Search Results (11)

Accurate assessment of pipeline defect integrity and proactive risk warning are essential for the safe, reliable, and economical transportation of oil and gas. Existing approaches are largely based on static assessment models, such as the Det Norske Veritas Recommended Practice for corroded pipelines (DNV-RP-F101), and often produce conservative failure-pressure predictions because time-dependent defect evolution and operational pressure fluctuations are not considered. To address this limitation, this study develops a dynamic defect-growth–failure-pressure coupling model that integrates a long short-term memory (LSTM) network with an enhanced DNV-RP-F101 framework. Time-varying axial and circumferential correction coefficients are introduced to update the bulging factor dynamically, thereby supporting defect-growth prediction and time-variant failure-pressure calculation. The model is validated against four established standards using public pipeline datasets. For single defects, the proposed model achieves the lowest mean square error (MSE) of 0.81 MPa and an average error of 1.18 MPa among the compared methods. For defect clusters, the prediction error remains within 8.64%. A five-level dynamic risk-warning system is further established by integrating Monte Carlo simulation with API 579 standards, enabling quantification of failure probability and prediction of remaining service life. Engineering case studies show that the proposed method can identify the time points at which pipelines enter hazardous failure-probability stages. This capability supports more precise early warning and provides a technical basis for intelligent pipeline integrity management and predictive maintenance. Full article

Steel pipelines for oil and gas transportation serve as the lifeline of energy conveyance, and their long-term safe operation constitutes a crucial safeguard for energy security. Nevertheless, in complex service environments, local defects formed on the inner pipe wall due to medium corrosion have emerged as a prominent hidden danger endangering pipeline integrity. Accurate evaluation of the residual strength of pipelines with corrosion defects is not only the technical foundation for ensuring the safe operation of pipelines, but also the key basis for formulating scientific maintenance strategies and prolonging the service life of pipelines. Taking three grades of steel pipelines (X52, X65 and X80), which represent the typical strength grades commonly used in long-distance oil and gas transmission pipelines, as the research objects, this paper establishes a three-dimensional finite element model of single-point uniform corrosion defects considering the nonlinear material behavior, and systematically investigates the influence laws of geometric parameters (depth, length and width) of corrosion defects on the failure pressure of pipelines under the action of monotonic internal pressure load. The accuracy of the proposed finite element model is verified by comparison with the test data from thirteen groups of full-scale burst experiments. On the basis of parametric analysis results, an explicit and high-precision predictive model for failure pressure is developed. The research findings reveal that corrosion depth acts as the dominant factor affecting pipeline failure pressure with a distinctly nonlinear influence characteristic: the load-bearing capacity of pipelines drops drastically when the relative depth d/t exceeds 0.6, where d is the corrosion depth and t is the pipe wall thickness. There exists a critical value for the impact of corrosion length, beyond which its weakening effect on failure pressure tends to level off. Within the commonly encountered engineering range (20~100°), corrosion width exerts a negligible influence on pipeline failure pressure and thus can be overlooked in engineering evaluation. In comparison with conventional industry assessment methods such as ASME B31G, DNV RP-F101, PCORRC and SY/T 6151, the newly established predictive model features higher prediction accuracy and broader applicability, which provides on-site engineers with a powerful theoretical tool and practical formula for the rapid and accurate evaluation of the residual strength of corroded pipelines. Full article

Subsea pipelines are critical lifelines for marine resource development, yet they face severe threats from accidental ship anchor impacts. This study addresses the scientific challenge of quantifying the “protection margin” of artificial rock-dumping layers, moving beyond traditional passive structural response to a “Critical Failure Intervention” logic. Based on the energy criteria of DNV-RP-F107, a critical velocity required to trigger Concrete Weight Coating (CWC) failure for a bare pipe was derived and established as the Safety Factor baseline (S = 1). Two groups of scaled model tests (1:15) were conducted using a Hall anchor to simulate impact scenarios, where impact forces were measured via force sensors beneath the pipeline under varying backfill thicknesses and configurations. Results show that artificial backfill provides a significant protective redundancy; a 10 cm coarse rock layer increases the safety factor to 3.69 relative to the H₀ baseline, while a multi-layer configuration (sand bedding plus coarse rock) elevates S to 27. Analysis reveals a non-linear relationship between backfill thickness and cushioning efficiency, characterized by diminishing marginal utility once a specific thickness threshold is reached. These findings indicate that while thickness is critical for extreme impacts, the protection efficiency optimizes at specific depths, providing a quantifiable framework to reduce small-particle layers in engineering projects without compromising safety. Full article

