Predictive Modeling with Artificial Intelligence to Mitigate Variability and Cost Overruns in Andean Public Infrastructure (#864)

Authors

Chavarry Vallejos, Carlos Magno

Támara Rodríguez, Joaquín Samuel

Chavarría Reyes, Liliana Janet

Panana Holgado, Elizabeth Clotilde

Chiok Guerra, Alicia Cristina

Coral Jamanca, Julio Cesar

Solorzano Poma, Jainer Eloy

Abstract

The objective of this research was to propose a framework based on predictive modeling with Artificial Intelligence (AI) to mitigate systemic instability, operational variability and cost overruns in the management of public infrastructure in the Andean region. The study was based on a quantitative-deductive approach methodology and explanatory level, analyzing a representative sample of 88 Peruvian state projects through a non-experimental cross-sectional design. The reliability of the data collection instrument was validated with a Cronbach's alpha coefficient of 0.840, certifying a high internal consistency. The results reveal a critical gap in the sector: 76% of the works ignore sustainability criteria and 88% lack predictive technological tools, which generates a state of critical risk and operational uncertainty. A significant correlation (rs=0.78) was demonstrated between the absence of machine learning models and the uncontrolled variability of costs and deadlines. It is concluded that the integration of AI is imperative to move towards precision engineering that prioritizes environmental protection and climate resilience under the framework of SINAGERD 2050. The main contribution lies in a decision-making support system capable of optimising resources, reducing the carbon footprint and ensuring compliance with international standards (ISO), consolidating technology as the axis of a symbiosis between industrial productivity and the preservation of ecosystems.