Enhancing Risk Management in Road Infrastructure Facing Flash Floods through Epistemological Approaches
Autor(en): |
Victor Andre Ariza Flores
Fernanda Oliveira de Sousa Sandra Oda |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 2 Juli 2024, n. 7, v. 14 |
Seite(n): | 1931 |
DOI: | 10.3390/buildings14071931 |
Abstrakt: |
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. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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01.09.2024