Smart Structures Research Laboratory
Research Projects
Condition Monitoring of Dry Storage Canisters
Over 90% of SNF dry cask storage systems in the United States use welded dry storage canisters (DSCs). Typically, these canisters represent the confinement barrier in the dry storage system preventing any release of SNF or radioactive noble gases to the environment. As the dry storage terms of SNF are extended, DSC monitoring issues become more important for safe operation of the dry cask storage systems. The objective of this research program is to develop a technology to enable the next generation of “intelligent spent nuclear fuel (SNF) dry storage canisters (DSCs),” that is, canisters with integrated sensing and processing capabilities to enable real-time state awareness.
Collaborators: Dr. Mitch Pryor (UT Austin), Dr. Blake Anderson (UT Austin), Orano TN, Idaho National Lab
Source of Support: DOE, NRC
Advanced vision-based assessment of infrastructure systems
Commonly, the service performance of reinforced concrete (RC) structures is assessed using some form of manual visual inspection (VI) procedure involving, among other items, the examination of crack lengths, widths, patterns, and orientations. Typically, this member/structure damage information is used in combination with various condition rating guidelines in an effort to quantify or categorize member damage in terms of visually observed cracking. While such guidelines may serve as practical means of cataloguing and monitoring damage in concrete infrastructure, they do not necessarily provide engineers with adequate information to make informed judgements regarding the structural capacity implications of the observed damage, particularly in the case of RC member cracking. In light of these considerations, the ultimate objective of the proposed research is to design and implement a vision-based technology that makes use of new developments in the area of mixed reality (MR) headsets and Artificial Intelligence (AI)-driven data processing techniques for in-situ health assessment of RC structures.
Collaborators: Dr. A. Ferche (UT Austin), Dr. K. Kumar (UT Austin), Dr. O. Bayrak (UT Austin), Screening Eagle Technology, TxDOT
Source of Support: Federal Highway Administration (FHWA)
Embedded, Chipless RFID Sensors for Structural Health Monitoring of Additively Manufactured Parts
The goal of the proposed effort is to develop an additive manufacturing (AM) process for integrating a sensing system into metallic components for structural health monitoring (SHM) applications. Numerous structures of interest to the Department of Defense (DOD), such as aircrafts and helicopters, are made of metallic structures where performance and functionality of those structures is essential. Currently, assessing the performance and safety of these structures relies on periodic visual inspections (VI), and unfortunately even with the recent advances in nondestructive evaluation (NDE) methods, initial indications of structural degradation are often missed. Therefore, there is a pressing need to transition from periodic VI established on the basis of limited-to-no-knowledge of likely damage to an automated, real-time, condition-based inspection method that could be performed on a daily basis. This paradigm shift can be achieved by equipping structures with sensing and analysis systems to enable real-time awareness. To accomplish this goal, we propose to develop a novel additive manufacturing (AM) process for embedding an advanced sensing system in structural components. The sensing system consists of chipless radio frequency identification (RFID) sensors capable of measuring two key state variables for SHM: (1) internal temperature and (2) strain.
Collaborators: Dr. M. Cullinan (UT Austin), Dr. D. Kovar (UT Austin), Dr. J. Beaman (UT Austin), J. Allison (UT Austin), D. Leigh (UT Austin), S. Li (UT Austin)
Source of Support: DARPA
Develop assessment and mitigation guidance for ancillary highway structures
Ancillary structures (AS) exist in a wide variety of applications critical to safety and daily needs of the travelling public (e.g. HMIP, COSS, and traffic signals). The long-term corrosion performance of these structures is of utmost importance to prevent deterioration and extend the structural design life and safety. The goal of this project is to identify and provide critical assessment parameters and guidance for TxDOT to determine if cracks should be monitored, repaired, or the structural component replaced. The proposed research includes a representative assessment of weld cracking in the AS inventory, the development of monitoring hardware and techniques, the development and assessment of repair techniques, and the development of certification methods/standards for inspection personnel.
Collaborators: Dr. T. Helwig (UT Austin), Dr. M. Hebdon (UT Austin), Dr. Stefan Hurlebaus (Texas A&M), Dr. Pete Keating (Texas A&M), Kinsey Skillen (Texas A&M)
Source of Support: Texas Department of Transportation (TxDOT)
Rail Defect Detection by Noncontact Vibration Measurements
In this project we propose a non-contact laser-based vibration measurements instrumentation, for the detection of rail defects in railroad tracks. Specifically, a laser Doppler Vibrometer (LDV) will be adopted in order to record the dynamic vibrations induced by the wheel-rail contact from a moving platform. We hypothesize that the analysis of vibrations, measured in the low frequency range of a rail can be used for flaw detection.
