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The gradual trench formation of steel catenary risers (SCRs) in the touchdown zone is known to significantly affect the SCR's fatigue life. However, there is still no coherent agreement among researchers on the beneficial or detrimental effects of the trench on fatigue. Recent studies have shown that a potential source of contradictory fatigue results could be the methodology to incorporate the trench in the numerical simulations. Since the predefined mathematical trench profiles create non-realistic contact pressure hot spots in the seabed, and the nonlinear hysteretic seabed interaction models may cause premature trench stabilization, both methods distort the damage distribution. To resolve these problems, a new model called the Hybrid Trench Model (HTM) has been developed in this study by combining the linear soil stiffness and nonlinear hysteretic seabed interaction model. This hybrid model provides an equivalent stiffness distribution in the touchdown zone to simulate the trench profile obtained from a nonlinear riser-seabed interaction model. HTM's capability in developing deep trenches, e.g., 5D, was examined along with perfect compatibility with the natural catenary shape of the riser, exhibiting the reliability of this method to incorporate the trench effect into the fatigue analysis. Key words: steel catenary riser, nonlinear soil model, equivalent soil stiffness, trench model, fatigue analysis, 1. Introduction Steel catenary risers (SCRs) are among the most cost-effective systems widely used in offshore oil and gas developments to convey hydrocarbons from the seabed well to the floating [...]

Many experiments and computational techniques have been employed to explain the mechanical properties of frozen soils. Nevertheless, due to the substantial complexity of their responses, modeling the stress–strain characteristics of frozen soils remains challenging. In this study, artificial neural networks (ANNs) were employed for modeling the mechanical behavior of frozen soil, while different testing strategies were carried out. A database covering stress–strain data from frozen sandy soil subjected to varying temperatures and confining pressures, resulting from triaxial tests, was compiled and employed to train the model. Subsequently, different artificial neural networks were trained and developed to estimate the deviatoric stress and volumetric strain, while temperature, axial strain, and confining pressure were considered as the main input variables. Based on the findings, it can be indicated that the models effectively predict the stress–strain behavior of frozen soil with a significant level of accuracy. [ABSTRACT FROM AUTHOR]

Hydrogen production risers (HPRs) connected to floating offshore wind turbines (FOWTs) must be properly configured to minimize both the top-end tension at the hang-off point and the oscillation amplitude in the touchdown zone (TDZ) under environmental loads. One of the best riser configurations to meet these requirements is the lazy-wave configuration, where the riser is lifted midway by buoyancy tanks to create a negative curvature, mitigating the motion dependency of the catenary part and the TDZ. Analytical solutions can be effectively used in riser optimization and configuration studies, where a large number of analyses need to be conducted iteratively. In this paper, an analytical model for HPRs has been developed by combining different approaches for the hanging and touchdown zones to improve the accuracy and continuity of shear force, bending moment, and axial tension distribution along the riser, which are the key parameters governing fatigue damage accumulation in the TDZ. Modified catenary equations were used for the hanging part, and a boundary layer model was implemented in the touchdown zone to model the seabed interaction, preventing stress discontinuity between the two sections. The model was used to assess a case study and compared with numerical simulations to ensure accuracy and viability. The proposed model can be used in daily engineering practice for preliminary investigations and optimization studies of HPRs. [ABSTRACT FROM AUTHOR]

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Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been an increased focus on offshore pipeline safety due to the development of offshore oil and gas resources. Both onshore and offshore pipelines may face significant geological hazards resulting from active faults. Pre-excavated soil can be used as backfill for trenches to prevent major pipeline deformations. Since these backfill materials have been heavily remolded, they are softer than the native soil. Therefore, the difference in shear strength between the backfill and native ground may have an effect on the interaction between the pipeline and the backfill. In this paper, the pipeline–backfill–trench interaction is investigated using a hybrid beam–spring model. The P-Y curves obtained from CEL analysis are incorporated into a 3D beam–spring model to analyze the pipeline's response to lateral strike-slip faults. Additionally, the nonlinearity of pipeline materials is considered to study pipeline failure modes under strike-slip fault movements. A series of parametric studies were conducted to explore the effects of fault intersection angle, pipe diameter, buried depth of the pipe, and soil conditions on the failure modes of buckling pipelines. The developed method can be used to analyze and assess pipeline–backfill–trench interaction when subjected to strike-slip fault displacements. [ABSTRACT FROM AUTHOR]