Application of fiber optic sensors to the digitalization and management of airport rigid pavement

To guarantee precise standards of effectiveness, efficiency and safety of an airport, the coordination of land-side and air-side operations is considered pivotal. Consequently, the corresponding interface infrastructure (apron) poses critical challenges in airport engineering due to the specific procedures and regulations required for its design, construction, and management.

Due to the extensive dimensions of an apron and the constant needs of aircrafts’ ground operations, highly efficient and consistent monitoring techniques are required to assist facility managers in both airport expansion projects and maintenance of existing assets. To assess the rigid pavement conditions of an apron, it is recommended to carry out remote sensing and multi-source non-destructive testing (NDT) surveys (generally with ground penetrating radar (GPR), heavy weight deflectometer (HWD) and visual inspections). Then, by a back-calculation process, it is possible to evaluate the resulting shear stress and total cumulative damage factor (CDF) assuming a mix traffic load.

Given the complexity of airport operations and the amount of heterogenous information collected, an interoperable digital twin offers an optimal procedural framework to strategically archive, organize and integrate the sampled parameters measured by each survey. Therefore, a digital informative model of an apron feeds on the outputs of the monitoring techniques to evaluate the degrading conditions of the asset, thus improving the reliability and accuracy of any assessment and decision-making process at land-side/air-side interfaces.

However, such an extensive survey campaign is expensive and impacts greatly on the operability of the airport, often resulting in delay or rescheduling. In addition, stresses are estimated through rational methods, generally a finite element model (FEM) approach that, despite resulting very reliable in simulating the mechanical behaviour of rigid pavement, can be computationally onerous and sometimes not reasonably applicable. Eventually, if the monitoring routines are not frequent and, accordingly, the geometrical and mechanical inputs are non-adequately calibrated, the potential misestimate of the structural health monitoring (SHM) parameters may undermine the effectiveness of any strategic decision taken forward.

In this context, fiber optic sensors have been identified as a possible solution to directly measure the shear stress or strain in rigid pavements. Indeed, the actual stress-strain characteristics can be measured by embedding fiber optic sensors into rigid pavements. Since the mechanical performances are continuously and directly monitored, the dataset is easily updated with the aim of feeding an apron’s digital twin without the need for even scheduling an actual survey. Moreover, it is possible to investigate current and alternative failure mechanisms of rigid pavements (generally associated to underestimation of mechanical loads and environmental factors, inaccurate model-measurement calibration). On the other hand, the application of fiber optic sensors into rigid pavements still stands as an open challenge, due to construction- and management-related issues.

Concluding, this study aims at exploring benefits and open challenges in implementing fiber optic sensors into rigid pavement, by investigating the current state-of-the-art’s installation, embedding, resistance and outputting issues emerged in relevant scientific experiences. Finally, this study puts the basis for the pioneering implementation of a digital twin of apron that is theoretically updated in real time.