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Marine pipelines for the transportation of oil and gas have become a safe and reliable part of the expanding infrastructure put in place for the development of the valuable resources below the worlds seas and oceans. The design of these pipelines is a relatively new technology and continues to evolve as the design of more cost effective pipelines becomes a priority and applications move into deeper waters and more hostile environments. This updated edition of a best selling title provides the reader with a scope and depth of detail related to the design of offshore pipelines and risers not seen before in a textbook format.

This book is based on the authors' 30 years of experience in offshore. The authors provide rigorous coverage of the entire spectrum of subjects in the discipline, from pipe installation and routing selection and planning to design, construction, and installation of pipelines in some of the harshest underwater environments around the world. All-inclusive, this must-have handbook covers the latest breakthroughs in subjects such as corrosion prevention, pipeline inspection, and welding, while offering an easy-to-understand guide to new design codes currently followed in the United States, United Kingdom, Norway, and other countries.

Accurate segmentation of underwater pipelines is essential for marine infrastructure inspection. However, deep learning models often struggle with extreme underwater conditions such as low light, sea snow, and sea fog, leading to poor generalization on unseen data. Existing approaches typically focus on either accuracy or computational efficiency, leaving the challenge of achieving an optimal balance between the two unresolved. This paper introduces a novel hybrid architecture, the Swin Transformer-EFSNet fusion network, which delivers state-of-the-art accuracy with significantly reduced computational complexity and strong generalization capability. The model employs a dual-encoder design: a lightweight Swin Transformer branch to capture contextual relationships and a modified EFSNet branch optimized for efficient local feature extraction. Their outputs are dynamically integrated using a three-head cross-attention fusion module which prioritizes salient spatial and contextual information before decoding the final segmentation mask. We also present the HOMOMO dataset, a new benchmark containing images with challenging conditions such as low light, fog, sea snow, and complex occlusions (e.g., pipelines buried under sand or covered by vegetation). Extensive experiments on HOMOMO and two public datasets demonstrate that our method outperforms strong baselines, including UNet, SwinUNet, TransUNet, Mask2Former, YOLOv5, YOLOv11, and YOLOv12. On HOMOMO, our model achieves a mIoU of 98.44% and an F-boundary of 82.01%, surpassing the best-performing method by 8.43% and 5.34%, respectively. Crucially, the proposed model exhibits outstanding generalization to unseen data, demonstrating robustness against domain shifts. By effectively balancing global and local processing, our hybrid design achieves high accuracy without imposing heavy computational costs. These results establish a new paradigm for efficient and reliable visual perception in underwater environments, paving the way for practical autonomous inspection systems.

This paper develops and compares different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. These architectures are underwater wired sensor networks, underwater acoustic wireless sensor networks, RF (Radio Frequency) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper also discusses the reliability challenges and enhancement approaches for these network architectures. The reliability evaluation, characteristics, advantages, and disadvantages among these architectures are discussed and compared. Three reliability factors are used for the discussion and comparison: the network connectivity, the continuity of power supply for the network, and the physical network security. In addition, the paper also develops and evaluates a hierarchical sensor network framework for underwater pipeline monitoring.

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm2 to 25.96 cm2, the estimated results were close to the actual area trends (R2 = 0.9425), and the area error was within 5.16 cm2 (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage.

The inspection of condition of underwater pipelines (UPs) based on autonomous underwater vehicles (AUVs) requires high accuracy of positioning while the AUV is moving along to the object being examined. Currently, acoustic, magnetometric, and visual means are used to detect and track UPs with AUVs. Compared to other methods, visual navigation can provide higher accuracy for local maneuvering at short distances to the object. According to the authors of the present article, the potential of video information for these purposes is not yet fully utilized, and, therefore, the study focused on the more efficient use of stereo images taken with an AUV’s video camera. For this, a new method has been developed to address inspection challenges, which consists in the highlighting of visible boundaries and the calculation of the UP centerline using algorithms for combined processing of 2D and 3D video data. Three techniques for initial recognition of the direction of UP upon its detection were analyzed: on the basis of a stereo-pair of images using point features of the surface; using tangent planes to the UP in one of the stereo-pair; and using the UP median planes in both images of the stereo-pair. Approaches for determining the parameters of the relative positions of the AUV and the UP during the subsequent tracking are also considered. The technology proposed can be of practical use in the development of navigation systems to be applied for UP inspection without deploying additional expensive equipment, either separately or in combination with measurements from other sensors.

The article describes methodological foundations for calculating the mechanical properties of the base metal of pipes: impact work, resistance to corrosion cracking under pressure, crack tip opening displacement at operating temperatures according to the known properties of table parameters “on the left” to unknown parameters “on the right”. The calculated values provide an assess of the system stability during the design and construction of extended offshore underwater pipelines, including a necessary reduction in the safety margin, taking into account hydraulic friction losses during gas pumping.

Underwater pipelines are the channels for oil transportation in the sea. In the course of pipeline operation, leakage accidents occur from time to time for natural and man-made reasons which result in economic losses and environmental pollution. To avoid economic losses and environmental pollution, damage detection of underwater pipelines must be carried out. In this paper, based on the histogram of oriented gradient (HOG) and support vector machine (SVM), a non-contact ultrasonic imaging method is proposed to detect the shedding damage of the metal underwater pipeline external anti-corrosion layer. Firstly, the principle of acoustic scattering characteristics for detecting the metal underwater pipelines is introduced. Following this, a HOG+SVM image-extracting algorithm is used to extract the pipeline area from the underwater ultrasonic image. According to the difference of mean gray value in the horizontal direction of the pipeline project area, the shedding damage parts are identified. Subsequently, taking the metal underwater pipelines with three layers of polyethylene outer anti-corrosive coatings as the detection object, an Autonomous Surface Vehicle (ASV) for underwater pipelines defect detection is developed to verify the detection effect of the method. Finally, the underwater ultrasonic image which used to detect the metal underwater pipeline shedding damage is obtained by acoustic sensor. The results show that the shedding damage can be detected by the proposed method. With the increase of shedding damage width, the effect of pipeline defect location detection is better.

Natural gas is an important source of energy. Underwater gas pipeline leaks, on the other hand, have a serious impact on the marine environment; hence, the need for a reliable and preferably automated inspection method is essential. Due to the high impedance difference and strong scattering properties of gas bubbles in the marine environment, sonar systems are recognized as excellent tools for leak detection. In this paper, a new method for gas leak detection is proposed based on gas bubble acoustic scattering modeling using Synthetic Aperture Sonar (SAS) technology, in which a coherent combination of gas bubble and pipeline scattering fields at different angles along synthetic apertures is used for leak detection. The proposed method can distinguish leak signals from the background noise using coherent processing in SAS range migration. SAS as an active sonar can collect accurate information at wide area coverage rate, independent of operating range and frequency, which can potentially reduce the time and cost of pipeline inspection. The simulation and comparison results of the proposed method based on coherent processing of synthetic aperture technology and the real aperture system show that the proposed method can effectively distinguish gas bubble signals at different ranges even in a single pass and improves pipeline leak detection operations.