An Artificial Neural Network-Based Equation for Predicting the Remaining Strength of Mid-to-High Strength Pipelines with a Single Corrosion Defect

Numerical methods such as finite element analysis (FEA) can accurately predict remaining strength, with strong correlation with actual burst tests. However, parametric studies with FEA are time and computationally intensive. Alternatively, an artificial neural network-based equation can be used. In this work, an equation for predicting the remaining strength of mid-to-high strength pipelines (API 5L X52, X65, and X80) with a single corrosion defect subjected to combined loadings of internal pressure and longitudinal compressive stress was derived from an ANN model trained based on FEA results. For FEA, the pipe was assumed to be isotropic and homogenous, and the effects of temperature on the pipe failure pressure were not considered. The error of remaining strength predictions, based on the equation, ranged fro

Failure pressure prediction of high toughness pipeline with a single corrosion defect subjected to combined loadings using artificial neural network (ANN)

Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (R2) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress

Artificial Neural Network-Based Failure Pressure Prediction of API 5L X80 Pipeline with Circumferentially Aligned Interacting Corrosion Defects Subjected to Combined Loadings

Conventional pipeline corrosion assessment methods produce conservative failure pressure predictions for pipes under the influence of both internal pressure and longitudinal compressive stress. Numerical approaches, on the other hand, are computationally expensive. This work provides an assessment method (empirical) for the failure pressure prediction of a high toughness corroded pipe subjected to combined loading, which is currently unavailable in the industry. Additionally, a correlation between the corrosion defect geometry, as well as longitudinal compressive stress and the failure pressure of a pipe based on the developed method, is established. An artificial neural network (ANN) trained with failure pressure from FEA of an API 5L X80 pipe for varied defect spacings, depths, defect lengths, and longitudinal compressive loads were used to develop the equation. With a coefficient of determination (R2) of 0.99, the proposed model was proven to be capable of producing accurate predictions when tested against arbitrary finite element models. The effects of defect spacing, length, and depth, and longitudinal compressive stress on the failure pressure of a corroded pipe with circumferentially interacting defects, were then investigated using the suggested model in a parametric analysis

The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review

Due to their exceptional structural integrity, steel pipelines are the main component for oil and gas transmission. However, these pipelines are often affected by corrosion, despite corrosion protection, because of harsh working conditions. In addition to corrosion defects, pipelines are often subjected to multiple external loads. The combination of corrosion defects and external loads can significantly reduce the failure pressure, resulting in various failure behaviors. This reduction in failure pressure is especially critical in pipe bends as they are the weakest link in a pipeline. This paper presents an overview of the failure behavior of corroded steel pipe components subjected to internal pressure and axial compressive stress

Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

Conventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with a longitudinal interacting corrosion defect subjected to combined loadings is proposed. An artificial neural network (ANN) trained with failure pressure obtained from finite element analysis (FEA) of API 5L X52, X65, and X80 pipes for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of the defect spacing, length, and depth, and axial compressive stress on the failure pressure of a corroded pipe with longitudinal interacting defects was performed to demonstrate a correlation between defect geometries and failure pressure of API 5L X52, X65, and X80 pipes, using the equation. The new equation predicted failure pressures for these pipe grades with a coefficient of determination (R2) value of 0.9930 and an error range of −10.00% to 1.22% for normalized defect spacings of 0.00 to 3.00, normalized effective defect lengths of 0.00 to 2.95, normalized effective defect depths of 0.00 to 0.80, and normalized axial compressive stress of 0.00 to 0.80

Failure Pressure Prediction of Medium to High Toughness Pipe with Circumferential Interacting Corrosion Defects Subjected to Combined Loadings Using Artificial Neural Network

