Conventional pipeline corrosion assessment methods result in failure pressure predictions that are conservative, especially for pipelines that are subjected to internal pressure and axial compressive stress. Alternatively, numerical methods may be used. However, they are computationally expensive. This paper proposes an analytical equation based on finite element analysis (FEA) for the failure pressure prediction of a high toughness corroded pipeline with a single corrosion defect subjected to internal pressure and axial compressive stress. The equation was developed based on the weights and biases of an Artificial Neural Network (ANN) model trained with failure pressure from finite element analysis (FEA) of a high toughness pipeline for various defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials and was found to be capable of making accurate predictions with a coefficient of determination (R²) of 0.99. An extensive parametric study using the proposed model was subsequently conducted to determine the effects of defect length, defect depth, and axial compressive stress on the failure pressure of a corroded pipe with a single defect. The application of ANN together with FEA has shown promising results in the development of an empirical solution for the failure pressure prediction of pipes with a single corrosion defect subjected to internal pressure and axial compressive stress.

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.

A Finite Element Method (FEM) simulation study is conducted, aiming to scrutinize the sensitivity of Sezawa wave mode in a multilayer AlN/SiO₂/Si Surface Acoustic Wave (SAW) sensor to low concentrations of Volatile Organic Compounds (VOCs), that is, trichloromethane, trichloroethylene, carbon tetrachloride and tetrachloroethene. A Complimentary Metal-Oxide Semiconductor (CMOS) compatible AlN/SiO₂/Si based multilayer SAW resonator structure is taken into account for this purpose. In this study, first, the influence of AlN and SiO₂ layers’ thicknesses over phase velocities and electromechanical coupling coefficients (k²) of two SAW modes (i.e., Rayleigh and Sezawa) is analyzed and the optimal thicknesses of AlN and SiO₂ layers are opted for best propagation characteristics. Next, the study is further extended to analyze the mass loading effect on resonance frequencies of SAW modes by coating a thin Polyisobutylene (PIB) polymer film over the AlN surface. Finally, the sensitivity of the two SAW modes is examined for VOCs. This study concluded that the sensitivity of Sezawa wave mode for 1 ppm of selected volatile organic gases is twice that of the Rayleigh wave mode.

Due to their exceptional structural integrity, steel pipelines are the main component for oil and gas transmission. However, these pipelines are often affected by corrosion, despite corrosion protection, because of harsh working conditions. In addition to corrosion defects, pipelines are often subjected to multiple external loads. The combination of corrosion defects and external loads can significantly reduce the failure pressure, resulting in various failure behaviors. This reduction in failure pressure is especially critical in pipe bends as they are the weakest link in a pipeline. This paper presents an overview of the failure behavior of corroded steel pipe components subjected to internal pressure and axial compressive stress.

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.

Exposure to load and offshore environment degrades the load-bearing capacity of tubular joints, necessitating reinforcement of these joints. Reinforcement is sometimes required for lifespan enhancement or qualification based on new requirements. Available reinforcement techniques include welded rings inside/outside the chord, doubler/collar plate at the brace-chord interface, grout filling, and clamp installation on the joints with/without cement. While these techniques increase the load-bearing capacity of damaged tubular joints, various practical limitations exist. Clamping may require heavy machinery, whereas welding stiffeners involves hot work and may not be permitted sometimes. Fiber-reinforced polymers (FRPs) have immense potential for reinforcing steel structures and are a viable alternative for rehabilitating tubular joints due to their exceptional mechanical and physical characteristics, offering competitive advantages over other methods. FRP reinforcement is becoming more feasible and economical for underwater joints. FRP reinforcement can be either precured, pre-impregnated, or wet layup. Aside from the significance of joint rehabilitation, a document covering the well-known options was lacking. This paper summarizes the advantages and limitations of these reinforcement methods, particularly FRP reinforcement. Possible research directions in FRP reinforcement of tubular joints are also discussed.