## 1 Introduction  
Pipelines play a critical role in the continuous supply of hydrocarbons from one point to another. They are often subject to harsh and corrosive environments, with corrosion potentially causing pipeline failure and resulting in environmental damage and economic losses. Corrosion reduces pipe thickness, thus weakening pipeline strength, and corrosion defects in pipelines affect cylindrical symmetry, which affects structural efficacy of resisting stress [1]. Stresses from internal pressure and external load often concentrate at the corrosion region, further reducing the remaining strength of a corroded pipeline [2]. Maintaining the integrity of pipelines requires routine inspection and remaining strength assessments. Assessment standards such as ASME B31G, Modified B31G, RSTRENG Effective Area, PCORRC, and DNV RP-F101 are widely used to determine the remaining strength of corroded pipelines. However, they are mostly applicable for corroded pipelines with single defects only subjected to internal pressure. Of all the established corrosion assessment standards, DNV RP-F101 is the most comprehensive, as it can assess the remaining strength of a corroded pipeline with single defect corrosion subjected to internal pressure and longitudinal compressive stress. However, the remaining strength assessment using DNV RP-F101 is conservative due to the assumptions and simplifications made for their safety factors [3]. Pipelines are usually pressurised to transport hydrocarbons, and they experience hoop stress due to internal pressure uniformly acting outward in a circumferential direction. External loadings such as longitudinal stresses are often caused by geological movement and thermal stress [4]. Longitudinal compressive stress often causes buckling and wrinkling failure, thus reducing the remaining strength of corroded pipelines when they are subjected to combined loadings [5; 6; 7]. A corroded pipeline will fail below the remaining strength predicted by DNV when the combined stresses due to internal pressure and external loads exceed ultimate tensile strength of the pipeline. Numerous experimental and numerical studies have been carried out to understand the failure of pipelines subjected to the combined loadings of internal pressure and longitudinal stress. [PERSON] et al. carried out full-scale burst tests on twelve API 5L X52 grade steel pipelines with artificially created corrosion defects as part of the development of DNV RP-F101 assessment standards for corroded pipelines [8]. Their work revealed significant effects of bending moment and axial compressive stress on the remaining strength of a pressurised corroded pipeline, especially for a pipeline with a deep corrosion defect. [PERSON] et al. 's work was supported by findings from [PERSON] et al. 's work on high strength pipelines and [PERSON] and [PERSON]'s studies on corroded X46 steel pipelines [9; 10]. However, burst tests are expensive and time consuming. Numerical methods such as the finite element method (FEM) are widely used to verify and validate the failure behaviour and remaining strength of corroded pipelines [11; 12; 13; 14; 15]. These methods are especially useful for parametric studies with varying corrosion geometries that would otherwise will be too costly to be experimentally carried out. They have proven to be better than the established standards to assess remaining strength of corroded pipeline [12]. However, parametric studies with numerical methods are time and computationally intensive. Alternatively, an artificial neural network (ANN) may be used to predict the remaining strength of corroded pipes with single corrosion defects and interacting corrosion defects based on a data-driven machine learning framework, with the datasets derived from FEA results and full-scale burst tests.