Technical Approach
Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Task 6
Task 1: Establishing weak point identification maps and conducting baseline field tests
This task involves collecting field data (details given below in subtasks) from the specified baseline test bridge in Maryland. The test will be conducted on a steel bridge led by UMD team. Findings from this task will be included in the report to US DOT.
Subtask 1.1: Analyzing the bridge – The selected baseline test bridge will be first analyzed through finite element analysis. The results of which will be compared with field test data for verification. This task will also attempt to obtain global and local failure maps for weak point identification using the computer (or simulation) models of bridge systems. Inspection data of the bridge will be utilized in order to incorporate the true global and local geometries and conditions into the computer models. Both global and local computational models will be established for qualitative (pass or fail) and quantitative (degree of deterioration) evaluations of bridge system performances.
Subtask 1.2: Sensor placement on the bridge – Based on finite element simulations results from subtask 1.1 and consultation with experts, sensor placement schemes for baseline field tests on the specified bridge will be developed in this subtask. Sensor placement scheme will be optimized based on the balance between wireless sensor cost, wireless sensor power consumption and desired environmental data collection.
Subtask 1.3: Environmental variable data collection – This subtask involves actual field test conducted on the specified baseline test bridge in Maryland. During the field test, the following environmental variable data will be collected: frequency bands of environmental noise, temperature, moisture, solar intensity and wind speed for developing the miniature wind turbine.
Return to top of page
Task 2: Fabrication and characterization of piezo paint AE sensing dot array
The UMD team will lead this research task on transformational development of a flexible and high-fidelity piezo paint acoustic emission (AE) sensor. The piezo paint AE sensor will be optimally tailored for the proposed AE signal based diagnostics algorithm. Development efforts will be made to realize a new version of piezo paint AE sensor that will inherit the low-profile and wideband features of the existing piezo paint AE sensor while extending to relatively large flexible pieces for sensing dot array.
Subtask 2.1: Development of piezo paint sensor with improved sensitivity - This subtask includes paint formulation, micromechanics modeling, fabrication technique for large area (> 4 inches in one dimension) sheets of piezo paint, polarization technique for large area paint, and experimental characterization of paint property (dielectric, elastic and piezo elastic constants) using d33 meter and impedance analyzer. To improve the sensitivity of piezo paint sensor, a 40-dB preamplifier will be developed to boost the AE signal strength.
Subtask 2.2: Development of piezo paint sensing dot array - This subtask involves the design and modeling, prototyping and characterization of piezo paint AE sensor, as well as sensor packaging for long-term durability in the field.
Return to top of page
Task 3: Development of a time-reversal (T-R) method for AE source identification
This task involves the development and verification of the T-R algorithm for detecting, locating, and characterizing the AE source. Laboratory test is planned for validation and debugging the T-R method for fatigue-induced AE source detection and characterization. The NCSU team will be responsible for performing this task that is divided into two subtasks discussed below.
Subtask 3.1: T-R method development through virtual T-R experiments – This subtask involves development of virtual T-R procedure for implementation in the wireless sensor system. Successful development of the interrogation of AE signals using the T-R method will detect, localize, and tracing localized damage with high fidelity. For locating the AE source from multiple sensors, the proposed T-R method will optimally focus back to the AE source and retrieve the actual loading function and amplitude. In this subtask, simulated sensor data from a plate under impact load will be employed for the optimal focusing to verify this approach without knowing the geometry and material properties of the plate.
Subtask 3.2: Lab tests for validating and characterizing T-R method - Laboratory tests on AE source identification will be performed in this subtask before field tests in Task 4.The test structures will be precut and placed on a testing machine under loading representative of the normal operation of bridges selected for field test. The AE source signals will be first received by the high-sensitive commercial AE sensors and physically time-reversed and re-emitted back into the structure. The location of AE source using the T-R technique will be compared with the triangulation method after removing the dispersion.
Return to top of page
Task 4: Development of a wireless smart sensor with a hybrid-mode energy harvester and embedded T-R algorithms
In this task, an integrated wireless smart sensor system including a self-sustained wireless smart sensor, a hybrid-mode energy harvester and embedded diagnosis algorithms will be developed. The NCSU team will perform this task.
Subtask 4.1: Continuous AE listening capability for wireless sensors with piezoelectric paint sensors - A continuous AE listening module will be first developed on the existing wireless sensing platform to extract knowledge from AE signals collected by the piezoelectric paint sensors and enable appropriate interfacing. Verification and performance evaluation tests are planned for such a new design of smart sensor. The existing hardware platform and software codes will then be migrated and extended to the new design. Initial tests will be performed on the piezo paint sensors or other AE sensors to verify dynamic ranges, sensitivity and power consumption.
