Underwater Structures Research Articles

Managing freshwater invasive mussel biofouling: Insights into byssal adhesion on underwater surfaces.

Managing freshwater invasive mussel biofouling: Insights into byssal adhesion on underwater surfaces.

Biomimetic antifouling coatings: Harnessing nature’s strategies for sustainable marine protection

Biofouling, the unwanted buildup of microorganisms, plants, algae, and animals on underwater structures, presents considerable difficulties in multiple sectors, especially in maritime activities. Conventional antifouling techniques, typically dependent on biocidal coatings, have proven successful but pose environmental issues because of their harmfulness. As a result, studies have increasingly focused on biomimetic antifouling coatings that take cues from the natural antifouling methods seen in marine life. These advanced coatings seek to imitate the natural physical and chemical protections, providing environmentally friendly options to traditional techniques. Recent research has emphasized the promise of micro-structured surfaces, like those resembling shark skin, and chemical repellents derived from marine organisms, in inhibiting biofouling. Although promising, the creation and widespread use of these biomimetic coatings pose challenges, such as difficulties in fabrication and worries about durability. Future studies aim to address these challenges, concentrating on the development of sustainable, efficient, and scalable antifouling strategies.

Detection of defects in underwater concrete structures: Integrating compact robotic systems with an edge detection model

Detection of defects in underwater concrete structures: Integrating compact robotic systems with an edge detection model

Recognition of Underwater Engineering Structures Using CNN Models and Data Expansion on Side-Scan Sonar Images

Side-scan sonar (SSS) is a critical tool in marine geophysical exploration, enabling the detection of seabed structures and geological phenomena. However, the manual interpretation of SSS images is time-consuming and relies heavily on expertise, limiting its efficiency and scalability. This study addresses these challenges by employing deep learning techniques for the automatic recognition of SSS images and introducing Marine-PULSE, a specialized dataset focusing on underwater engineering structures. The dataset refines previous classifications by distinguishing four categories of objects: pipeline or cable, underwater residual mound, seabed surface, and engineering platform. A convolutional neural network (CNN) model based on GoogleNet architecture, combined with transfer learning, was applied to assess classification accuracy and the impact of data expansion. The results demonstrate a test accuracy exceeding 92%, with data expansion improving small-sample model performance by over 7%. Notably, mutual influence effects were observed between categories, with similar features enhancing classification accuracy and distinct features causing inhibitory effects. These findings highlight the importance of balanced datasets and effective data expansion strategies in overcoming data scarcity. This work establishes a robust framework for SSS image recognition, advancing applications in marine geophysical exploration and underwater object detection.

Crackwave R-convolutional neural network: A discrete wavelet transform and deep learning fusion model for underwater dam crack detection

Crack detection is an essential part of structural health monitoring (SHM) for underwater dams, which is crucial for preventing potential structural failures and ensuring the long-term stability. Deep learning-based image processing algorithms have become a research hotspot in the field of crack detection. However, the complex underwater environment has posed challenges to underwater dam crack detection. To address these issues, we propose CrackWave R-convolutional neural network (CW R-CNN), a novel underwater dam crack detection model that fuses discrete wavelet transform (DWT) and deep learning. The proposed model introduces a novel backbone network, DwtResNet, which incorporates DWT to comprehensively extract frequency-domain features from underwater crack images. To overcome the limitations of Intersection over Union (IoU), particularly when predicted and ground truth bounding boxes do not overlap, we employ the generalized IoU (GIoU) function. Furthermore, we apply the soft nonmaximum suppression (NMS) algorithm to reduce the risk of missing fine cracks. In addition, we utilized a self-developed underwater dam image acquisition robot to capture a large number of underwater dam crack images, forming the self-acquired dataset. Evaluating the proposed model on this dataset showed that its MAP_0.5 outperformed SSD, YOLOv5, and the conventional Faster R-CNN. The proposed model proved more effective than other models, especially in detecting fine cracks and handling complex backgrounds. These experimental results not only demonstrate the effectiveness of CW R-CNN in underwater dam crack detection but also highlight its potential application in SHM. It provides essential technical support for the safe monitoring of underwater dam structures.

