Mr. Koosha Raisi Profile

Koosha Raisi

Doctoral Research Assistant at Univ of Massachusetts Lowell

SPIE Involvement:

Author

Area of Expertise:

NDT/E , GPR , Structural Health Monitoring (SHM) , SAR

Koosha Raisi is a doctoral research assistant in Structural Engineering at the University of Massachusetts Lowell. Before this position, he acquired his bachelor’s degree from Sharif University of Technology (SUT) and received his master’s degree in Structural Engineering at the University of Birmingham with a distinction. A member of the Structural Engineering Research Group (SERG), his current research revolves around nondestructive testing and evaluation of civil engineering infrastructures such as highways and bridges, with more emphasis on ground penetrating radar (GPR), and synthetic aperture radar (SAR). In addition, his other research interests include structural dynamics, sustainable concrete, 3D printing of structural elements, finite element analysis, and applications of artificial neural networks (ANNs) in structural engineering.

Proceedings Article | 18 April 2023 Presentation + Paper

Denoising of GPR B-scan images using discrete wavelet transform

Tzuyang Yu, Koosha Raisi, Ritham Batchu

Proceedings Volume 12487, 1248703 (2023) https://doi.org/10.1117/12.2657741

KEYWORDS: Wavelets, Denoising, Discrete wavelet transforms, Inspection, Synthetic aperture radar, Image restoration, Transportation, Ground penetrating radar

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Subsurface inspection of concrete structures using electromagnetic (EM) sensors such as ground penetrating radar (GPR) and synthetic aperture radar (SAR) is a field applicable approach for critical civil infrastructure systems. Compared to other nondestructive inspection/evaluation/testing techniques, EM waves can penetrate the surface of concrete structures and travel inside the subsurface of concrete structures to generate backscattering signals from a subsurface target (e.g., corroded steel rebar, delamination/cracking) for condition assessment. However, variations in the EM property of concrete and unpredictable EM background noises can contribute to the difficulties of image interpretation. Denoising of radar images is a necessary step before engineers can perform quantitative assessment of the images. The objective of this paper is to denoise GPR images using discrete wavelet transform (DWT). Four concrete panel specimens (30-by-30-by-3.5 cm3 ) were prepared with three artificial cracks (CNC, CNCD, and CNCW) of known dimensions and subjected to B-scan inspection using a 1.6 GHz GPR sensor (StructureScan Mini, GSSI). Level five Daubechies wavelet was used in processing all GPR B-scan images for its capability of detecting high frequency components in this study. The purpose of image denoising was to reveal a clear hyperbolic pattern by eliminating undesired local maximum and minimum points. After each image processing, horizontal, vertical, and diagonal details were generated. Four different denoising schemes were considered; i) without all details, ii) horizontal detail only, iii) vertical detail only, and iv) diagonal detail only. Denoising was repeated in five steps in each scheme. Performance of denoising was evaluated by the number of local maximum and minimum points and their geometric pattern. From our results, it was found that, for some type of artificial crack (CNCD), the hyperbolic pattern can be clearly revealed after one step of denoising, regardless of the denoising scheme. Among four denoising schemes, the best scheme is the one with the approximation coefficients and the diagonal detail coefficients. It was also found that including horizontal detail can introduce high frequency artifacts, resulting in an over-denoised image.

Proceedings Article | 18 April 2023 Presentation + Paper

Detection of steel rebar corrosion in bridge piers using 1.6GHz ground penetrating radar

Koosha Raisi, Ritham Batchu, Tzuyang Yu

Proceedings Volume 12487, 124870X (2023) https://doi.org/10.1117/12.2657731

KEYWORDS: Corrosion, Reflection, Dielectrics, Bridges, Ground penetrating radar, Phase velocity

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Corrosion of steel reinforcement in concrete highway bridges due to carbonation and chloride attack is an ongoing and prevalent problem. If left undetected in time, late-stage corrosion can lead to section loss of steel rebars, an uneven distribution of internal stresses, and ultimately lead to surface cracks and spalling of the concrete. Nondestructive testing and evaluation (NDT/E) sensors like ground penetrating radar (GPR) are commonly used to detect subsurface objects and anomalies (e.g., concrete cracking, and rebars). In this paper, the feasibility of a 1.6GHz GPR device for detection of steel rebar corrosion is tested in both laboratory reinforced concrete (RC) specimens and in-situ RC structures. For this purpose, three RC specimens (12 x 12 x 5 in³ ) were cast with a No.5 steel rebar (5/8” diameter) at the center of each specimen. Two of the RC panels were corroded using the accelerated corrosion test (ACT) to achieve different levels of corrosion, while the third one was left un-corroded to serve as an intact baseline in our measurements. The RC specimens were kept in a temperature-controlled environment (73 ∼ 77◦F) for six years (2017-2023). In addition, a bridge column of a RC highway bridge underpass (Chelmsford, MA), which exhibits the signs of steel rebar corrosion, was selected for collecting in-situ GPR scans. A known intact RC bridge column was chosen and scanned to serve as the baseline for comparison. In both scenarios, B-scan images were developed from the lab RC specimens and the damaged bridge column. The changes in the reflection amplitudes of the hyperbolic-shaped rebar reflections in GPR B-scan images due to corrosion were studied in both the time and the frequency domains, and cross-validated with the results from previous research. From our experiments, it was found that a 1.6GHz GPR sensor can successfully detect and distinguish corrosion level in three RC specimens and one bridge column by the combined use of 1D and 2D analytic methods.

Proceedings Article | 18 April 2023 Paper

Structural health monitoring of a train model under traffic loading

Ritham Batchu, Koosha Raisi, TzuYang Yu

Proceedings Volume 12487, 124870T (2023) https://doi.org/10.1117/12.2658173

KEYWORDS: Sensors, Bridges, Spectral response, Structural health monitoring, Education and training, Transportation, Frequency response

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Proceedings Article | 18 April 2022 Paper

Application of dual-frequency GPR for subsurface void detection in culverts

Koosha Raisi, Nimun Nak Khun, Tzuyang Yu

Proceedings Volume 12047, 120470Q (2022) https://doi.org/10.1117/12.2613083

KEYWORDS: Dielectrics, Radar, Reflection, Metals, Inspection, Roads, Image processing, Signal detection

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