Green Algorithm and Artificial Intelligence for the Sustainability of Civil Infrastructure in Peru (#699)

Authors

Chavarry Vallejos, Carlos Magno

Támara Rodríguez, Joaquín Samuel

Chavarría Reyes, Liliana Janet

Valencia Gutiérrez, Andrés Avelino

Escobar Serrano, Jackeline Carol

Chiok Guerra, Alicia Cristina

Panana Holgado, Elizabeth Clotilde

Abstract

The global climate crisis and the high vulnerability of the Peruvian territory to extreme hydrometeorological events require a structural transformation in civil engineering, a sector where currently only 24% of projects incorporate formal sustainability criteria. The objective of this research was to evaluate the effectiveness of the "Green Algorithm" paradigm and Artificial Intelligence (AI) in optimizing he resilience and environmental performance of national infrastructure. A qualitative-technical methodology based on parametric modeling and resource simulation was used, analyzing the duality Green in AI and Green by AI. The main results highlight a 35% reduction in the energy consumption of computational processes, optimizing training times from 142 to 92 hours. In the architectural field, the optimized designs achieved a 25% saving in the thermal demand of air conditioning and a 39.9% decrease in the light load5555. Regarding materials science, it was possible to reduce the carbon footprint of concrete by 20%, reducing emissions from 320 to 256 kg/m2, while mechanical resistance was increased by 2.3% with a predictive reliability of 94.5%. It is concluded that the Green Algorithm acts as an essential catalyst to achieve the 2030 Agenda, allowing technology to serve society through resilient infrastructure that mitigates environmental impact in contexts of limited resources.