Experimental Activity with a Rover for Underwater Inspection

The inspection of underwater structures is often hampered by harsh environmental conditions, limited access, high costs, and inherent safety issues. This paper focuses on the use of an underwater rover to implement automated imaging techniques for facilitating inspections. The application of such techniques can significantly improve the state of monitoring, reduce operational complexity, and partially offset the financial burden of periodic inspections. To date, there has been very little work on image-based techniques for detecting and quantifying the extent of structural damage, particularly in the submerged part of marine structures. This work seeks to address this knowledge gap through the development and performance evaluation of underwater photogrammetry. The development of the research has been carried out using the FIFISH V6 rover with the Brave 7 camera, which has all the characteristics required for successful photogrammetry. To connect the sensor to the rover, a support was designed accordingly. Finally, experimental photogrammetry tests of an anchor were carried out and compared, both in and out of the sea environment, to validate the model presented. The results obtained so far confirm the validity of the proposed approach and encourage the future development of this apparatus for underwater inspections.

Review on Repair Technologies for Underwater Concrete Structure Damage of Infrastructures

This paper comprehensively summarizes and discusses the latest research progress in the underwater concrete structure damage repair technology of infrastructures. The prompt application of underwater concrete structure repair technology can effectively deal with the damaged parts of underwater concrete structures, and it can ensure the safe and stable operation of infrastructure and extend its service life. Firstly, this study uses bibliometric methods to analyze the characteristics of the literature on research into underwater concrete repair in the past 30 years (1993–2023), and expounds the research status and hotspots of this field. Then, we conduct a comprehensive classification and discussion of the underwater concrete structure damage repair technologies at the current stage. This technology can be divided into two major types: direct underwater type and dry environment type. Further, the development history of these technologies is systematically sorted out and, combined with practical engineering application cases, the operation processes, applicability, limitations, and economy of these technologies are analyzed. Finally, the challenges and future development trends of the current underwater concrete structure damage repair technology are pointed out, which provides a direction for future research on the intelligent maintenance of underwater concrete structures.

Detection method for underwater dock joints: underwater sonar imaging based on 3D technology

In the detection of surface defects in underwater structures, traditional methods using manual diving are inefficient. Equipment such as underwater high-definition cameras and underwater laser imaging face significant signal attenuation in deep and turbid environments, and the information contained in two-dimensional sonar images is limited, making it difficult to meet accuracy requirements. To address these shortcomings, a detection method based on sonar imaging for underwater docks using three-dimensional (3D) reconstruction is proposed. This method first reduces environmental interference through preprocessing. Then, emit sound waves towards the underwater target and receive the returning signals, which are converted into digital signals. Next, perform 3D modeling and visualization. Finally, a detailed analysis of the 3D images is conducted to identify, analyze, and assess the severity and distribution patterns of defects. The experimental results show that the 3D scanning sonar imaging detection technology can effectively detect targets and accurately identify misalignment in caisson joints, meeting practical application requirements.

A multi-stage method for defect detection of underwater structures based on deep learning

Underwater defect detection faces challenges such as difficulty in image acquisition, low precision in detection and inaccurate defect localization. This article presents a multi-stage method to address these issues. A custom-built remotely operated vehicle (ROV) with advanced path planning was used to collect images of underwater defects. An improved YOLOv8 network, integrating deformable convolution and a multi-head self-attention mechanism, significantly enhanced defect detection accuracy. Furthermore, an upgraded Deeplabv3+ semantic segmentation network with a densely connected atrous spatial pyramid pooling module was proposed for precise pixel-level mapping of defects, particularly elongated ones. A 3D reconstruction method based on structure from motion was developed to generate accurate 3D point clouds for precise defect localization. The experimental results demonstrated that the developed ROV, equipped with a high-resolution camera and a multi-source heterogeneous vision enhancement module, efficiently captured defect images and improved image quality in turbid water. The improved YOLOv8 achieved a 6.61% increase in mAP50, while the upgraded Deeplabv3+ showed a 4.19% increase in mean intersections over union. These enhancements enabled the integrated method to achieve pixel-level defect detection and segmentation, demonstrating significant advancements across all performance metrics and competitive frames per second for real-time applications. The successful visualization of defects in the 3D model validated the effectiveness and feasibility of the proposed multi-stage method.

