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Currently, in the oil and gas industry, finite element method (FEM)-based commercial software (such as ANSYS and abaqus) is commonly employed for determining the stress intensity factor (SIF). In their earlier work, the authors proposed an adaptive Gaussian process regression model (AGPRM) for the SIF prediction of a crack propagating in topside piping, as an inexpensive alternative to FEM. This paper is the continuation of the earlier work, as it focuses on the experimental validation of the proposed AGPRM. For validation purposes, the values of SIF obtained from experiments available in the literature are used. The experimental validation of AGPRM also consists of the comparison of the prediction accuracy of AGPRM and FEM relative to the experimentally derived SIF values. Five metrics, namely, root-mean-square error (RMSE), average absolute error (AAE), mean absolute percentage error (MAPE), maximum absolute error (MAE), and coefficient of determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of AGPRM is comparable with and even higher than FEM, provided the training points of AGPRM are chosen aptly. Good prediction accuracy coupled with less time consumption favors AGPRM as an alternative to FEM for SIF prediction.

Vibration induced fatigue (VIF) failure of topside piping is one of the most common causes of the hydrocarbon release on offshore oil and gas platforms operating in the North Sea region. An effective inspection plan for the identification of fatigue critical piping locations has the potential to minimize the hydrocarbon release. One of the primary challenges in preparation of inspection program for offshore piping is to identify the fatigue critical piping locations. At present, the three-staged risk assessment process (RAP) given in the Energy Institute (EI) guidelines is used by inspection engineers to determine the likelihood of failure (LoF) of process piping due to VIF. Since the RAP is afflicted by certain drawbacks, this paper presents an alternative risk assessment approach (RAA) to RAP for identification and prioritization of fatigue critical piping locations. The proposed RAA consists of two stages. The first stage involves a qualitative risk assessment using fuzzy-analytical hierarchy process (FAHP) methodology to identify fatigue critical systems (and the most dominant excitation mechanism) and is briefly discussed in the paper. The fatigue critical system identified during stage 1 of RAA undergoes further assessment in the second stage of the RAA. This stage employs a fuzzy-logic method to determine the LoF of the mainline piping. The outcome of the proposed RAA is the categorization of mainline piping, into high, medium, or low risk grouping. The mainline piping in the high-risk category is thereby prioritized for inspection. An illustrative case study demonstrating the usability of the proposed RAA is presented.

This paper examines the applicability of the different surrogate-models (SMs) to predict the stress intensity factor (SIF) of a crack propagating in topside piping, as an inexpensive alternative to the finite element methods (FEM). Six different SMs, namely, multilinear regression (MLR), polynomial regression (PR) of order two, three, and four (with interaction), Gaussian process regression (GPR), neural networks (NN), relevance vector regression (RVR), and support vector regression (SVR) have been tested. Seventy data points (consisting of load (L), crack depth (a), half crack length (c) and SIF values obtained by FEM) are used to train the aforementioned SMs, while 30 data points are used for testing. In order to compare the accuracy of the SMs, four metrics, namely, root-mean-square error (RMSE), average absolute error (AAE), maximum absolute error (MAE), and coefficient of determination (R2) are used. A case study illustrating the comparison of the prediction capability of various SMs is presented. python and matlab are used to train and test the SMs. Although PR emerged as the best fit, GPR was selected as the best SM for SIF determination due to its capability of calculating the uncertainty related to the prediction values. The aforementioned uncertainty representation is quite valuable, as it is used to adaptively train the GPR model (GPRM), which further improves its prediction accuracy and makes it an accurate, faster, and alternative method to FEM for predicting SIF.

Although novel techniques and high performance material open the way for generation of high-performance welded joints, the welding operations are inherently quite complex and expensive. This has been the case, especially for super duplex stainless steel (SDSS) welding. If inappropriate parameter combinations are selected, then the welding process degrades the strength and corrosion resistance because of an unbalanced ferrite/austenite content in an SDSS welded joint. Therefore, it is vital to determine the optimal combination of parameters such as welding process, rate of shielding gas, heat input, and weld geometry. In addition, the optimal combination of parameter levels plays an important role in maintaining the microstructural and mechanical properties in the weld's metal region. This paper illustrates an expert knowledge based methodology for designing the optimal parameter combination, using an engineering robust design approach (ERDA) and related experimentation results. The experiments were performed to investigate the effect of welding factors (i.e., gap geometry, different welding techniques, material transfer and welding processes) on the material properties in the weld and heat affected zone (HAZ). The optimal parameter combination, results of the verification experiment, and the metallographic examination results of selected regions of the butt welded joints are presented.

Technology qualification (TQ) centers on establishing an acceptable level of confidence in innovative aspects of new technologies that are not addressed by the normative standards and/or common certification procedures. Risk-based technology qualification aims to minimize the uncertainty and risk of potential failures in novel designs, concepts, or applications that are not covered by existing standards, industry codes, and/or best practices. The degree of success in a technology qualification process (TQP) depends on its potential for minimizing the uncertainty of a novel technology under assessment and the level of uncertainty arising from the qualification methods and basis. Due to the lack of generic reliability data, focused research and development, and in-service experience, it is necessary to employ risk-based qualification of new technology. In a risk-based TQ, the technology under consideration is decomposed into manageable elements to assess those that involve aspects of new technology and to identify the key challenges and uncertainties. The aforementioned requires risk ranking with the support of experts, who represent relevant technical disciplines and field experience in design, fabrication, installation, inspection, maintenance, and operation. Hence, it is vital to have a comprehensive approach for ranking the risk of potential failures in a TQP, especially to reduce the variability present in the risk ranking and the overall uncertainty. This paper proposes a fuzzy logic based approach, which enables the variability present in the risk ranking of a TQP to be minimized. It also demonstrates how to make risk rankings by means of an illustrative case.

Underwater friction stud welding (FRSW) without a shroud around the stud requires controlling welding parameters to achieve optimal joint strength. A hydraulic FRSW unit has been designed and fabricated to perform FRSW without a shroud via a remotely operated underwater vehicle (ROV). An experimental study has been initiated to investigate the optimal FRSW parameter combinations and corresponding values leading to optimal ultimate tensile load capacity (UTLC) values in the welded joint. The engineering robust design (ERD) approach has been deployed to run the experiment. Each weld has been made with a S355 M12 stud on S355 structural steel plate. This paper discusses the experimental approach, results and conclusions reached in the parameter evaluation, estimation, and experimentation.