Source of Support: Federal Rail Administration (FRA), BNSF
Enhancing rodent behavioral phenotyping using acoustic waves
Rodents are a cornerstone of neuroscience and physiology research, but the methods for monitoring behavior and physiology in these animals suffer from several key limitations. Monitoring physiological processes such as heart rate, breathing, and muscle activity requires invasive sensors that impede natural behavior of the animals. Similarly, behavioral assays can require highly constraining apparatus. The overarching hypothesis of this project is that these elastic waves can provide valuable information about mouse physiology, behavior, and underlying mental states. Under this proposal we will test the hypothesis that acoustic waves can be used to non-invasively track low-amplitude responses, such as breathing, heart rate, and startle, which currently can only be monitored using invasive or highly constraining apparatus.
Collaborator: Dr. Micheal Drew (Neuroscience Department, UT Austin)
Source of Support: NIH
An Integrated Corrosion Monitoring System for Pipelines
Corrosion is the leading causes of failures pipelines in US. This project aims to design and implement an innovative monitoring system for corrosion-damage assessment of pipelines. The monitoring system is based on permanently installed arrays of low profile piezoelectric transducers to generate and receive helical guided ultrasonic waves throughout the pipeline. Advanced signal processing algorithms based on probabilistic concepts will be developed to perform the critical tasks of: 1) damage localization, and 2) damage characterization.
Source of Support: ExxonMobil, Pipeline & Hazardous Materials Safety Administration
Smart leak-detection in water networks through low-cost Acoustic sensors
This proposal focuses on water loss control by creating new computational techniques for leak-before-break detection using measurements collected from low-cost, non-intrusive, acoustic sensors distributed in the water network. This research will bridge the current gaps in computational intelligence for transforming digital data from acoustic sensors to actionable information for water loss control.
Collaborator: Prof. Polina Sela
Source of Support: ConTex, Austin Water
Integrated Structural Health Monitoring Systems for Navy Structures
The objective of this research is to design, implement and validate a novel probabilistic framework to enhance accuracy and capabilities of wave-based techniques for the structural health monitoring of metallic panels, which are ubiquitous in Navy structures. The uniqueness of the framework resides in the development of a computational method that leverage the large number of reflections to enable damage diagnostics and characterization while using fewer transducers than conventional techniques.
Source of Support: Office of Naval Research (ONR)
Development of a sensing system for detection of underground pipelines
The objective of this project is to develop a technology for the detection underground gas pipelines
Collaborator: Dr. Neal Hall (UT austin)
Source of Support: Chevron Phillips Chem
Evaluation of Structural Cracking in Concrete
The primary objective of this project is to explore the development of concrete shear strength assessment procedures for reinforced concrete bridge members on the basis of visually observed crack information (e.g., crack widths, crack orientations, crack patterns/spacings, etc.).
Collaborators: T. Hrynyk, and O. Bayrak
Source of Support: Texas Department of Transportation
Corrosion Damage Assessment of Prestressed Concrete Structures
The goal of this project is to design and implement an innovative monitoring system for damage assessment of prestressed concrete structures. The monitoring system is based on embedded arrays of piezoelectric transducers that generate and receive guided ultrasonic waves (GUW) throughout the steel strand. Accelerated corrosion tests are currently being conducted and will provide insights on the underlying corrosion mechanisms, including temporal variations in GUW features.
Collaborators: R. Ferron
Source of Support: Texas Department of Transportation, USDOT/UTRC2
A Vision-Based Technique for Damage Assessment of Civil Structures
The objective of this project is to create a vision-based structural health monitoring system for the automatic assessment of damage in reinforced concrete infrastructure. The system is based on the fractal analysis of images taken in the visible spectrum, and is capable of retrieving surface crack patterns that can provide a quantitative measure of damage.
Source of Support: Federal Highway Administration, National Science Foundation
Augmented Reality (AR) for advanced inspections of infrastructure systems
In this project we propose to design, implement, and validate a holistic framework for rapid damage assessment of infrastructure systems. The proposed framework uses augmented-reality (AR) visualization tools to manage in real-time heterogeneous data, and eventually augment real-time damage state awareness. (AR) is a novel disruptive technology, in which the live view of a real-world environment is enhanced by virtual (interactive) overlay techniques.
Post-earthquake assessment of reinforced concrete shear walls
Reinforced concrete shear walls (RCSW) are widely used in conventional building and safety-related nuclear structures. The cracking behavior of these critical structural elements is crucial because of its harmful effects on structural performance such as serviceability and durability requirements. The objective of this project is the development of a structural health monitoring (SHM) system to reduce the life-cycle costs and improve the safety of RCSW.