Full-scale validation of a mechanistic model for asphalt grid reinforcement

ABSTRACT

The pavement engineering community currently lacks an accepted response model that can practically capture and emulate the effects of asphalt grid reinforcement (AGR) products. A candidate model in this context was recently developed in the work of Nielsen et al. (2022), and the main objective of this (current) study was to validate it experimentally. A full-scale test setup was designed and constructed for this purpose; it involved two road sections instrumented with strain gauges and temperature sensors. The sections were nominally identical to each other, except that one included an AGR – installed at the bottom of the asphalt concrete layer. Initially, the as-constructed properties of the two sections were investigated by a combination of field and laboratory tests. Then after, they were loaded by several passes of a heavy vehicle with known weight and dimensions. The experimental campaign targeted slow speeds and relatively high asphalt concrete temperatures for which, according to the model, the AGR effect was expected to be most pronounced. The model was validated by demonstrating its ability to simultaneously reproduce all strain gauge readings; this was achieved in both the unreinforced and reinforced sections for any given vehicle pass. Overall, the investigation provided evidence that: (i) it was possible to observe the AGR effect and quantify the associated model parameters; and (ii) it was necessary to activate the AGR model-component for replicating the sensor measurements in the reinforced section. These findings attest to the potential suitability of the new model as a mechanistic component for asphalt pavement design – especially when including AGR products.

KEYWORDS:

• Model validation
• mechanistic pavement modelling
• asphalt grid reinforcement
• layered elastic theory
• viscoelasticity
• full-scale testing
• pavement instrumentation

Acknowledgments

The authors would like to dedicate a special thanks to Klavs Olsen (S&P Reinforcement Nordic ApS), Zhao Du (Tongji University), Robert Svan (DTU Construct), Troels Kristensen (DTU Construct), and Ian Rasmussen (DTU Construct) for their contribution to this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors would like to thank Innovation Fund Denmark [grant number 9065-00107B] and S&P Reinforcement Nordic ApS for the financial support of the research presented in this paper.