Intelligent Landslide Early Warning Systems: Application on the Skopje – Veles Road Section

Summary

Abstract

The transition from passive rockfall mitigation to proactive digital monitoring is critical for safeguarding highly vulnerable transport infrastructure under intensifying climate conditions. Along the Pan-European Corridor X near Katlanovo, the Skopje–Veles highway faces chronic hazards from sudden rockfalls due to the highly fractured and weathered nature of its Paleozoic schist formations. This study proposes a conceptual engineering framework for an automated Intelligent Early Warning System (EWS). The perception layer utilizes a spatial grid of ten high-resolution MEMS digital inclinometers optimized in a dual-row configuration to detect real-time slope deformations. Data transmission is sustained via a low-power, long-range LoRaWAN network architecture, while decentralized Edge AI filtering is implemented within the sensor nodes to suppress mechanical traffic vibrations and mitigate false-positive alarms. Kinematic modeling based on the corridor's design speed of 70 km/h and wet pavement friction criteria establishes a critical stopping sight distance of 130 m. Consequently, the system integrates upstream Variable Message Sign (VMS) portals positioned 300 m before the hazard zone to ensure safe vehicle cessation. Furthermore, the operational workflow adopts the international DATEX II standard to enable automated XML data dissemination across national Intelligent Transport Systems, accelerating emergency coordination with road maintenance units and traffic police. The proposed IoT solution offers a highly cost-effective and scalable framework that minimizes capital expenditures compared to massive structural retaining walls. By validating the geomechanical and telecommunication feasibility of the sensor grid, this paper provides a replicable blueprint for smart transport corridors, drastically enhancing regional traffic safety and long-term infrastructure resilience against geohazards.