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At present, the horizontal distance between the surface subsidence boundary and the panel is typically selected as the width of the protection coal pillar with the buried pipeline at the gas–coal integrated mining area (traditional method), which causes abundant coal resources to be unrecoverable. To improve the recovery rate of coal resources, the protective coal pillar of the pipeline is optimally designed. First, the Gaussian function equation of the surface subsidence curve is investigated using the probability integral method (PIM). The elastic deformation limit of the pipeline within the subsidence basin was analysed. Then, the failure probability of the pipeline was calculated by analysing the multifactor indicators that affect it. The elastic deformation limit was modified by considering the time effect of the surface subsidence and the failure probability. Next, by analysing the pipeline deformation in the mining subsidence basins, a novel method for the optimal width of the protective coal pillars with buried pipelines in the thick loose layer undermining is proposed. Meanwhile, the verification method and protection measures for pipeline safety are proposed. Finally, theoretical analysis and engineering examples are used for analysis and verification. The results show that the surface subsidence curve caused by critical mining can be expressed by the Gaussian function when the buried depth/thickness ratio (DTR) of the flat coal seam is greater than 40–60 under thick loose layer. Using Panel 132201 as an example, the prediction method reduced the width of the protected coal pillar by 14 m and increased the panel recovery rate by 3.11% while ensuring the safety of the pipeline. This method effectively promotes coordinated mining between oil–gas and coal resources and provides a reference for the design of pipeline protection coal pillars in gas–coal integrated mining areas. HighlightsA novel method for the optimal width of the protective coal pillars with buried pipelines in the thick loose layer undermining was proposed.Under the premise of considering pipeline safety, this method reduced the width of protective coal pillars and increased the panel recovery rate.The elastic deformation limit was corrected while considering the pipeline failure probability.

Ground blast loads are of great importance to the safe operation of steel and gas pipelines, and the results obtained from traditional theoretical formulas for pipeline safety prediction are in error with the actual measured data. In this paper, full-size field tests and corresponding numerical simulations are carried out using Timoshenko beam theory and explosion stress wave theory, which consider shear effects. At the same time, combined with the theory of foundation stiffness and pipeline stiffness flexibility ratio, a modified theoretical model is obtained in line with the actual conditions of the site, which can accurately calculate the deformation and displacement of pipeline underground explosion load, and greatly reduce the error of theoretical prediction results. The innovation of the research results in this paper is that the theoretical stress in the Timoshenko beam can be replaced by the circumferential strain. On the other hand, the modified theoretical solution can obtain the critical weight of explosives to prevent pipeline damage at different buried depths. It provides a theoretical basis for the protection of pipelines’ underground blast loads and provides research ideas for the safe protection and design of pipelines.

Hydraulic transients pose a significant threat to pipeline integrity, leading to catastrophic failures from pressure surges. Traditional methods for selecting protection devices – such as air vessels and surge tanks – often rely on engineering judgment, potentially leading to suboptimal solutions. This study introduces a data-driven approach using artificial neural networks (ANNs) to objectively select the most suitable protection devices, overcoming the limitations of conventional engineering intuition. A comprehensive dataset, representing diverse pipeline configurations and commercial materials, was developed. Utilizing established selection criteria, we identified optimal protection devices for various scenarios. Four distinct ANN architectures were trained and assessed based on performance metrics such as accuracy and precision, with the best model validated using an independent dataset of previously unseen configurations. The trained ANN model demonstrated 91.5% accuracy in device selection, outperforming traditional methods and offering enhanced strategies for pipeline protection across diverse scenarios. By incorporating a broader range of pipeline configurations and physical factors, the proposed ANN-based approach offers a robust tool for optimizing pipeline protection strategies and transcends engineer intuition, potentially revealing unconventional yet highly effective solution.

Although a lot of AC corrosion failure cases and research work on cathodically protected pipelines have been reported, the mechanism of the AC corrosion process has not been completely understood, and there still exist many debates on AC corrosion assessment criteria under cathodic protection (CP), especially under a high CP level. AC corrosion simulation experiments in three kinds of environments were conducted to study the AC corrosion behavior under different conditions. Based on the AC corrosion rates and the corresponding AC current densities, DC IR-free potentials, and DC current densities, the AC corrosion rate assessment diagrams were presented and the threshold values for AC and DC parameters corresponding to certain corrosion rates were determined in three studied environments. Besides, the AC corrosion morphologies, products, local environment parameters, and electrochemical characteristics were measured and analyzed under the combined effects of AC and CP. The effect of hydrogen evolution reaction on the AC corrosion process under a high CP level was discussed based on the electrical equivalent circuit model at the steel/electrolyte interface, the dynamic electrochemical reaction process, and the change of local environment close to the specimen surface.

Water hammer phenomena pose significant threats to the operational safety and structural integrity of long-distance water transmission pipeline systems. This study develops an integrated intelligent system combining deep recurrent neural networks with distributed pressure sensor data fusion for water hammer transient prediction and dynamic protection optimization. A multi-layer bidirectional Long Short-Term Memory network with attention mechanism is constructed to capture spatial-temporal pressure dynamics from distributed sensor measurements. A Deep Q-Network based reinforcement learning algorithm generates optimal real-time protection strategies by coordinating multiple devices including surge tanks, relief valves, and valve closure sequences. Comprehensive validation demonstrates that the proposed system achieves superior prediction accuracy compared to conventional methods and significantly reduces maximum transient pressures while shortening stabilization duration. The intelligent decision framework provides water utilities with an adaptive tool for enhancing pipeline safety, minimizing infrastructure damage risks, and optimizing protection resource allocation in complex hydraulic systems.

