A Semantic Digital Twinning Approach for the Management of Road Distress Data

Abstract

This paper addresses the challenge of managing and maintaining road pavements effectively throughout their lifecycle. Emphasizing the need for a cohesive data collection and handling approach, the paper highlights the vulnerability caused by the absence of a structured information system, resulting in reworks, information loss, and misinterpretation of collected data. A unified data handling and collection structure, introducing the concept of a pavement Digital Twin through a standardized data structure is presented. The Digital Twin aims to integrate information regarding pavement failures, with the future aim of predicting deterioration and facilitating informed decision-making in management and maintenance interventions. Complexities in pavement failures, categorized into surface and subsurface modes, prompt the necessity for a reliable classification and representation system. The proposed methodology introduces a grid of cells for surface failures and three-dimensional voxels for subsurface failures, providing a structured approach for data integration throughout the pavement's lifecycle. An approach for the semantic representation of pavement distresses is also presented. The proposed methodology stresses the importance of scalability and flexibility in data storage, forming the basis for a comprehensive Digital Twin of road pavements. Finally, through the use of multiple pavement failure datasets, the methodology is shown to have a high potential in providing a structure for comprehensive management of road data.