Subtask 4.2: A hybrid-mode energy harvester with MWTS and solar panel - The proposed hybrid-mode energy harvester will be developed through improving current prototypes simultaneously. The harvester basically serves as a charger for the flexible battery integrated in the smart sensor to provide continuously charging currents and compensate its self-discharging losses. The major part of the proposed hybrid-mode energy harvester is a miniature wind turbine system (MWTS). Due to time-varying wind speed, solar panels molded onto the exterior of the MWTS will also be included as an auxiliary supply to increase energy density and supplement the instantaneous current limit. The hybrid harvester will be designed, optimized, and fabricated in this subtask.
Subtask 4.3: Mathematical cores in FPGA and embedded algorithms - At the final stage, mathematical cores will be integrated into FPGA (Field Programmable Gate Array) in the smart sensor and embedded diagnosis algorithms will be designed, evaluated and optimized in conjunction with the results obtained from the system. The performance of the resulting data will be used for identifying the capability to develop embedded algorithms. Embedded algorithms will then be developed to minimize the amount of data traffic. The instantaneous current consumption and execution time will be monitored and recorded to analyze power profile. Methods to further improve power efficiency will be planned and implemented.
Return to top of page
Task 5: Developing ISHM in both laboratory and field environments and implementation with Bridge Management System
This task involves development and experimental evaluation of a prototype integrated structural health monitoring (ISHM) system in controlled laboratory environment with large scale bridge components and field tests on real bridges. The interplay between key system parameters and system performance will be investigated in the tests. Field testing in a real-world setting provides a comprehensive assessment of the ISHM performance under various environmental interferences and will be conducted in the final phase of the development effort for the ISHM system. The UMD team will lead this task with active participation of the NCSU and URS teams.
Subtask 5.1: Developing ISHM for bridges in controlled lab environment – This subtask involves implementation and performance characterization of the prototype ISHM system on large scale bridge components in the UMD Structures Lab. Piezo paint AE sensors integrated with wireless smart sensor and hybrid-mode energy harvester will be evaluated in lab tests. The test specimens are steel plate components with fatigue details and bolted connections subject to fatigue loading. The primary tasks of lab testing of the ISHM system consist of AE signal recoding denoising, damage localization, wireless data transmission and other data analysis works such as reliability updating and remaining life estimation. Tests will be conducted for both bare and typical three-coat bridge systems. In this subtask, a scaled version of ISHM system will be installed on structural test specimens during lab tests for ISHM system prototyping and validation.
Subtask 5.2: Demonstration of ISHM system for bridges in field environment – This subtask involves intensive investigation of the ISHM system with remote sensing ability in real-world operating conditions. Piezo paint AE sensors integrated with wireless smart sensor and hybrid-mode energy harvester will be evaluated in field tests. Field implementation on bridges includes design of wireless sensor placement scheme, finite element analysis, performing the test, and data analysis including diagnostics of the monitored structural elements. Remote sensing ability of the ISHM system will be realized through a data acquisition system with wireless communication modem, and web-based remote data logging and processing. Field tests will be performed in two full-scale bridge structures including one medium-span steel girder bridge and one long-span steel truss bridges. Factors to consider in field test include possible complication issues such as noise levels, environmental conditions, and wireless sensor placement scheme.
Subtask 5.3: Improving quantitative bridge condition assessment in the Bridge Management System (BMS) –A bridge condition assessment module to be integrated into the ISHM system will be developed in this subtask augment current BMS practice. A strategy to combine remote sensing data and Bayesian updating will be developed which will enable more accurate updating of bridge reliability and remaining useful life and optimal maintenance strategies in BMS.
Return to top of page
Task 6: Project Website, Report and Project Assessment
Subtask 6.1: A project website hosted at the UMD will be established at the onset of the project and kept updated throughout the project duration.
Subtask 6.2: A report summarizing the results of the technical assessment and evaluation will be created and submitted as deliverable of this task. Also will be submitted is a report detailing the results of the economic evaluation of the project. The MDDOT and NCDOT provided in-kind support to implement the technology and make the system ready for commercialization.
Subtask 6.3: Strategy of commercialization, consulted to the TAC, BEST Center industrial partners, Offices of Technology Commercialization (OTC) of UMD and NCSU, and other DOTs, will also be discussed, concluded and established in this task.
Task 1 | Task 2 | Task 3 | Task 4 | Task 5 | Task 6
Top of page
|
