Automated Interpretation of Sensor Data for Evaluating In-Situ Conditions
The research described herein proposes and develops a concept for Automated Interpretation of large quantities of Slensory Data (AISD). This concept automatically applies encoded knowledge to the interpretation of the signals generated by sensory devices used to detect the in-situ condition of materials and structures. The knowledge is structured according to the interpretive disciplines required, and the signals are digitally processed into signatures that can be recognized by the knowledge base. The objective of this work has been to demonstrate the feasibility of the AISD concept. Potential applications for the AISD concept were reviewed and the automated analysis of bridge deck deterioration using ground penetrating radar was selected as a model for further study. A knowledge base comprising bridge deck deterioration, bridge engineering, radar/concrete physics, and radar signal analysis was developed, structured, and encoded. Prototype computational tools to automate the application of this knowledge were developed and applied to simulated data. A prototype Intelligent Bridge Deck Analyzer (IBDA), was developed. IBDA accepts raw radar data and user knowledge as input, and produces interpretations of bridge deck conditions at each measurement location. Computational experiments were conducted to demonstrate the influence of the radar data and the user input on the condition interpretation. The results show how the interpretive process can be automated, and how knowledge from various sources can be automatically combined to produce stronger conclusions than any source individually. These findings significantly expand the future potential to develop automated in-situ measurement equipment.
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