Evaluation of Advanced Signal Processing Techniques to Improve Detection and Identification of Embedded Defects
By the end of 1996, 109 Nuclear Power Plants were operating in the United States, producing 22% of the Nation’s electricity [1]. At present, more than two thirds of these power plants are more than 40 years old. The purpose of the U.S. Department of Energy Office of Nuclear Energy’s Light Water Reactor Sustainability (LWRS) Program is to develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the operating lifetimes of nuclear power plants (NPPs) beyond 60 years [2]. The most important safety structures in an NPP are constructed of concrete. The structures generally do not allow for destructive evaluation and access is limited to one side of the concrete element. Therefore, there is a need for techniques and technologies that can assess the internal health of complex, reinforced concrete structures nondestructively. Previously, we documented the challenges associated with Non-Destructive Evaluation (NDE) of thick, reinforced concrete sections and prioritized conceptual designs of specimens that could be fabricated to represent NPP concrete structures [3]. Consequently, a 7 feet tall, by 7 feet wide, by 3 feet and 4-inch-thick concrete specimen was constructed with 2.257-inch-and 1-inch-diameter rebar every 6 to 12 inches. In addition, defects were embedded the specimen to assess the performance of existing and future NDE techniques. The defects were designed to give a mix of realistic and controlled defects for assessment of the necessary measures needed to overcome the challenges with more heavily reinforced concrete structures. Information on the embedded defects is documented in [4]. We also documented the superiority of Frequency Banded Decomposition (FBD) Synthetic Aperture Focusing Technique (SAFT) over conventional SAFT when probing defects under deep concrete cover. Improvements include seeing an intensity corresponding to a defect that is either not visible at all in regular, full frequency content SAFT, or an improvement in contrast over conventional SAFT reconstructed images. This report documents our efforts in four fronts: 1) Comparative study between traditional SAFT and FBD SAFT for concrete specimen with and without Alkali-Silica Reaction (ASR) damage, 2) improvement of our Model-Based Iterative Reconstruction (MBIR) for thick reinforced concrete [5], 3) development of a universal framework for sharing, reconstruction, and visualization of ultrasound NDE datasets, and 4) application of machine learning techniques for automated detection of ASR inside concrete. Our comparative study between FBD and traditional SAFT reconstruction images shows a clear difference between images of ASR and non-ASR specimens. In particular, the left first harmonic shows an increased contrast and sensitivity to ASR damage. For MBIR, we show the superiority of model-based techniques over delay and sum techniques such as SAFT. Improvements include elimination of artifacts caused by direct arrival signals, and increased contrast and Signal to Noise Ratio. For the universal framework, we document a format for data storage based on the HDF5 file format, and also propose a modular Graphic User Interface (GUI) for easy customization of data conversion, reconstruction, and visualization routines. Finally, two techniques for ASR automated detection are presented. The first technique is based on an analysis of the frequency content using Hilbert Transform Indicator (HTI) and the second technique employees Artificial Neural Network (ANN) techniques for training and classification of ultrasound data as ASR or non-ASR damaged classes. The ANN technique shows great potential with classification accuracy above 95%. These approaches are extensible to the detection of additional reinforced, thick concrete defects and damage.