Análisis de la Utilidad de la Inteligencia Artificial para Identificar Daños en la Infraestructura Vial en el Contexto Colombiano

Abstract

This thesis examines the use of artificial intelligence (AI) for damage detection in road infrastructure in Colombia, with the aim of evaluating the effectiveness of AI in the maintenance and management of the country's roads. To achieve this, methods such as literature review, analysis of the Colombian context, comparison of global findings, and cost-benefit analysis with specific strategic recommendations were employed. The results revealed that AI, particularly through neural networks and computer vision technologies, can significantly improve the accuracy and efficiency of damage identification in pavements, overcoming the limitations of traditional methods such as visual inspections. These findings are significant because they indicate that the implementation of AI could transform road management in Colombia, optimizing resources, reducing operational costs, and improving road safety. Finally, it is concluded that the integration of AI in road interventions is feasible and highly promising, especially in the early detection of damage and the optimization of resource allocation. These results have implications for public policy formulation and road infrastructure maintenance in Colombia, highlighting the need for training and technological adaptation, as well as a suitable regulatory framework to ensure the success of this implementation.