or in areas where human intervention is considered to be unsafe.

Fully coupled hydromechanical study of tunnel excavation considering leachate leakage from a high water-level landfill

The Jigongshan Mountain Tunnel was constructed beneath a high water-level landfill in Shenzhen, China. Previous studies could not fully capture the hydraulic interactions between the tunnel excavation and the landfill leachate seepage. In this study, a fully coupled hydromechanical numerical model, considering the unsaturated properties and the changing permeability of the waste and geological materials, was established to simulate the landfill leakage and the tunnel excavation simultaneously. Parameter analysis was conducted to explore the effects of the configuration and dimensions of the leakage pathway and leakage points on the seepage field. Results indicate the landfill leakage has a minimal effect on tunnel displacement. The leakage width and the leakage path in the clay significantly affect the pore pressure of the tunnel. In scenarios of severe leakage, the pore pressure of the tunnel can reach as high as 0.615 MPa. Three defensive lines and the fully cover waterproofing layer were suggested for the tunnel waterproofing system under the Xiaping landfill. This research contributes valuable insights for the design and construction of subsurface structures in proximity to landfills.

Exploring the protective effect and mechanism of icariside II on the bladder in a rat model of radiation cystitis based on transcriptome sequencing

Purpose Radiation cystitis (RC) is a complex and common complication after radiotherapy for pelvic cancer. Icariside II (ICAII) is a flavonoid compound extracted from Epimedium, a traditional Chinese medicine, with various pharmacological activities. The aim of the present study was to investigate the cysto-protective effects of ICAII in RC rats and its possible mechanisms. Materials and Methods A rat model of induced radiation cystitis using pelvic X-ray irradiation was used, and bladder function was assessed by bladder volume and bladder leakage point pressure (LPP) after ICAII treatment. HE and Masson stains were used to assess the histopathological changes in the bladder. IL-6, TNF-α, IL-10, IL-4 and IL-1β were measured by ELISA to assess the level of inflammation. The gene-level changes in ICAII-treated RC were observed by transcriptome sequencing, and then the potential targets of action and biological mechanisms were explored by PPI, GO and KEGG enrichment analysis of the differentially expressed genes. Finally, the predicted targets of action were experimentally validated using immunohistochemistry, RT–qPCR, molecular docking and CETSA. Results ICAII significantly increased bladder volume and the LPP, ameliorated pathological damage to bladder tissues, decreased the levels of IL-6, TNF-α, and IL-1β, and increased the levels of IL-10 and IL-4 in radiation-injured rats. A total of 90 differentially expressed genes were obtained by transcriptome sequencing, and PPI analysis identified H3F3C, ISG15, SPP1, and LCN2 as possible potential targets of action. GO and KEGG analyses revealed that these differentially expressed genes were mainly enriched in the pathways metabolism of xenobiotics by cytochrome P450, arachidonic acid metabolism, Staphylococcus aureus infection and chemical carcinogenesis - reactive oxygen species. Experimental validation showed that ICAII could significantly increase the expression of H3F3C and ISG15 and inhibit the expression of SPP1 and LCN2. ICAII binds well to H3F3C, ISG15, SPP1 and LCN2, with the best binding ability to H3F3C. Furthermore, ICAII inhibited the protein degradation of H3F3C in bladder epithelial cells. Conclusions ICAII may alleviate the bladder inflammatory response and inhibit the fibrosis process of bladder tissues through the regulation of H3F3C, ISG15, SPP1, and LCN2 targets and has a protective effect on the bladder of radioinjured rats. In particular, H3F3C may be one of the most promising therapeutic targets.

Model for the Failure Prediction Mechanism of In-Service Pipelines Based on IoT Technology

With the rapid increase in pipeline mileage in China, the accurate prediction of corrosion issues in in-service pipelines has become crucial for ensuring safe pipeline operation. Traditional pipeline leakage monitoring methods are significantly limited by human factors and equipment precision, making it challenging to predict and identify leakage points accurately. Therefore, aligned with the trend of intelligent pipeline development, this study aims to construct a failure pressure prediction mechanism model for corroded pipelines based on IoT technology. This model leverages intelligent sensing and prediction to assess the safety status of corroded pipeline sections. Ultrasonic phased array technology detects specific corrosion points and detailed defect parameters within pipeline sections. The parameters are then utilized in the Simdroid domestic finite element analysis model to simulate the ultimate burst pressure of the pipeline. A single-variable approach is employed to analyze the sensitivity of different parameters to the pipeline’s ultimate burst pressure, with the minimum burst pressure point of multi-point corroded sections selected as the overall segment failure pressure. Finite element simulation data are integrated into a neural network database to predict the pipeline failure pressure. The real-time operational data of the pipeline are monitored using negative-pressure wave sensing. The operational pressure of the corroded points is compared with the algorithm-predicted failure pressure; if the values approach a critical threshold, an alarm is triggered. Moreover, the remote control terminals evaluate the pipeline’s self-rescue time, providing a buffer for pipeline leakage self-rescue. The failure prediction mechanism model for in-service pipelines was applied to the Fujian–Guangdong branch of the West–East Gas Pipeline III to verify its accuracy and feasibility. The research results offer technical support for the maintenance and emergency repair of pipeline leakage scenarios, leveraging intelligent pipeline technology to reduce costs and increase the efficiency of pipeline operations, thereby supporting the sustainable development of China’s oil and gas pipelines with theoretical and technical backing.