Assessment of a corroded pipe is crucial to determine when it must be repaired or replaced. However, the conventional corrosion assessment codes for the failure pressure predictions of corroded pipes with circumferentially aligned interacting defects are conservative (underestimations of more than 40%), resulting in premature repair or replacements of pipelines. Alternatively, numerical approaches may be used, but they are time consuming and computationally expensive. In this study, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with circumferentially aligned interacting corrosion defects subjected to combined loadings is proposed. An artificial neural network trained with failure pressure obtained from the finite element analysis of the three pipe grades for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of these parameters on the failure pressure of a corroded pipe with circumferential-interacting defects was conducted using the equation to determine the correlation between the defect geometries and failure pressure of the pipe. The new equations predicted failure pressures for these pipe grades with an R2 value of 0.99 and an error range of −9.92% to 0.98% for normalised defect spacings of 0.00 to 3.00, normalised effective defect lengths of 0.00 to 2.95, normalised effective defect depths of 0.00 to 0.80, and normalised axial compressive stress of 0.00 to 0.60

Initial Virologic Response and HIV Drug Resistance Among HIV-Infected Individuals Initiating First-line Antiretroviral Therapy at 2 Clinics in Chennai and Mumbai, India

Human immunodeficiency virus drug resistance (HIVDR) in cohorts of patients initiating antiretroviral therapy (ART) at clinics in Chennai and Mumbai, India, was assessed following World Health Organization (WHO) guidelines. Twelve months after ART initiation, 75% and 64.6% of participants at the Chennai and Mumbai clinics, respectively, achieved viral load suppression of <1000 copies/mL (HIVDR prevention). HIVDR at initiation of ART (P <.05) and 12-month CD4 cell counts <200 cells/μL (P <.05) were associated with HIVDR at 12 months. HIVDR prevention exceeded WHO guidelines (≥70%) at the Chennai clinic but was below the target in Mumbai due to high rates of loss to follow-up. Findings highlight the need for defaulter tracing and scale-up of routine viral load testing to identify patients failing first-line AR

Covert communication over multi-user channels

The objective of the proposed research is to characterize the maximum rate at which information can be transmitted reliably to a legitimate receiver over certain multi-user channels while simultaneously escaping detection from one or more adversaries. Specifically, we investigate the fundamental limits of covert communication over the following multi-user channel models — a K-user discrete memoryless multiple-access channel (MAC) monitored by a single warden, a discrete memoryless broadcast channel in which one of the receivers is a warden trying to detect the presence of a covert message, and a relay channel model in which the relay aids covert transmission amidst two non-colluding wardens each monitoring the transmitter and the relay, respectively. In all three models, we observe that the covert throughput is subject to the square-root law. Also, building upon a previous result that codeword-level asynchronism results in an improved covert throughput that circumvents the square-root law, we analyze the impact on the covert throughput as a result of symbol-level asynchronism at the receiver and the warden.Ph.D

Empirical Failure Pressure Prediction Equations for Pipelines with Longitudinal Interacting Corrosion Defects Based on Artificial Neural Network

Conventional pipeline failure pressure assessment codes do not allow for failure pressure prediction of interacting defects subjected to combined loadings. Alternatively, numerical approaches may be used; however, they are computationally expensive. In this work, an analytical equation based on finite element analysis for the failure pressure prediction of API 5L X52, X65, and X80 corroded pipes with a longitudinal interacting corrosion defect subjected to combined loadings is proposed. An artificial neural network (ANN) trained with failure pressure obtained from finite element analysis (FEA) of API 5L X52, X65, and X80 pipes for varied defect spacings, depths and lengths, and axial compressive stress were used to develop the equation. Subsequently, a parametric study on the effects of the defect spacing, length, and depth, and axial compressive stress on the failure pressure of a corroded pipe with longitudinal interacting defects was performed to demonstrate a correlation between defect geometries and failure pressure of API 5L X52, X65, and X80 pipes, using the equation. The new equation predicted failure pressures for these pipe grades with a coefficient of determination (R2) value of 0.9930 and an error range of &minus;10.00% to 1.22% for normalized defect spacings of 0.00 to 3.00, normalized effective defect lengths of 0.00 to 2.95, normalized effective defect depths of 0.00 to 0.80, and normalized axial compressive stress of 0.00 to 0.80