High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed.

Effective quality assurance and quality control inspections of new roads as well as assessment of remaining service-life of existing assets is taking priority nowadays. Within this context, use of ground penetrating radar (GPR) is well-established in the field, although standards for a correct management of datasets collected on roads are still missing. This paper reports a signal processing method for data acquired on flexible pavements using GPR. To demonstrate the viability of the method, a dataset collected on a real-life flexible pavement was used for processing purposes. An overview of the use of non-destructive testing (NDT) methods in the field, including GPR, is first given. A multi-stage method is then presented including: (i) raw signal correction; (ii) removal of lower frequency harmonics; (iii) removal of antenna ringing; (iv) signal gain; and (v) band-pass filtering. Use of special processing steps such as vertical resolution enhancement, migration and time-to-depth conversion are finally discussed. Key considerations about the effects of each step are given by way of comparison between processed and unprocessed radargrams. Results have proven the viability of the proposed method and provided recommendations on use of specific processing stages depending on survey requirements and quality of the raw dataset.