A digital twin for management of landslides and slope incidents on strategic road infrastructure

In 2023, Otis strengthened from a slight tropical storm into a major hurricane (Category 5) within only about 12 hours before it made landfall. The storm slammed into Mexico's coast with maximum sustained winds of over 165 mph and hurricane-force winds extending up to 30 miles from its center. The SICT (Secretariat of Infrastructure, Communications and Transportation) warned of a total closure of the Mexico-Acapulco highway in the Chilpancingo-Acapulco section. Faced with reports of hundreds of landslides through the lines, the SICT deployed more than 1000 workers, 100 vehicles and 300 pieces of heavy machinery in the hope of “restoring traffic as soon as possible and providing safety to users.” Unfortunately, predictions could not anticipate close enough the Otis destructive force.

Ensuring the proper functioning of road infrastructure is a fundamental aspect in risk management. Landslides have the potential to impair critical transportation infrastructure, particularly road networks in the hilly regions in Mexico. Recognizing the extremely changing climate conditions in the Mexican Pacific coasts are becoming increasingly difficult to predict, in this research advanced technologies are integrated into an intelligent digital scenario to simulate and control this linear infrastructure before, during and after extreme rainfalls occur.

The strategic roads digital twin comprises i. dynamic susceptibility maps, ii. satellite radar information of control points (the landslides pathologies are easily detected through them), iii. an artificial intelligence slope stability calculator (in near real-time) for pointing incipient instability, and iv. a semi-immersive scenario for analyzing future states based on the information of pluvial stations and control points, once this information is analyzed with the intelligent calculator. For communicate the input conditions, the aggravating factors and the future responses, a digital twin of potentially affected road sections (detected on the dynamic maps) is developed. Simulate scenarios before rainfall increases, help to make informed maintenance and risk prevention decisions in road infrastructure in areas with high geotechnical complexity and strong seasonal rainfall patterns. Exploiting precalculated extremely dangerous conditions, this digital twin can serve as an early warning system because it is programmed for immediate communication of graduated alarms that announce the proximity to dangerous states.