Two‐step rapid inspection of underwater concrete bridge structures combining sonar, camera, and deep learning

AbstractUnderwater defects in piers pose potential hazards to the safety and durability of river‐crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time‐consuming. This study proposes a two‐step rapid inspection method for underwater concrete bridge piers by combining acoustics and optics. The first step combines macroscopic sonar scanning with an enhanced YOLOv7 to detect and locate piers and defects. Second, the camera approaches the defects for image acquisition, and an enhanced DeepLabv3+ is used for defect identification. The results demonstrate an average mean average precision@0.5 of 95.10% for defect and pier detection, and an mean intersection over union of 0.914 for exposed reinforcement and spalling identification. The method was applied to a real river‐crossing bridge and reduced inspection time by 51.2% compared to traditional methods for assessing a row of 11 piers.

Experimental study on influencing factors of force change of slender submerged body under internal solitary wave

Experimental study on influencing factors of force change of slender submerged body under internal solitary wave

A neural network for the prediction of damage to reinforced cylindrical shells subjected to non-contact underwater explosions

Abstract Explosion tests and numerical simulations are of great significance for the study of submarine and other underwater target damage characteristics. However, the cost of the real ship test is high, the implementation is difficult, and the time cost of the simulation calculation is high, which presents some difficulties in attempting to quickly assess the damage of underwater structures. Currently, the combination of machine learning and numerical simulation has become a more effective means of addressing the aforementioned issues. In this paper, the acoustic-structural arithmetic is employed to realise the non-contact underwater explosion simulation calculation of the reinforced cylindrical shell section. The maximum deflection of the reinforcement bar is extracted as the output parameter from the calculation results. The explosion distance, explosive equivalent, and the thickness of the reinforcement bar are taken as the input parameter. The prediction and analysis are carried out based on the back-propagation neural network algorithm of machine learning. The data generated by the neural network are processed and analysed for the error analysis. The processing and error analysis of the data generated by the neural network revealed that the neural network exhibited a superior prediction effect, establishing an accurate and efficient prediction model for the damage characteristics of underwater exploding cylindrical shells.

Improving safety by fatigue life assessment of pressure hull considering corrosion factors: a review

Abstract Research on determining fatigue endurance of submarine pressure-resistant hulls is crucial for determining their remaining operational lifespan. These studies typically focus on the effects of hydrostatic pressure on fatigue under ideal conditions without taking into account other significant factors. However, the actual condition of pressure hulls is influenced by multiple elements, one of which is operational environmental factors such as corrosion. While current research has examined these factors individually, there is a notable absence of a comprehensive analysis that integrates all of them. Corrosion can reduce structural strength due to the resulting plate thinning effect caused by hydrostatic pressure and low cyclic fatigue. This oversight hampers the development of accurate models for predicting fatigue life and compromises the safety and reliability of underwater structures. This paper addresses the research gap by emphasizing corrosion factors in fatigue life assessments using the low-cyclic fatigue method. By incorporating these elements into the assessment methodology, a more comprehensive understanding of pressure hull fatigue can be achieved, leading to more accurate predictions of pressure hull remaining lifespan.

Research on passive adaptive wall-climbing cleaning and inspection robot of marine cylindrical steel structure based on conical magnetic adsorption wheel

Research on passive adaptive wall-climbing cleaning and inspection robot of marine cylindrical steel structure based on conical magnetic adsorption wheel

Research on the generation and evaluation of bridge defect datasets for underwater environments utilizing CycleGAN networks

Research on the generation and evaluation of bridge defect datasets for underwater environments utilizing CycleGAN networks

A method for detecting defects in reinforced concrete structures of underwater tunnels based on ultrasonic echo signal and CNN

