Short synopsis:
Bridges are key elements of the road infrastructures; and, they have proven to be very susceptible to deterioration and collapse because of ineffective and, in some cases, inappropriate inspection techniques. The main difficulty with bridges collapses is related to factors such as scouring, corrosion, fatigue and deterioration of materials. In the search of novel inspection and resilience assessment of bridges, this project aims in developing a pipeline for inspection and resilience assessment of bridges based on the monitoring, diagnosis and prognosis of damage via the use of UAVs and sensor data. Further, we assess the applicability of state-of-the-art deep/machine learning techniques (such as Convolutional Neural Networks) for the automatic per-pixel segmentation of cracks on the structure surface.
Main objectives of the project:
Objective 1:
- Developing a pipeline for inspection and resilience assessment of bridges based on UAV and sensor data
Objective 2:
- Establish a digital (asset) information system capable of integrating within a bridge management system.
Objective 3:
- Establish dynamic monitoring and condition evaluation methods for bridge performance and resilience analysis
Contact info:
PhD student:
Mostafa Aliyari <mostafa.aliyari@hiof.no>
Main supervisor:
Yonas Zewdu Ayele <yonas.z.ayele@hiof.no>