## 1 Introduction  
The increase in energy demand was driven by economic recovery after the pandemic lockdown and several occasions of extreme weather events [1]. Of various energy sources, natural gas is still the preferred energy source, displaying the highest portion of 36% of total energy production and energy consumption [2]. Natural gas pipelines are a crucial component of the general energy infrastructure as they supply natural gas from upstream sectors to downstream consumers, and the reliability and safety of these pipelines are essential [3]. Currently, many pipelines in the United States (U.S.) are often operated near capacity due to the increase in demand and global population in general. Therefore, they are occasionally subjected to structural or nonstructural failures when this demand coupled with extreme environmental conditions [4]. Understanding the causes of pipeline failures and the impact of the incidents is essential for preventing the occurrence of future incidents [5]. Based on past incidents in 2022, failures of pipeline infrastructure in the United States were commonly caused by corrosion (18%), material/veld/equipment failure (35%), excavation damage (18%), incorrect operation (8%), other outside force damage (5%), natural force damage (3%), and other causes due to harsh climatic and operational conditions [6]. Gas pipeline failures can also potentially result in more severe operator injuries, including fatalities and other substantial economic losses. Thus, pipeline structural health monitoring and modeling are crucial for avoiding these undesirable adverse effects caused by incidents of pipeline failure [7, 8]. Various fields of research aim to enhance pipeline reliability and safety from physical, operational, and environmental perspectives. Efforts to improve physical characteristics involve analyzing material structures and understanding the complex physical phenomena that lead to failure. For example, [PERSON] and [PERSON] (2017) computed natural gas losses from damaged pipelines from a complex physics phenomena approach, using a combination of analytical discharge equations and computational fluid dynamics modeling software [9]. [PERSON] et al. (2011) presented a numerical prediction model for pipeline response to vibration induced by ground activities, such as earthquakes, construction, traffic, explosions, or industrial activities [10], in addition to corrosion, which has been found to be another important cause inducing pipeline failure [11; 12]. The pipeline damage risk is not only caused by physical phenomena such as internal and external corrosion. Since the pipeline infrastructure is spatially extensive, the location and the weather conditions at that location may influence the risk of pipeline damage. [PERSON] and [PERSON] (2018) proposed a quantitative risk-based model to analyze the spatial patterns risk of urban natural gas pipelines [13]. [PERSON] et al. (2016) studied a pipeline network's trend in reliability due to internal and external corrosion using the Homogeneous Poisson Process (HPP) and Non-Homogeneous Poisson Process (NHPP) stochastic models [3]. Their results showed the two most significant failure characteristics were the age in decades of installation and previous failures [3]. With the current demanding pipeline operating conditions and the reliability of most pipeline infrastructure perpetually declining, pipelines require regular maintenance to ensure they are operating safely and efficiently. This can be challenging in remote areas where access is limited or in harsh environments where weather conditions are extreme. Thus, pipeline operation should emphasize seeking a balance between the increasing operational demand, aging infrastructure, and the occasional effects of extreme weather conditions. With sensor advancements, data collection has become more accessible, enabling data-driven analysis with machine learning, artificial intelligence, or big data. This has advanced pipeline analysis with various data sources [7].