Due to the long-term effects of wind and wave corrosion and hydraulic erosion on underwater structures, various degrees of damage such as cracks, holes, and corrosion may occur. When underwater attachments cause lift off disturbances, various interference signals are introduced, increasing the difficulty of defect detection. In order to maintain the safety and stability of reinforced concrete structures in underwater tunnels in a timely manner, a defect detection method for reinforced concrete structures in underwater tunnels based on ultrasonic echo signals and the CNN is proposed. The collection method and formation mechanism of ultrasonic echo signals for reinforced concrete structures in underwater tunnels are analyzed first. After obtaining the ultrasonic echo signals, noise is removed from the signals through empirical mode decomposition and the sparse table algorithm. An ultrasonic defect detection model for concrete structures based on the multi-attention FasterRCNN structure is constructed, the denoised ultrasonic echo signal is input into the model, the multi-attention mechanism is applied to extract the characteristics of the ultrasonic echo signal, and a balanced feature pyramid network is used to achieve feature fusion. The fused features are generated into defect candidate boxes through regional generation networks, and defect position information and category information are output at the fully connected layer of the model to complete defect detection of reinforced concrete structures in underwater tunnels. The experimental results show that this method can accurately remove noise from the echo signal of reinforced concrete structures and shows high denoising performance. When conducting defect detection, it can quickly detect the defect category and position, and provide the defect depth and diameter. The detection results are accurate and do not show significant errors.

Stability Assessment of Weirs Reinforced with Sheet Piles

Extreme weather events induced by climate change are increasing across the world. In particular, floods resulting from unpredictable heavy rains cause significant damage to property and human life. In Korea, river and underwater structure management is performed continuously in accordance with strict regulations. However, to prepare for unprecedented downpours, the structures of river reservoirs must be reinforced and their stability must be evaluated. This study focuses on the stability evaluation of piping, which represents the main cause of weir collapse. To this end, sheet piles are applied to weirs to evaluate the effects of changes in their length and location on their seepage discharge, seepage velocity, and maximum hydraulic gradient. Additionally, piping stability evaluation of weirs is conducted using sheet piles by evaluating the critical velocity. The results derived in this study are expected to be highly beneficial for reinforcing weirs currently in operation as well as for the design, construction, and operation of future underwater structures.

Enhanced Depth Control and Stability in Submersible Pylon Inspection Robots Using IMU-Based Extended Kalman Filter and PID Control

The research developed a Submersible Pylon Inspection Robot (SPIR) to clean and inspect underwater structures automatically, facing challenges in stability and depth of operation. Integrating the Inertial Measurement Unit (IMU) with the Extended Kalman Filter (EKF), as well as implementing customized PID controls, aims to improve the accuracy and stability of SPIR in dynamic underwater environments. The results show that the SPIR control system can follow the reference depth at shallow depths well, but has difficulty maintaining stability at deeper depths. The position error graph on the Z-axis shows the initial fluctuation that decreases over time, reflecting the increased calibration. The use of drive motors also shows different working patterns, with some motors active in depth settings and others for stabilization. This study shows that the integration approach of IMU, EKF, and PID control can improve SPIR performance, although further adjustments are required to meet the extreme challenges in underwater environments.

Mechanism and controlling factors of mass transport complexes migration: A case study of the mass transport complexes in the taranaki deep water basin, New Zealand

Mechanism and controlling factors of mass transport complexes migration: A case study of the mass transport complexes in the taranaki deep water basin, New Zealand

Influence of temperature on the cathodic polarization behavior and calcareous deposit properties of X65 steel in sea water

PurposeThis paper aims to investigate how cathodic polarization behavior significantly affects the selection of cathodic protection parameters and the effectiveness of protecting underwater metal structures. Factors such as water depth and operating conditions impact seawater temperature, making it crucial to understand the effects of temperature on cathodic protection parameters for underwater pipelines.Design/methodology/approachIn this paper, potentiostatic polarization was carried out by three-electrode method, and morphology, X-ray diffraction and electrochemical analysis.FindingsIt was determined that the stable current densities at the minimum negative potential (−0.8 VSSC) for pipeline steel varied at different temperatures: 7°C, room temperature and 60°C. The cathodic protection potential corresponding to the lowest stable current density was observed to be −1.0 VSSC at 7°C and −0.95 VSSC at room temperature and 60°C.Originality/valueThis study elucidates the mechanisms by which different temperatures affect the protective performance of calcareous deposits and current densities.