This study examines the integration of epistemological principles into road infrastructure risk management, emphasizing the need for adaptive strategies in the face of inherent climate uncertainties, particularly flash floods. A systematic review of peer-reviewed articles, industry reports, and case studies from the past two decades was conducted, focusing on the application of epistemological approaches within the infrastructure sector. The research employs a mixed methods approach. Quantitatively, the risk of pavement failure is measured by analyzing the relationship between pavement serviceability rates and Intensity–Duration–Frequency (IDF) data in areas frequently affected by flash floods. For example, rainfall intensities during flood events on the BR-324 highway in Brazil were significantly higher than monthly averages, with maximum values reaching 235.73 mm for a 5 min duration over a 50-year return period. These intensities showed an increase of approximately 15% over 5 to 10 years and 8% over 50 to 75 years. Qualitatively, traditional risk management methods are combined with epistemological concepts. This integrated approach fosters reflective practice, encourages the use of both quantitative and qualitative data, promotes a dynamic management environment, and supports sustainable development goals by aligning risk management with environmental and social sustainability. This study finds that incorporating epistemological insights can lead to more fluid and continuously improving risk management practices in construction, design, and maintenance. It concludes with a call for future research to explore the integration of emerging technologies such as artificial intelligence to further refine these approaches and more effectively manage complexity and uncertainty.

In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays a critical role in ensuring the safety and operational efficiency of facilities. This case study uses a combination of the multi-criteria analysis approach and hydrological studies that use machine learning algorithms to simulate new rainfall events in order to estimate the risk of flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, and land use and land cover, will be weighed using the multicriteria approach. A methodical evaluation of the most vulnerable locations on the railroad network will be possible thanks to the analysis of these parameters based on the geographic information system (GIS) approach. In the meantime, historical precipitation, flow, and hydrological balance data will be used to calibrate and validate hydrological models. The database required for the machine learning model can be created with these hydrological data. The research regions are situated in the densely rail-networked state of Minas Gerais. The geographical and climatic diversity of Minas Gerais makes it the perfect place to test and validate the suggested approaches. The models evaluated included linear regression, random forest, decision tree, and support vector machines. Among the evaluated models, Linear Regression emerged as the best-performing model with an R² value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011).