Ground collapse is one of the main geological disasters affecting the safe operation of oil and gas pipelines. Studying the stress-strain characteristics of pipe-soil interaction under ground collapse has an important guiding role for the prevention of ground collapse and the safety protection of pipelines. The current results are mostly concentrated in a single theory or the stress of the pipeline itself, which cannot fully consider the pipe-soil interaction. In this paper, ABAQUS is used to establish the finite element geometric model. Considering the pipeline and the surrounding geological environment conditions, the study is carried out from five aspects: the length of the collapse area, the thickness of the cover layer, the buried depth of the pipeline, the diameter of the pipeline, and the thickness of the pipeline. The displacement of the pipeline soil, the deformation of the pipeline, and the characteristics of the pipeline stress are analyzed, and the variation law is determined through the development trend of the pipeline stress and strain. At the same time, by fitting and analyzing the relationship between the span of different subsidence areas, the thickness of the cover layer, the buried depth of the pipeline, the diameter of the pipeline, the thickness of the pipeline, and the maximum deformation of the pipeline, the reference value of the maximum subsidence displacement of the pipeline under the action of ground collapse is proposed. The work has practical application value for pipeline monitoring, early warning, and disaster management.

Submarine pipelines are the main transport carriers of marine resources. In order to protect these pipelines, geotextile and stone covering measures are adopted in this paper and the protective effect is studied. A sequence of physical model tests was conducted to carry out the research. The hydrodynamic characteristics and seabed oscillation response of the seabed surrounding the pipeline were analyzed with or without geotextile and stone cover protection, and it was found that they were affected by waves (and currents). The experimental results show the following: (1) comparing the regular wave and current with the regular wave alone, it is found that forward current promotes wave propagation and reverse current inhibits wave propagation; (2) the protective effect of geotextile and stone covering measures on different positions of the pipeline (the front, the bottom, and the back of the pipe) is basically same; (3) in the case of waves with large wave heights and long wave periods superimposed with ocean currents, the protective effect of geotextile and stone coverings on the hydrodynamic and seabed pore pressure around the pipeline is more significant.

In this paper, the depth of scouring phenomenon below the pipelines across rivers was predicted using Support Vector Machine (SVM). To this end, the related dataset was collected from literature. Performance of SVM was evaluated via calculation of error indices such as coefficient of determination (R 2 ) and Root Mean Square of Error (RMSE). The accuracy of SVM was compared with artificial neural network (ANN) and Adaptive Neuro fuzzy Inference Systems (ANFIS). To find out the most effective parameters on scouring depth, a sensitivity analysis was conducted on ANN, ANFIS, and SVM. During the development of SVM, it was found that this model with R 2 = 0.94 and RMSE = 0.103 in testing stage has a suitable performance for predicting the scouring depth below the river pipeline. Assessing kernel functions showed that radial basis function has the best outcomes. Comparing the accuracy of SVM with ANN and SVM showed that the accuracy of SVM is a bit better than ANN with R 2 = 0.89 and RMSE = 0.12 and ANFIS with R 2 = 0.92 and RMSE = 0.13. Sensitivity analysis showed that e/D, τ* and Fr are the most effective parameters for predicting the scouring depth below the pipeline.

Positive and negative pressure waves caused by water hammer possibly may lead to high damages to the water conveyance pipelines. To decrease the negative effects of pressure waves, costly equipment are implemented in pipelines. An economic design of these devices that also provides the safety of the pipeline against water hammer pressure waves and cavitation can be achieved by simulation and optimization tools. In this paper, the simulation task was carried out by meta modeling. The accuracy of three meta models: artificial neural network (ANN), support vector regression (SVR) and adaptive neuro-fussy inference system (ANFIS) was evaluated. According to the results, SVR was identified as the inferior method due to low capability of generalization, ANFIS as the median, and ANN as the superior method for function approximation. Then, ANN was coupled with an evolutionary algorithm (EA), Differential Evolution (DE) to find the optimal size and location of water hammer control devices in a water pipeline. Optimization was carried out on two single- and multi-objective approaches. The results showed that multi-objective optimization approach presents better designs than the single objective approach and optimal designs obtained by both approaches outperform the current setup of the water hammer facilities in terms of both costs and functionality. The single objective-based design could decrease the costs up to 12.5% whereas multi-objective approach was able to reach nearly 30% cost saving with higher level of the safety against cavitation. Results also showed that air chamber is the most effective device and air-valves have little effect for pipeline protection against water hammer.

Failure of pipelines below road pavement results to the disruption of both the traffic movement and the consumers of the pipelines. Intermediate safeguard layer can be used to protect the pipeline from heavy traffic loads. The present study proposed analytical solutions to obtain the dynamic response of buried pipe below road pavement with and without considering safeguard based on the concept of triple and double beam system respectively. Pavement layer, safeguard and the pipeline are considered as Euler Bernoulli’s beam. Advanced soil model is used (viscoelastic foundation with shear interaction between springs) to model the surrounding soil. Self-weight of soil is also considered in the present study. The obtained governing coupled differential equations are solved adopting finite sine Fourier transform, Laplace transform and their inverse transformation. The proposed formulation is initially verified with the past numerical and analytical studies and then validated with the three-dimensional finite element based numerical analysis. From parametric study it is perceived that the stability of the pipe can be significantly increased by providing intermediate barrier. Further, pipe deformation is increases with increasing traffic loads. At very high-speed range (> 60 m s −1 ), pipe deformation is significantly rises with increasing traffic speed. The present study can be useful in preliminary design stage before performing rigorous and expensive numerical or experimental study.