Development of New Algorithm for NDT: Material Characterization of the Pavement Structure Using FWDT
RA: Ako Bahari (Visiting PhD Intern)
Project Duration: 2017 – Present
Collaborators & Partnerships: Dr. Ahmed Shalaby (Department of Civil Engineering & Municipal Infrastructure Chair – UofM)
Financial Support: NSERC ENGAGE & FPrimeC Solutions Inc., Toronto, Canada
Project Description:
Pavement structures reach performance limits when there are multiple deteriorations caused by repeated vehicle loading and environmental effects. Therefore acquiring detailed information about the state of the pavement structures is an important element in rehabilitation planning of sections showing significant damage. NDT Methods have been broadly used for structural health monitoring and low-cost assessment in transportation. Among different NDT approaches, FWD is one of the most popular techniques in the characterization of the mechanical properties of the pavement structure. The FWD test measures the surface deflections caused by dropping a known mass from a specific height, and associated stress wave propagating through the multilayer pavement structure. The surface displacements occur due to the reflected and transmitted waves to the surface. Geophones are used to record these surface deflection time histories in the vertical direction at various distances (up to 1.20 m) from the center of the load-plate. A back-calculation algorithm is applied to inversely determine the mechanical properties of the layers constituting the multilayered flexible pavement system based on acquired surface displacement data. The current practical state-of-the-art of this method is limited to the quasi-static inversion of the maximum measured deflections. However, this approach does not take advantage of the additional information contained in the stress wave time histories such as damping, moisture content, and the effect of porous structure. The accuracy of determining the mechanical properties of the layers would also improve by using a comprehensive dynamic analysis rather than a quasi-static analysis.
The presence of moisture in the pavement structure can significantly affect the performance of the pavement. For example, an increase of 2% in optimal moisture content, would decrease the resilient modulus of the whole structure by as much as 75%. Another important issue to the pavement maintenance is the seasonal factor in cold regions. For example, the resilient modulus in winter is approximately on average 40% higher than the one in early spring (thawing season). The current state-of-the-art in FWD, using a quasi-static technique, cannot account for the presence of moisture or the frost line in the subgrade. Consequently, a more realistic hydro-mechanical dynamic model rather than a quasi-static approach is needed to determine mechanical as well as hydraulic properties such as water content, frost line, porosity, and visco-elastic parameters.
The objective of the proposed research is to develop an algorithm for the material characterization of the pavement structure through a least square-based inversion technique.
Outcomes:
Bahari A., Shalaby A., Maghoul P., 2018. Application of Falling Weight Deflectometer Test for the Material Characterization of the Pavement Structure: A Critical Review, In preparation.
Bahari A., Maghoul P., Shalaby A., 2018. An Improved Algorithm for Viscoelastic Forward Solvers for the Falling Weight Deflectometer Test, International Journal of Solids and Structures (Elsevier), In Preparation.
Bahari A., Shalaby A., Maghoul P., 2018. A study on the effect of poro-visco-elasticity on the mechanical properties of pavement structure determined by Falling Weight Deflectometer Test, 71th Canadian Geotechnical Conference (GeoEdmonton 2018), Edmonton, Canada.
Development of New Algorithm for NDT in Permafrost
RA: Hongwei Liu (PhD Student)
Project Duration: 2018 – Present
Collaborators & Partnerships:
Financial Support:
Project Description:
coming soon …