To ensure the economy and safety of the pipelines, the study of the residual strength of corrosion pipelines is key to determining whether the pipelines can continue to operate. There is often a conflict between accuracy and convenience. Artificial intelligence algorithms offer the advantages of high accuracy and ease of use. Therefore, research on the prediction of the residual strength of corroded pipelines using artificial intelligence algorithms is of great significance. CNN and LSTM algorithms are often used to predict the remaining strength of pipelines. However, single CNN models perform poorly in handling time-series data, while LSTM and BiLSTM models also have limitations in processing high-dimensional spatial features. In this article, a pipeline residual strength prediction model based on the CNN-BiLSTM-Adaboost algorithm is proposed. Correlation analysis was used to evaluate the influencing factors of the pipeline’s residual strength, and the CNN algorithm parameters were optimized using BiLSTM and AdaBoost algorithms. The proposed CNN–BiLSTM–AdaBoost evaluation method achieves a significantly improved prediction accuracy for pipeline residual strength, with an average relative error of 4.694%. Our method reduces the predictive error by 28.901%, 43.391%, and 40.753% relative to ASME B31G, DNV RP F101, and PCORRC. This model can predict the residual strength of pipelines conveniently and accurately, minimizing losses caused by corrosion. Full article

This paper presents a novel approach for predicting the failure pressure of corroded pipelines by integrating empirical formulas into the loss function of a neural network-based prediction model. Traditional empirical formulas, such as ASME-B31G, DNV RP-F101, and PCORRC, have been widely used for their simplicity but often suffer from significant prediction errors due to the complex interactions between defect parameters and material properties. In contrast, artificial neural networks (ANNs) offer more accurate predictions but require substantial training data. To address these limitations, we propose an integrated loss function that combines the strengths of empirical formulas and the powerful fitting capabilities of ANNs. The proposed loss function incorporates an additional defect factor term predicted by the neural network to compensate for errors caused by varying defect conditions, thereby enhancing the model′s adaptability and accuracy. The model is trained using a diverse dataset of 60 burst test results from various literature sources, covering a wide range of corrosion scenarios. The results demonstrate that the proposed method significantly improves prediction accuracy compared to traditional empirical formulas and ANN models trained with standard loss functions. The proposed approach achieves a mean absolute percentage error (MAPE) of 2.52%, a root mean square error (RMSE) of 0.39 MPa, and a coefficient of determination (R²) of 0.9886 on the validation set. This study highlights the effectiveness of integrating empirical knowledge with data-driven models and provides a robust and accurate solution for predicting the failure pressure of corroded pipelines, contributing to enhanced pipeline integrity assessment and safety management. Full article

Fatigue poses significant challenges for the structural integrity of monopiles, the most common type of foundation for offshore wind turbines. These structures are usually manufactured by rolling and welding together large steel plates. Offshore wind turbines are typically designed to operate for 20 years or longer, thus the number of cycles to failure ($N_f$) that these structures are required to withstand lies in the so called ultrahigh-cycle fatigue (UHCF) regime ($N_f>{10}^8$). Moreover, because, in the past few years, there has been a continuous increase in the size of monopiles, the fatigue life reduction caused by the utilization of thicker steel plates plays an important role (i.e., thickness or size effect). Different regions worldwide apply distinct codes to ensure that offshore structures can withstand fatigue damages, but most of them are tailored for the high-cycle fatigue (HCF) regime. This paper seeks to compare a selection of these codes, highlighting both differences and similarities, while also questioning their suitability in the UHCF regime and for much thicker structures (compared to the reference thickness values reported in the standards). By doing so, it aims to contribute to the ongoing efforts to optimize the efficiency of the fatigue life assessment of offshore wind infrastructures. The focus of this study is on double-V transverse butt welds and their S-N curves in air and seawater (with and without cathodic protection), while the analyzed standards are those provided by the Det Norske Veritas (DNV-RP-C203-2021), the British Standards Institution (BS 7608, including the amendments of 2015), and the European Union (EN 1993-1-9, updated in 2005). The results have been discussed in terms of the level of conservatism that each of these standards offers and in identifying the areas for further research to enable extended lives in the current and future offshore wind monopile foundations. Full article

The DNVGL-RP-F105 guidelines provide essential techniques for the preliminary design of undersea pipelines. However, its approximations for static displacement and the natural frequency of the pipe are restricted to cases where the ratio of the pipe’s diameter to its length (L/D) is less than 140. This limitation poses challenges for longer spans, which, although rare, can sometimes be unavoidable. This study introduces a novel analytical method, rooted in the energy method and cable theory, for computing the static deformation and natural frequency of long free-span underwater pipelines. We conducted a comprehensive verification of our proposed method by comparing its outcomes with those of 212 finite element analysis simulations. The results reveal excellent predictions for long spans. However, for shorter spans, traditional methods remain more accurate. Additionally, we explored the influence of pipeline’s diameter, thickness, and boundary conditions on both static displacement and frequency, providing valuable insights for design considerations. We found that the boundary conditions’ impact on the fundamental frequency becomes negligible for long spans, with up to a 10% difference between pinned–pinned and fixed–fixed conditions. In essence, this research offers a vital enhancement to the existing DNV guidelines, becoming particularly beneficial during the preliminary design phases of pipelines with L/D ratios exceeding 140. Full article

