Research Navid Hasheminejad

Abstract

Crack resistance and healing are two of the most important mechanical properties of asphalt mixtures. Understanding these properties is essential in the design of cost-effective and sustainable road structures. To develop a fundamental understanding of the complex behavior of asphalt mixtures, research in the micro (bitumen) and meso (mortar) subscales is needed. Despite previous efforts, our fundamental understanding of the healing phenomenon remains limited. In the state-of-the-art, Atomic Force Microscopy (AFM) is used to observe the cracking and healing in the microscale. However, AFM imaging only provides data at a limited amount of experiment stages. Furthermore, cracking and healing are investigated by fatigue or fracture-based tests on larger scales which are extremely time-consuming. Lastly, in the currently available tests, it is often impossible to distinguish actual healing from recoveries due to other reversible phenomena. In this research, several novel methods are proposed to investigate these phenomena in both micro and mesoscales. In microscale, a novel testing procedure will capture the dynamic evolution of the microcracks using a Confocal Laser Scanning Microscope (CLSM). Furthermore, in the mesoscale, a Finite Element Method (FEM) is used to train a Convolutional Neural Network (CNN) capable of assessing cracking and healing in a bituminous mortar using the strain field recorded by Digital Image Correlation (DIC).

Researcher(s)

• Promoter: Vuye Cedric
• Co-promoter: Hasheminejad Navid

Research team(s)

• Sustainable Pavements and Asphalt Research (SuPAR)

Project type(s)

• Research Project

Abstract

The current approach to monitoring road quality is based on manual inspections and is labor intensive and relatively expensive. Hybrid AI for Predictive Road Maintenance (HAIRoad) aims to use (hybrid) AI to map the condition of the road network and make recommendations for road maintenance. An efficient and robust data pipeline will be developed using MLOps tools, which allow easy switching between model development and implementation/production. Three demonstrators will illustrate the feasibility of the approach: one with the Port of Antwerp Bruges and two at the municipal level. The demonstrators will allow to validate both the more technical aspects and the market potential. HAIRoad will deliver several innovations such as automated detection of the road conditions, new indicators for road management, sensor fusion by combining information from multiple sensors, and the application of hybrid-AI where we will incorporate physical models of road degradation into data-driven machine learning models.

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Anwar Ali
• Co-promoter: Daems Walter
• Co-promoter: Hasheminejad Navid
• Co-promoter: Hernando David
• Co-promoter: Steckel Jan
• Co-promoter: Vanlanduit Steve
• Co-promoter: Vuye Cedric

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Investigating the viscoelastic properties of bituminous mortars can lead to a better understanding of the mechanical behavior of asphalt mixtures and therefore, the design and construction of cost-effective and sustainable road structures. Even though bituminous mortar is considered as the missing link between binder and asphalt mixtures and is gaining increased worldwide attention, there are still no effective tests to quantify its viscoelastic behavior. The state-of-the-art methods to determine the properties of these materials are cyclic-loading tests, which are time-consuming and use classical measurement instruments that only provide a global view of the mechanical performance of the whole sample. In this research, novel accelerated testing procedures are proposed that use the full-field vibration response of the samples to estimate the complex modulus and fatigue properties of bituminous mortars. Different optical measurement techniques are used and combined to design and validate these novel methods. These methods will be a big step forward in the road engineering community since the testing time is reduced from hours/days to a few minutes. This offers the possibility to conduct research on more samples and improve the mixture designs. Furthermore, the full-field measurements with the combined optical systems can shed light on some of the highly investigated aspects of asphalt mixtures, such as blending efficiency, self-heating, and the location of microcracks.

Researcher(s)

• Promoter: Vuye Cedric
• Co-promoter: Vanlanduit Steve
• Fellow: Hasheminejad Navid

Research team(s)

• Sustainable Pavements and Asphalt Research (SuPAR)

Project type(s)

• Research Project

Abstract

Bituminous mortar is considered as the missing link between binder and asphalt mixtures and has gained worldwide attention over the past decade. Investigating the mechanical properties of bituminous mortars can lead to a better understanding of the mechanical behavior of asphalt mixtures and therefore, the design and construction of cost-effective and sustainable road structures. The state-of-the-art tests to find the properties of these materials are cyclic-loading tests, which are time-consuming and use classical measurement instruments that only provide a global view on the mechanical properties of the whole sample. In this research, novel accelerated mechanical testing procedures are proposed that use the full-field vibration of the samples to estimate the complex modulus of elasticity, fatigue properties, and healing potential of the investigated bituminous mortars. Different optical measurement techniques are used and combined to design and validate these novel methods. These methods can be a big step forward in the road engineering community since the testing time is reduced from hours/days to a few minutes. This offers the possibility to conduct research on many more samples and improve the mixture designs. Furthermore, the full-field measurements with the combined optical systems can shed light on some of the highly investigated aspects of asphalt mixtures such as blending efficiency, self-heating, or the localized healing of microcracks.

Researcher(s)

• Promoter: Vuye Cedric
• Co-promoter: Vanlanduit Steve
• Fellow: Hasheminejad Navid

Research team(s)

Project type(s)

• Research Project

Abstract

Quantitative values for mechanical properties of materials are required in the simulation of the behavior of structures and systems in several engineering domains: civil engineering (buildings, bridges, roads, …), mechanical engineering (aircraft, cars, …), biomedical engineering (implants, scaffolds, etc.) and electronic engineering (semiconductor materials). In addition, the knowledge of these material properties provides a means to follow-up the health of a structure or system during operation and to estimate the remaining lifetime. The proposed novel hybrid material characterization method combines two distinct approaches to estimate mechanical material parameters, which has never been attempted before. By using laser Doppler vibrometry for the optical measurement of both resonating (at low frequencies) and propagating surface waves (at high frequencies), modal parameters and wave propagation characteristics can be derived simultaneously. By comparing these results with Finite Element and analytical models and by using an inverse modelling approach with intelligent optimization algorithms, it will be possible to identify more material parameters with an improved accuracy in a reduced measuring time. This will allow applications on more complex materials (e.g. layered poro-elastic road surface) in an in-situ environment. The proposed method will lead to several innovations, in the fields of measuring, data processing and optimization, and will be validated in three different applications: asphalt pavements (civil engineering), composite materials (mechanical engineering), and a tympanic membrane and bone material (biomedical engineering).

Researcher(s)

• Promoter: Vuye Cedric
• Co-promoter: Dirckx Joris
• Co-promoter: Hasheminejad Navid
• Co-promoter: Vanlanduit Steve

Research team(s)

Project type(s)

• Research Project