This Major Research Instrumentation (MRI) award provides funding for the acquisition of an acoustical measurement system. The acquisition of the measurement system enables a new program for structural health monitoring for environmental changes on Maryland's Eastern Shore. It facilitates research of structural health monitoring using guided acoustic waves that can propagate a long distance in solid materials such as bars, plates and pipes. Specific research objectives include: (1) investigation of a time reversal Lamb wave communication system using steel pipes or bars as communication channels; (2) research of new methods for assessing material stress behaviors based on the Hopkinson' bar testing using strain sensors; and (3) development of novel approaches to extracting baseline free and baseline dependent defect features using integrated signal processing and machine learning techniques. The projects that will be supported by this equipment will enable understanding of the characteristics of the local civil structures and new design of SHM systems specific to the Eastern Shore's coastal environment.
If successful, the acquisition of this equipment will allow the University of Maryland Eastern Shore (UMES) to facilitate a new program to address the significant aging infrastructure problem facing the Eastern Shore area, but more widely the nation. It will enhance the quality of student research and instruction and provide faculty with the necessary tools to pursue aspects of their research which are presently not available to them. Each year, two undergraduate students will be intensely trained to operate this instrument. In addition, laboratory projects utilizing this equipment will be incorporated into several upper level undergraduate courses and senior design capstone projects affecting a minimum of thirty students per year. This instrument will also enhance collaboration with Salisbury University and the Northrop Grumman Technical Service facility, also located on Maryland's Eastern Shore.
Structural health monitoring (SHM) is a systematic methodology for continuously assessing the integrity of structures and identifying their potential structural damages. Guided acoustic waves that can propagate in solid materials such as bars, plates, and pipes have long been used for inspecting structural integrity. With the recent advances of sensor technologies and data processing techniques, guided acoustic wave technologies have sparked renewed interests for structural health monitoring. Unique operating conditions for infrastructures such as offshore wind turbines in coastal areas, combined with recent environmental change on the Eastern Shore of Maryland, demand innovative and customized damage detection and monitoring methodologies and algorithms for this region. This project enabled understanding of the characteristics of the local civil structures and new design of SHM systems specific to the Eastern Shore of Maryland's coastal environment. It also enabled the University of Maryland Eastern Shore (UMES) to develop a new program to address the significant aging infrastructure problem facing the Eastern Shore area and more widely the nation to ensure resilience and sustainability of critical infrastructures. This award enabled acquisition of a National Instruments (NI) acoustic data acquisition unit and various piezoelectric acoustic sensors. With the purchase of the data acquisition system, we were able to conduct experiments to achieve three specific aims and tested our hypotheses. First, we investigated a time reversal Lamb wave communication scheme using steel pipes or bars as communication channels. The proposed time reversal pulse position modulation scheme provided a novel data communication method on large structures in an operating environment that conventional electromagnetic waves or acoustic waves may not be applicable. Second, we studied new methods for assessing material stress behavior based on the Hopkinson Bar test using strain sensors. The stress-strain graphs obtained from the test enabled inferring defects such as cracks when comparing with healthy specimen as a baseline. Third, we developed novel approaches to extracting baseline free and baseline dependent defect features using integrated signal processing and machine learning techniques. The acquisition of this equipment enhanced the quality of student research and curriculum development. It provided faculty with the necessary tools to pursue aspects of their research which were not available to them. Under this project, five undergraduate engineering students were able to conduct acoustic experiments using the acquired instrument for independent research and senior design projects. Laboratory projects utilizing this equipment were incorporated into several upper level undergraduate courses and senior design capstone projects. Research results and discoveries have been published in top-tier journals such as Sensors, IEEE Transactions on Signal Processing, and IEEE Transactions on Image Processing and represented in flagship professional conferences in the field of structural health monitoring.