A detailed simulation study on radionuclide dispersion under spent fuel road transportation conditions: Effects of vessel type and coniferous vegetation growth

Vegetation barriers are an important environmental characteristic of spent fuel road transportation accidents. Spent fuel vessels may be affected by force majeure factors during transportation, which leads to damage to spent fuel assemblies and containers and can cause radionuclides to gradually release from assemblies to vessels to the external environment. In this work, considering the growth periods of coniferous vegetation barriers and vessel type, a radionuclide dispersion model based on computational fluid dynamics (CFD) was established by adding a decay term and a pressure loss term. The simulations showed that, first, compared to the small (Type-II) vessel, the effects of fluid flow around the large vessel (Type-I) have a more significant impact on radionuclide dispersion. The backflow around the Type-I vessel causes leaked radionuclides to disperse towards the vessel, and the larger the vessel is, the more significant the rise of the leaked radionuclide plume tail will be due to the increased negative pressure gradient area. Moreover, the area contaminated exceeding the maximum allowable concentration by radioactivity for the Type-I vessel is reduced gradually with the growth of coniferous vegetation barriers due to the weakening of the backflow effect by growing vegetation. Second, compared to vegetation barriers of 15 years and 23 years, the horizontal distance exceeding the maximum allowable concentration of the leaked 131I dispersion from Type II vessels near vegetation barriers for 12 years is the longest. The older the vegetation barrier is, the shorter the horizontal dispersion range, and the shape of radionuclide dispersion gradually transforms from flat to semicircular with vegetation barrier growth, but this could cause a greater radioactive accumulation effect near the leakage point, and the maximum concentration of leaked 131I reached 0.54 kBq·m−3 for leaked radionuclides from the Type II vessel under vegetation barriers of 23 years. In addition, improvement suggestions based on the proposed method are presented, which will enable the Standards Institutes to apply the research methodologies described herein across various scenarios. Environmental ImplicationCompared to nonradioative pollutants, radioactive pollutants are intercepted by vegetation barriers and then migrate to the soil through leaves, stems, and roots, which can contaminate the surrounding environment. Considering the effects of vessel type and coniferous vegetation growth, a radionuclide dispersion model based on CFD was established. Suggestions for decontaminating radioactive pollution areas have been proposed based on the simulation results of hypothetical scenarios. The scenario applicability improvements based on the proposed model could assist relevant Standards Institutes to making improving measures.

Characteristics analysis of leakage diseases of Beijing underground subway stations based on the field investigation and data statistics

With a continuous increasing of the underground water level of Beijing, many leakage diseases induced by the structural defect have been exposed in the subway tunnels that have been put into operation. The leakage diseases of underground subway stations may severely cause the safety and durability problems of the structure. Moreover, it could affect the passenger experience and seriously threaten the operation safety. Therefore, this paper collected the leakage states of a total of 258 underground subway stations in 16 lines of Beijing by field investigation, which include the number of average leakage points, leakage types and classification, distribution scenes of leakage, the construction methods and operation ages of subway stations and so on. Besides, in order to further investigate the causes of the leakage diseases, three typical cases of Beijing subway stations with severe leakage diseases, which were constructed by different methods, were selected to carry out the statistical analysis of leakage disease. The typical positions of leakage diseases of Beijing subway stations with different construction methods were presented by three-dimensional modelling, and the leakage characteristics of each station were analyzed. Finally, based on the field investigation and data statistics, a data-based Bayesian network leakage prediction model to evaluate the leakage risk of subway stations was constructed. This research can provide an intuitive reference for early warning, monitoring and prevention of leakage diseases of metro subways in operation.

