Bridge Health Modeling
Bridges are the national critical infrastructures. However, according to ASCE bridge report card in 2009, more than 26% of the nation’s bridges are either functionally obsolete or structurally deficient. Current bridge management predominantly relies on visual inspection, which cannot uncover the hidden deteriorations. Bridge Health Monitoring and Modeling (BHM) is the innovative approach to assess the bridge health performance. Most of BHM programs are limited to data collection. In this project, Bentley Systems and STRAAM Corporation have jointly conducted the pilot study on the bridge health performance for the approach span of Verrazano Narrows Bridge in New York City. With the detailed bridge design drawings, the finite element model (FEM) was built by using Bentley STAAD Pro. The constructed FEM was employed to optimize the sensor placement of the bridge span. The optimized sensor location helped to determine and finalize the placement of the accelerometers, which were placed on the bridge to record the dynamic responses of the bridge span under normal traffic condition during the day. The recorded responses were processed to extract the primary modes and the frequencies that were used for FEM calibration, which was dramatically facilitated by the optimization runs conducted on a High Performance Computing (HPC) cluster using Darwin Optimization Framework. The calibrated FE model can serve as a valuable knowledge tool for the bridge condition assessment and the solution evaluation of prognostic bridge maintenance.
Published Papers:
Signal Processing for Modal Parameter Identification
Dynamic responses of civil structures under ambient environment and external loading condition, e.g. earthquake, contain the important information of the structural health condition. The responses are usually measured (in short term) or monitored (in long term) by using accelerometers, which record a large amount of raw data of accelerations. The data needs to be processed so that useful information, including natural frequencies and modal shapes, can be extracted for structural health evaluation and modeling. This project developed the algorithms for processing the data to identify dynamic signatures (natural frequency, damping ratio and mode shape) of a civil structure. The developed methods are applied to the UCLA Factor Building, a benchmark of building health monitoring, under both conditions of the ambient vibration and the earthquake vibration. The results obtained are compared with those previously published in literature. It is verified that the data processing framework we developed is effective and generic at modal parameter identification. It serves as a tool for extracting modal information from raw dynamic data for civil structures.
Sensor Placement for Structural Health Monitoring
Structural health monitoring (SHM) is becoming an increasingly important approach for providing good data to come up with technically-sound solutions for life-cycle infrastructure management. SHM can be undertaken for a few hours, so-called short-term testing or measurement and continuously so-called long-term monitoring. Various sensors, wired and wireless, are used for measuring different parameters, such as acceleration, strain, displacement, deflection, corrosion, temperature and humidity etc., which are related to structural health performance. Among all the parameters, acceleration is the most important factor to be used for assessing the integrity of dynamic behavior of the bridge structure (and many other civil structures too). However, determining how many accelerometers are to be placed and where to place them is not straight forward, it usually relies on the engineering judgment. In this project, a sensor placement method is presented and applied to optimize the accelerometer placement. The sensor placement method is formulated to search for the sensor locations, represented as nodes in finite element model, to maximize the coverage of the dynamic integrity, which is defined as percentage of the detectable damage scenarios. Using Mont Carlo method and the finite element model, thousands of damage scenarios are automatically generated and analyzed, the calculated responses are used to form a sensitivity matrix, which is used to evaluate the sensor placement location. For a given number of sensors, the placement solution is optimized by using Darwin optimization framework a generic parallel optimization tool.
Piezoelectric-based Method for Damage Identification