Damage Tracking in Laboratory Reinforced Concrete Bridge Columns Under Reverse-cyclic Loading Using Fusion-based Imaging

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\u27s 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

Multimodal Imaging of Structural Concrete Using Image Fusion and Deep Learning

Concrete structures may be exposed to a variety of loads and environments during their service life. Non-destructive testing (NDT) techniques can be helpful in evaluating the condition of a structure. Imaging provides a visual representation of the interior of concrete and its condition non-destructively. Ground penetrating radar (GPR) and ultrasonic echo array (UEA) using electromagnetic and stress waves, respectively, provide the data that can be used to reconstruct an image. In this PhD dissertation, image reconstruction and fusion algorithms, simulation, and a deep learning model were investigated with the goal to lay the foundation for enhanced imaging applications for concrete. First, a multimodal 2D imaging pipeline is introduced that can process and fuse GPR and UEA data to enhance imaging of concrete. An algorithm, named extended total focusing method (XTFM) was developed that can reconstruct images from the raw signals collected with GPR and UEA instruments. This algorithm combines synthetic aperture focusing technique (SAFT) and total focusing method (TFM) concepts and can reconstruct images from multi-channel arrays with overlapping measurements. In addition, an image fusion algorithm is introduced that fuses GPR and UEA images using a multi-level wavelet decomposition and a NDT knowledge-based fusion rule. A novel local evaluation metric was developed to evaluate the output images of the algorithm. The results from three concrete specimens built in laboratory are reported and it is shown qualitatively, quantitatively, locally and globally that the reconstructed images represent an enhanced precise image of the interior of the concrete. Second, the imaging pipeline was used to track damage progression in two full-scale reinforced concrete bridge column-footing specimens with different lap-splice detailing undergoing reverse-cyclic loading in the laboratory. A quantitative analysis revealed that changes in the images can be tracked as early as the columns undergo some initial damage. In addition, it is shown that changes along the height of column vary, i.e., the lower sections of the column exhibit more damage. This observation is in agreement with the internal force demand distribution of the column. A so-called backwall analysis suggests that the difference in the performance of the two tested columns can be captured using imaging. Finally, GPR simulations and deep learning pipeline was developed that can be used to generate large datasets with different setups to employ deep learning to assist with imaging. A simulated dataset with 3000 data examples of B-scans was generated. A deep neural network model is introduced that can accurately predict two key required parameters for image reconstruction: the dielectric constant of the concrete and the time offset parameter of the GPR measurement. Tuning these two parameters is a cumbersome process usually done manually. Precise prediction of these parameters results in focused images where reflectors such as rebars in concrete, have correct shape and location. It is shown that the developed model can accurately predict these two parameters with an R2 \u3e 0.999. The model was tested on data from the three experimental specimens and resulted in accurate images. The generalizability of the method is also discussed. Gradient visualization is used to highlight which part of an image is utilized most in predictions. It was found that the neural network pays the most attention to the angle of reflections to predict the dielectric constant, and the surface wave portion of GPR for the time offset parameter

Diagnostic Imaging of Structural Concrete Using Ground Penetrating Radar and Ultrasonic Array

Structural concrete is the most widely used construction material in the world. Many structures critical to a society such as bridges, hospitals, and airports are built with concrete. While this material is well understood from a mechanical design point of view, still no accurate quantitative tools exist to assess it for damage and deterioration. This is of particular concern for an urban area like Portland with a mega-thrust earthquake waiting to occur. Non-destructive evaluation tools that can quickly and accurately give a full picture of the integrity of structural concrete elements will be key to help plan effective and safe recovery missions. Ground penetrating radar is a non-destructive testing device which uses electromagnetic waves to assess the interior of the concrete and is especially useful in identifying embedded rebars in the concrete. In addition, ultrasonic array is a tool which uses stress waves and their echoes to image inside the concrete. This technique is most useful where there is a concrete and air interface. In this research, signals from both modalities are obtained from the specimens built in the iSTAR laboratory. Further, the signals are processed to generate a visual representation of the interior of the concrete. Finally, a high resolution image is obtained by data fusion of the two different modalities. The results show a good approximation of the details such as the location of the rebars, air void and the depth of the concrete

A Pipeline for Enhanced Multimodal 2D Imaging of Concrete Structures

We present an imaging pipeline to achieve enhanced images of the interior of concrete from ground penetrating radar (GPR) and ultrasonic echo array (UEA) measurements. This work lays the foundation for an advanced yet practical imaging tool to assess concrete structures. Specifically, we propose an enhanced two-dimensional (2D) total focusing method (XTFM) to reconstruct images from raw GPR and UEA data. The proposed XTFM algorithm integrates total focusing method (TFM) and synthetic aperture focusing technique (SAFT) concepts to post-process large independent and interelement measurements from both modalities in a computationally efficient way. Furthermore, we introduce a novel 2D image fusion algorithm using wavelet multilevel decomposition and an NDT knowledge-based rule to fuse GPR and UEA images. We then compare our algorithm with conventional fusion algorithms such as averaging, maximum, and product. The results from three laboratory concrete reference specimens are evaluated in detail. The fused images are compared with each other as well as benchmarked with the original GPR and UEA images. The output image obtained from our proposed pipeline is an enhanced 2D image of the interior of concrete structures that eases interpretation by a human inspector as well as it has the potential to improve interpretation by computer vision and image analysis algorithms

Ultrasonic Coda Wave Monitoring of Alkali-Silica Reactivity in Concrete Laboratory Prisms

Alkali-silica reaction (ASR) is a deleterious reaction in concrete that leads to the expansion and cracking of concrete. Laboratory approaches to monitor concrete for ASR activity are often lengthy and depend on an operator for regular measurements. The aim of this research is to develop an automated and reliable monitoring approach based on ultrasonic coda (or diffuse) wavefields, which are highly sensitive to minute and slowly occurring changes in a material—ideal for ASR. In this paper, the proposed approach is introduced along with an experimental study that compares ultrasonic coda wave monitoring data with traditional expansion measurements following ASTM C1293. A simple, fast, and robust algorithm to track a selected coda wave feature is proposed and evaluated and was applied to the recorded data. The monitored concrete prisms were designed to have three different levels of ASR activity by varying the lithium admixture dosage. The proposed approach was found to be promising. The process is automated and the monitoring of the specimens using coda wavefields was able to clearly differentiate the mixtures with varying ASR expansions