Leakage Sign Is a Reliable Predictor of Hematoma Expansion in Acute Epidural Hematoma

Hematoma expansion (H-Ex) in small-/medium-sized acute epidural hematoma (AEDH) cases upon emergency admission is critical. Predicting H-Ex can lead to early surgical interventions, improving outcomes, and eliminating the need to check for expansion via computed tomography (CT). This study aimed to identify the most reliable predictors of AEDH expansion. We retrospectively collected data from patients with pure AEDH not requiring surgical treatment upon emergency admission from 2012 to 2022. We assessed clinical and laboratory data, time from injury to the first CT, and time to follow-up CT. Factors predictive of H-Ex on the second follow-up CT, including the leakage sign (LS), were analyzed. A total of 23 patients with pure AEDH without surgery at admission were included, and LS was positive in 18. Thirteen patients showed H-Ex. The H-Ex group showed a significantly higher rate of positive LS and a lower mean platelet count than the group without H-Ex. LS's predictive value for AEDH expansion showed 100% sensitivity and 50% specificity. All patients with negative LS and normal platelet counts showed no H-Ex. Analyzing the time from injury to the first CT suggested that LS (+) within 120minutes strongly predicted H-Ex. Reconstructed three-dimensional images of the leakage point on the skull revealed multiple mottled bleeding points on the dural surface. LS can predict H-Ex in patients with pure AEDH for whom emergency surgery is unnecessary at admission. The time from injury and platelet counts must also be considered.

Study on temperature field distribution and leakage point localization of buried carbon dioxide pipeline leakage

Leakage from buried CO2 pipelines is a critical issue in the context of carbon capture and storage (CCS). Therefore, we developed a small-scale experimental system for buried CO2 pipeline leakage. to investigate the effects of leak pressure, leak aperture size, and soil type on the temperature distribution near the leak point. The results of the study showed that the lowest soil temperature appeared directly above the leak. The minimum temperature at the soil axial measurement point increased with increasing leak pressure, but the peak maximum temperature difference decreased. 3 mm and 5 mm leak pore sizes, the peak maximum temperature difference occurred at 10 cm and 20 cm, respectively. At all pressures, the maximum temperature difference peaked at 20 cm. Soil surface temperature variations were mainly influenced by leak pressure. The maximum peak temperature difference at the soil surface measurement points also increased with increasing leak pressure. Smaller leakage apertures slowed down the decrease of soil surface temperature. In addition, based on the experimental results, we propose a new method to locate leaks based on the soil temperature gradient.

Using a combination of δ13CDIC-DOC-difference in dissolved inorganic and organic carbon, δ2H, and δ18O to localize leachate leaks at landfill sites in China

The investigation of leachate leakage at numerous landfill sites is urgently needed. This study presents an exploration of environmental tracing methods using δ2H and δ13C-difference in dissolved carbon (δ13CDIC-DOC) to localize leachate leak points at landfill sites. δ2H, δ13CDIC, δ13CDOC, δ18O, and an array of physicochemical indices (e.g., total dissolved solids, temperature, and oxidation reduction potential) were monitored in both leachate and groundwater from different zones of a landfill site in China during the year of 2021–2023. Moreover, data for these parameters (i.e., the isotopic composition and physicochemical indices) from twelve published landfill cases were also collected, and these groundwater/leachate data points were located within 1 km away from the landfill boundary. Then statistical analyses, such as Pearson correlation analysis and redundancy analysis (RDA), were performed using both the detected and collected parameters at landfill sites. Consequently, the intensity of interaction between leachate and background groundwater was found to significantly control the isotopic fractionation features of hydrogen and carbon, and both the content of major contamination indicators (total dissolved solids, chemical oxygen demand, and ammoniacal nitrogen) and the oxidation reduction potential were the key impact factors. Accordingly, the water type used to indicate leachate leakage points was determined to be leachate that significantly interacted with the background groundwater or precipitation (LBGP). δ2H showed a perfect linear correlation (0.81 ≤ r2 < 1.0) with δ13CDIC-DOC in leachate under highly anaerobic landfill conditions, and the δ2H &δ13CDIC-DOC combinations in the LBGP were significantly different from those in the other water types. For groundwater with total dissolved solids lower than 1400 mg/L at landfill sites, a strong positive linear correlation (r = 0.83) was revealed between δ13CDIC and δ13CDOC. Based on these insights, δ2H versus δ13CDIC-DOC plots and RDA using δ2H and δ13CDIC-DOC as response variables were proposed to localize leak points at both lined landfills and leachate facilities. These findings further understanding of the isotopic fractionation features of hydrogen, carbon, and oxygen and provide novel environmental tracer methods for investigating leachate leak points at MSW landfill sites.