Most of the standards available for the assessment of the failure pressure of corroded pipelines are limited in their ability to assess complex loadings, and their estimations are conservative. To overcome this research gap, this study employed an artificial neural network (ANN) model trained with data obtained using the finite element method (FEM) to develop an assessment equation to predict the failure pressure of a corroded pipeline with a single corrosion defect. A finite element analysis (FEA) of medium-toughness pipelines (API 5L X65) subjected to combined loads of internal pressure and longitudinal compressive stress was carried out. The results from the FEA with various corrosion geometric parameters and loads were used as the training dataset for the ANN. After the ANN was trained, its performance was evaluated, and its weights and biases were obtained for the development of a corrosion assessment equation. The prediction from the newly developed equation has a good correlation value, R² of 0.9998, with percentage errors ranging from −1.16% to 1.78%, when compared with the FEA results. When compared with the failure pressure estimates based on the Det Norske Veritas (DNV-RP-F101) guidelines, the standard was more conservative in its prediction than the assessment equation developed in this study. Full article

Prediction of residual strength and residual life of corrosion pipelines is the key to ensuring pipeline safety. Accurate assessment and prediction make it possible to prevent unnecessary accidents and casualties, and avoid the waste of resources caused by the large-scale replacement of pipelines. However, due to many factors affecting pipeline corrosion, it is difficult to achieve accurate predictions. This paper reviews the research on residual strength and residual life of pipelines in the past decade. Through careful reading, this paper compared several traditional evaluation methods horizontally, extracted 71 intelligent models, discussed the publishing time, the evaluation accuracy of traditional models, and the prediction accuracy of intelligent models, input variables, and output value. This paper’s main contributions and findings are as follows: (1) Comparing several traditional evaluation methods, PCORRC and DNV-RP-F101 perform well in evaluating low-strength pipelines, and DNV-RP-F101 has a better performance in evaluating medium–high strength pipelines. (2) In intelligent models, the most frequently used error indicators are mean square error, goodness of fit, mean absolute percentage error, root mean square error, and mean absolute error. Among them, mean absolute percentage error was in the range of 0.0123–0.1499. Goodness of fit was in the range of 0.619–0.999. (3) The size of the data set of different models and the data division ratio was counted. The proportion of the test data set was between 0.015 and 0.4. (4) The input variables and output value of predictions were summarized. Full article

In order to study the residual strength of buried pipelines with internal corrosion defects in seasonally frozen soil regions, we established a thermo-mechanical coupling model of a buried pipeline under differential frost heave by using the finite element elastoplastic analysis method. The material nonlinearity and geometric nonlinearity were considered as the basis of analysis. Firstly, the location of the maximum Mises equivalent stress in the inner wall of the buried non-corroded pipeline was determined. Furthermore, the residual strength of the buried pipeline with corrosion defects and the stress state of internal corrosion area in the pipeline under different defect parameters was analyzed by the orthogonal design method. Based on the data results of the finite element simulation calculation, the prediction formula of residual strength of buried pipelines with internal corrosion defects was obtained by SPSS (Statistical Product and Service Solutions) fitting. The prediction results were analyzed in comparison with the evaluation results of B31G, DNV RP-F101 and the experimental data of hydraulic blasting. The rationality of the finite element model and the accuracy of the fitting formula were verified. The results show that the effect degree of main factors on residual strength was in order of corrosion depth, corrosion length, and corrosion width. when the corrosion length exceeds 600 mm, which affects the influence degree of residual strength will gradually decrease. the prediction error of the fitting formula is small and the distribution is uniform, it can meet the prediction requirements of failure pressure of buried pipelines with internal corrosion defects in seasonally frozen soil regions. This method may provide some useful theoretical reference for the simulation real-time monitoring and safety analysis in the pipeline operation stage. Full article

Machine learning tools are increasingly adopted in various industries because of their excellent predictive capability, with high precision and high accuracy. In this work, analytical equations to predict the failure pressure of a corroded pipeline with longitudinally interacting corrosion defects subjected to combined loads of internal pressure and longitudinal compressive stress were derived, based on an artificial neural network (ANN) model trained with data obtained from the finite element method (FEM). The FEM was validated against full-scale burst tests and subsequently used to simulate the failure of a pipeline with various corrosion geometric parameters and loadings. The results from the finite element analysis (FEA) were also compared with the Det Norske Veritas (DNV-RP-F101) method. The ANN model was developed based on the training data from FEA and its performance was evaluated after the model was trained. Analytical equations to predict the failure pressure were derived based on the weights and biases of the trained neural network. The equations have a good correlation value, with an R² of 0.9921, with the percentage error ranging from −9.39% to 4.63%, when compared with FEA results. Full article