Navid Hasheminejad

Supervisors: Wim Van den bergh & Navid Hasheminejad​

In this project, we aim to collect and verify reliable deformation data of asphalt pavements by using Fiber Bragg Grating sensors. The project is in collaboration with Port of Antwerp and industrial partners. There are several test tracks in the Port of Antwerp and on campus providing FBG-installations in the asphalt pavement structure.

Objectives of the Phd:

    1-      Estimation of the mechanical properties of asphalt mixtures (in the laboratory and in-situ) using Fiber Bragg technology

    2-      Validation of the estimated mechanical properties by simulations and other experimental techniques.

    3-      Long term monitoring of asphalt mixtures (including the effect of ageing and healing) by improving the existing models and use of machine learning.

Work Packages:

Four Work Packages (WP) are defined in this project that are done after the literature survey:

WP 1. Laboratory tests on homogenous material

The first task in this project is to employ FBGs on a homogenous material and conduct laboratory experiments (such as four-point bending) to get familiar with the procedure of implementing FBGs and analyzing the signal recorded by the interrogator. State-of-the-art algorithms (such as maximum detection algorithm or phase correlation algorithm) are used to detect the peaks of the measurements and determine the strain distribution in the material. This strain is validated using a Finite Element Model or analytical formulas.

WP 2. Laboratory tests on asphalt mixtures and development of strain calculation algorithm

Asphalt mixtures are heterogeneous materials with viscoelastic behavior at low levels of strain. Therefore, the spectrum measured by the FBG sensors integrated into the asphalt mixture will be distorted and possibly more difficult to analyze. At this step, the technique used in WP 1 is employed and further developed to overcome this issue. Finally, the calculated strains are validated using other experimental techniques and simulations.

WP 3. In-situ material characterization and validation

We currently have several roads with FBG sensors implemented in them. At this step, we will use a Falling Weight Deflectometer (FWD) to apply impact to the road and develop a back-calculation technique to estimate the mechanical properties of the asphalt mixture using the vibration measurements from FBG sensors. Simple models and assumptions are initially used the estimated road properties and validated using the results from FWD. Finally, more advanced models to simulate the complex viscoelastic behavior of the road are considered to estimate the properties of the pavement.

WP 4. Long-term monitoring

Long-term monitoring of the test tracks is done with two objectives. The first one is to develop a  complex model that includes ageing, cracking and healing (effect of time and temperature variations) in pavement simulation. And the second objective is to train a neural network that can predict the damage in the pavement.

You can apply for this position until March 1, 2022 via this link.