Research on pipeline leakage localization method based on CZIM algorithm

The time difference of arrival is a common method to find the leakage point of water pipeline. The leakage point localization is achieved by calculating the time delay between the signals reaching different sensors. Mainstream time delay estimation algorithms based on signal correlation analysis are susceptible to the introduction of noise signals, low sampling rates, and signal clipping, resulting in inaccurate localization results. The article analyzed the impact of different interference factors and proposed a new time delay estimation algorithm based on signal cross-zero information modulation (CZIM) to address these problems. By normalizing the amplitude of the two signals at the detection points on both sides of the pipeline leakage position, two sets of sparse signal sequences with only two eigenvalues of 0 and 1 are obtained. The error coefficient function is calculated by a similar traversal method to finally index the time delay. In this paper, the principle and characteristics of the algorithm are analyzed and compared with the most commonly used GCC method. In both numerical simulations and actual pipe leakage localization experiments, the CZIM algorithm has shown its wide applicability, low impact by low sampling rate, and adaptability to low signal-to-noise ratios, etc. At the same time, the algorithm is simple in design and has a small amount of calculation and can meet the demand for real-time data processing, providing a new idea for the development of acoustic localization technology.

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling

Leakage from the overhead hydrogen piping (OHP) must be contained because of the wide flammability range and low minimum ignition energy of hydrogen; rapid leakage detection and appropriate emergency responses are necessary to ensure safe OHP operation. Consequence analysis after leakage detection is useful for hazardous-area estimation and assists operators in emergency-response planning. However, consequence analysis and leakage-detection methods have not been combined so far, and an integrated method has not yet been developed. This study aimed to develop a method based on machine learning (ML), physical modeling, and consequence analyses for rapid and appropriate decision-making after hydrogen leakage. Initially, an OHP model was constructed using physical modeling; this is a method that models target systems based on fundamental physical equations. Subsequently, an ML model for leakage detection was constructed based on various pressure or flow-rate profiles obtained from the OHP model. Regression models were constructed to estimate the leakage diameter. In addition, consequence analyses were conducted to estimate the effects of hydrogen explosions and fires using the leakage diameters predicted by the regression model. We confirmed that the ML model could distinguish between leakage and non-leakage conditions, and the estimated consequences could be used to visualize the risk level of hazardous areas near the leakage points in the case studies. The results of the case studies demonstrated the effectiveness of the proposed method for rapid decision making when hydrogen leaks from OHP. This method enables improved emergency responses in post-leakage situations.

Pathogenesis of Central Serous Chorioretinopathy and the Link Between Choroidal Hyperpermeability and Retinal Pigment Epithelium Pump Reversal

Purpose: To describe 6 cases of acute central serous chorioretinopathy (CSCR) and the response to laser treatment, focusing on the underlying pathogenic mechanism. Methods: Multimodal imaging from 6 eyes of 6 patients with acute and recurrent CSCR were reviewed including fundus autofluorescence (FAF), fluorescein angiography (FA), indocyanine green angiography (ICGA) and optical coherence tomography (OCT) at baseline and after laser therapy. Results: In 3 out of the 6 cases with acute CSCR, the hyporeflective lucency sign was identified with cross sectional and en face OCT and co-localized with an intense active inkblot retinal pigment epithelium (RPE) leak on FA. The development of this sign was suggestive of active leakage into the subretinal space displacing overlying subretinal hyperreflective material (SHRM) and suggestive of a reversal of RPE pump function. All six cases were treated with focal laser to directly target the intense leakage points with remarkable resolution of the fluid due to destruction of the RPE cells mediating reversal of pump function. Conclusions: Unlike chronic CSCR in which degenerative changes of the RPE lead to oozing of fluid into the subretinal space, in acute forms of CSCR including bullous CSCR, there are focal leaks of the RPE that actively drive fluid into the subretinal space suggestive of RPE pump reversal. We propose that pachychoroid disease causes increased hydrostatic pressure and increased resistance to the RPE pump thereby triggering a reversal in pump function. Understanding this concept can have therapeutic implications.

Evolution of mechanical properties of shield tunnels induced by water-soil gushing

Water-soil gushing in shield tunnels has been a major threat to the safety of tunnel structures. However, traditional numerical methods cannot reasonably simulate large soil deformation around the tunnel caused by the water-soil gushing. Therefore, it is difficult to reveal the evolution of the internal force and deformation of tunnel structures. In this paper, based on the two-phase Material Point Method, the numerical analysis method for the water-soil gushing was established to investigate the development of water-soil pressure acting on the tunnel surface, and then the evolution of the tunnel mechanical behaviours in the process of tunnel gushing was further studied. The results indicate that during the “rapid gushing stage”, the tunnel surface pressures around the leakage point decrease sharply, whereas those on the tunnel invert increase significantly. This gives rise to a significant increase in the shear force and bending moment of the tunnel lining, with increase rates of 114% and 82%, respectively. In addition, the axial and angular deformation of Joint No.2 near the leakage point increased significantly, which may lead to serious longitudinal joint dislocation or even concrete crushing. Thus, close attention to the joint deformation is required.