###### Abstract
These processes lead to the physical and/or chemical aging/degradation of the geomembrane, which will be reflected in a reduction in its mechanical properties [29; 30]. Typically, these degradation mechanisms do not act in isolation; their synergistic effects might accelerate the material's degradation [46]. One prominent degradation mechanism is oxidation, which can be accelerated by thermal or radiation energy, as well as by the presence of transition metals [29; 46]. This process involves the generation of free radicals through reactions with oxygen, leading to the breakdown of polymer chains, changes in molecular structure, and alterations in morphology [29]. As a result, the geomembrane becomes brittle and susceptible to stress cracking, which might negatively influence the efficiency of the geomembrane as a hydraulic barrier [46]. Antioxidants and stabilizers added to the geomembrane formulation aim to \"capture\" these free radicals or reduce active hydroperoxides into inactive alcohols [29]. Some of the commonly used antioxidants include hindered phenols, thiosynergists, and hindered amine stabilizers (HALSs) [43]. In engineering, the application of statistical tools to data analysis and the prediction of the behavior of systems during their service lives is a common practice aimed at enhancing reliability [47; 48; 49; 50; 51; 52; 53]. Various studies indicate variability in the test results in assessing the aging and degradation of geomembranes, such as the high-pressure oxidative induction time (HP-OIT) and SCR [30; 31; 32; 33; 34; 35; 36; 30; 37; 38; 39; 46; 54]. These variations are evident even when considering the same batch of geomembranes, as the data are random variables. Non-parametric statistics allow for variations in and adjustments to the representative curve of the probability density functions (PDFs) to better fit the random-variable data. Given that random variables are influenced by the associated factors, it is advisable to employ probabilistic statistical tools when dealing with these parameters. This approach allows for a deeper understanding of the sample behavior and enables inferences about the population, ultimately enhancing reliability and reducing the inherent calculation uncertainties associated with deterministic analyses. In this context, this article applied a complementary approach to assessing the reliability of geomembranes exposed to weather conditions for an extended period, specifically 7 and 11 years, at a gold mining tailing site in Brazil. The proposed methodology advocates for the application of non-parametric statistics, utilizing a Kernel Density Estimator (KDE). With this methodology, it is possible to obtain a PDF that best represents the behavior evaluated in the material as a function of the chosen variable within the context of the boundary conditions to which the material is subjected. We aimed to investigate the degradation that had occurred in the properties of HDPE geomembranes after 7 and 11 years of exposure to local weathering conditions and provide insights into the HDPE geomembranes' long-term performance. Thus, we also aimed to contribute to reducing the risks and uncertainties inherent in the design process, depending on the environment and function of a given geomembrane, thereby contributing to the execution of resilient projects.