Development of a digital twin of a local road network : a case study

Abstract

Virtual replicas of infrastructure can be used to run simulations and optimize the construction, management, and maintenance of such assets throughout their entire lifecycle. These digital twins (defined as integrated multi-physics, multiscale, and probabilistic simulations of a complex product) mirror the behavior and environmental responses of its corresponding twin. Digital reconstruction techniques using optical sensor technologies and mobile sensor platforms are providing viable, low-cost alternatives to develop digital twins of physical infrastructure. In previous work, the digital twinning of asphalt pavement surfacings using visual simultaneous localization and mapping and the initiation of a digital twin of a local road network were investigated and successfully demonstrated. In this article, the further development of the concept, incorporating road surface temperatures collected over a 1-month period, as well as potential inferences based on these data, in the micro- and macro-twinning of a local road, are discussed. Light detection and ranging, unmanned aerial vehicles, and traffic counting artificial intelligence allows for quantification of the road geometry and infrastructure utilization over large areas (macro-twinning), whereas the photogrammetric reconstruction technique based on a neural network, a proprietary environmental condition sensor (SNOET, or SNiffing Omgewing / Environmental Tester) and commercial temperature sensors were used to acquire the surface texture and environmental conditions respectively (micro-twinning), as well as surface temperatures at four locations and different surfacing materials. The combination of advanced environmental monitoring data, physical data, and surface temperature data provide management data that can assist in the maintenance of such roads. This article expands (with the permission of the conference organizers) on a GeoChina 2021 article through the addition of further temperature data collected on the discussed digital twin, with substantial additional data analysis and discussion.