Damage tracking in laboratory reinforced concrete bridge columns under reverse-cyclic loading using fusion-based imaging

ABSTRACT

Fusion-based imaging using ground-penetrating radar (GPR) and ultrasonic echo array (UEA) was employed to track damage progression in the columns of two full-scale reinforced concrete (RC) bridge column-footing subassembly laboratory specimens. The specimens had different lap-splice detailing and were subjected to reverse-cyclic lateral loading simulating a subduction zone earthquake. GPR and UEA scans were performed on the east and west faces of the columns at select ductility levels. Reconstructed images were obtained using the extended total focusing method (XTFM) and fused using a wavelet-based technique. Composite images of each column's interior were created by merging the images from both sides. A quantitative analysis based on the structural similarity (SSIM) index accurately captured damage progression. A backwall analysis using the amplitude of the backwall reflector was also performed. Changes as early as in the first measurement (μ = 0.5 displacement ductility level) could be detected. Damage variation along the column height was observed, consistent with greater damage at the base. The proposed analyses distinguished the structural behavior differences between the two specimens. In summary, the SSIM metric provides a valuable tool for detecting changes, while the backwall analysis offers simple yet informative insights into damage progression and distribution in full-scale RC members.

KEYWORDS:

• Earthquake damage
• reinforced concrete bridge column
• ultrasonic echo array
• ground-penetrating radar
• SAFT-based imaging

Acknowledgments

Full-scale experimental testing was carried out in the infraStructure Testing and Applied Research (iSTAR) Laboratory at Portland State University (PSU) and made possible by the Oregon Department of Transportation (ODOT) through sponsorship of their research project SPR 802 [3]. Additional funding to develop the imaging and image fusion algorithms used in this study was provided by a joint-seed grant from the Oregon Health and Science University (OHSU) and PSU. Research assistants at the iSTAR Laboratory, i.e., Patrick McCoy, Ilya Palnikov, Gregory Norton, Bradley Sharpshair, and Yuhang Wei, assisted with manufacturing, installation, and instrumentation of the test specimens, and their help is greatly appreciated.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, TS, upon reasonable request.

Additional information

Funding

The work was supported by the Oregon Department of Transportation [SPR 802]; Oregon Health and Science University and Portland State University [Joint-seed grant].