Development of Time Domain Inverse Scattering Algorithm for the Detection and Imaging of Buried Objects

• Main Author: Deanne, Edwin
• Format: Thesis
• Language: English; English
• Published: Universiti Malaysia Sarawak (UNIMAS) 2020
• Subjects: QA75 Electronic computers. Computer science; T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/32973/1/Deanne%20%2824%29.pdf; http://ir.unimas.my/id/eprint/32973/4/Deanne%20Anak%20Edwin%20ft.pdf; http://ir.unimas.my/id/eprint/32973/
• Institution: Universiti Malaysia Sarawak

The tremors from the earthquake created heavy damages and cracks to some buildings, infrastructures and caused landslips. Therefore, the post-hazard assessments have to be held to certify the quality and condition of the damaged buildings, infrastructures and lands before continue to use it in the future. Object and crack detection is widely used in structural health monitoring (SHM) application especially in civil structure. There are some of previous methods use for the object and crack detection such as ground penetrating radar (GPR), non-destructive microwave radar and analytical method. Those methods are able to detect the presence of the unknown buried object. However, the information that obtained from the methods is not enough because they are not able to reconstruct the image such as shape and size of the unknown objects. In this research, a new approach is proposed which combines the advantages of both Forward Backward Time Stepping (FBTS) technique and Overset Grid Generation (OGG) method in Finite Difference Time Domain (FDTD) method to develop the efficient numerical method for the image reconstruction in the detection of unknown object and cracks under the soil. Firstly, the accuracy of proposed method is investigated by analysing the measured electric signal or direct problem with empty grids and in stationary case between the proposed method with FBTS technique utilizing FDTD method only. Then, the investigation is furthered to inspect the accuracy of the proposed method by analysing the different kind ratio of grid size between the main mesh and sub mesh. The proposed method is then applied to SHM application focusing on crack and object detection. From the results obtained in Section 4.6.1(a) and 4.6.2(a), it is shown that the proposed method has 5.22% error for object detection; meanwhile the crack detection has 21.55% respectively. Therefore, it is observed that the proposed method can detect and reconstruct the image of the objects and crack clearly because the percentage of relative error is near to the actual value.