To improve the simulation accuracy of the finite-element (FE) model of an as-built structure, measurement data from the actual structure can be utilized for updating the model parameters, which is termed as FE model updating. During the past few decades, most efforts on FE model updating intend to update the entire structure model altogether, while using measurement data from sensors installed throughout the structure. When applied on large and complex structural models, the typical model updating approaches may fail due to computational challenges and convergence issues. In order to reduce the computational difficulty, this paper studies a decentralized FE model updating approach that intends to update one substructure at a time. The approach divides the entire structure into a substructure (currently being instrumented and updated) and the residual structure. The Craig-Bampton transform is adopted to condense the overall structural model. The optimization objective is formulated to minimize the modal dynamic residuals from the eigenvalue equations in structural dynamics involving natural frequencies and mode shapes. This paper investigates the effects of different substructure locations and sizes on updating performance. A space frame example, which is based on an actual pedestrian bridge on Georgia Tech campus, is used to study the substructure location and size effects. Keywords: substructure
KEYWORDS: Sensors, Control systems, Microcontrollers, Standards development, Analog electronics, Actuators, Feedback control, Control systems design, Computing systems, Wind turbine technology
The introduction of wireless telemetry into the design of monitoring and control systems has been shown to reduce
system costs while simplifying installations. To date, wireless nodes proposed for sensing and actuation in cyberphysical
systems have been designed using microcontrollers with one computational pipeline (i.e., single-core
microcontrollers). While concurrent code execution can be implemented on single-core microcontrollers,
concurrency is emulated by splitting the pipeline’s resources to support multiple threads of code execution. For
many applications, this approach to multi-threading is acceptable in terms of speed and function. However, some
applications such as feedback controls demand deterministic timing of code execution and maximum computational
throughput. For these applications, the adoption of multi-core processor architectures represents one effective
solution. Multi-core microcontrollers have multiple computational pipelines that can execute embedded code in
parallel and can be interrupted independent of one another. In this study, a new wireless platform named Martlet is
introduced with a dual-core microcontroller adopted in its design. The dual-core microcontroller design allows
Martlet to dedicate one core to standard wireless sensor operations while the other core is reserved for embedded
data processing and real-time feedback control law execution. Another distinct feature of Martlet is a standardized
hardware interface that allows specialized daughter boards (termed wing boards) to be interfaced to the Martlet
baseboard. This extensibility opens opportunity to encapsulate specialized sensing and actuation functions in a wing
board without altering the design of Martlet. In addition to describing the design of Martlet, a few example wings
are detailed, along with experiments showing the Martlet’s ability to monitor and control physical systems such as
wind turbines and buildings.
This research studies a substructure model updating approach. Requiring modal testing data from only part of a large structure (i.e. a substructure), finite element model parameters for the substructure can be updated. Prior to updating, Craig-Bampton transform is adopted to condense the entire structural model into the substructure (currently being instrumented and to be updated) and the residual structure. Finite element model of the substructure remains at high resolution, while dynamic behavior of the residual structure is approximated using only a limited number of dominant mode shapes. To update the condensed structural model, physical parameters in the substructure and modal parameters of the residual structure are chosen as optimization variables; minimization of the modal dynamic residual is chosen as the optimization objective. An iterative linearization procedure is adopted for efficiently solving the optimization problem. The proposed substructure model updating approach is validated through numerical simulation with two plane structures
This research investigates the field performance of a mobile sensor network designed for structural health monitoring.
Each mobile sensing node (MSN) is a small magnet-wheeled tetherless robot that carries sensors and autonomously
navigates on a steel structure. A four-node mobile sensor network is deployed for navigating on the top plane of a space
frame bridge. With little human effort, the MSNs navigate to different sections of the steel bridge, attach
accelerometers, and measure structural vibrations at high spatial resolution. Using high-resolution data collected by a
small number of MSNs, detailed modal characteristics of the bridge are identified. A finite element model for the bridge
is constructed according to structural drawings, and updated using modal characteristics extracted from mobile sensing
data.
In order to assess structural safety conditions, many vibration-based damage detection methods have been developed in
recent years. Among these methods, transmissibility function analysis can utilize output data only, and proves to be
effective in damage detection. However, previous research mostly focused on experimental validation of using
transmissibility function for damage detection. Very few studies are devoted to analytically investigating its performance
for damage detection. In this paper, a spring-mass-damper model with multiple degrees-of-freedom is formulated for
further analytical studies on the damage sensitivity of transmissibility functions. The sensitivity of transmissibility
function against structural mass and stiffness change is analytically derived and validated by numerical examples.
To advance wireless structural monitoring systems mature into a reliable substitute to wired structural monitoring
systems, efforts should be paid to investigate their in-field performance on real civil structures, especially complex mega
structures. This study carries out an investigation into a vibration monitoring wireless sensor network (WSN) for modal
identification of a huge cantilever structure. The testbed under study is the New Headquarters of Shenzhen Stock
Exchange (NHSSE). One outstanding feature of NHSSE is its huge floating platform, which is a steel truss structure with
an overall plan dimension of 98x162 m and a total height of 24 m. It overhangs from the main tower 36 m along the long
axis and 22 m along the short axis at a height of 36 m above the ground, making it the largest cantilever structure in the
world. Recognizing the uniqueness of this floating platform, the performance of the WSN for ambient vibration
measurement of this structure is examined. A preliminary two-point simultaneous acceleration measurement using the
WSN is reported in this paper. The preliminary study demonstrates that the WSN is capable of measuring the ambient
vibration and identifying the modal properties of a huge cantilever structure.
Structural health monitoring (SHM) and damage detection have attracted great interest in recent decades, in meeting the
challenges of assessing the safety condition of large-scale civil structures. By wiring remote sensors directly to a
centralized data acquisition system, traditional structural health monitoring systems are usually costly and the installation
is time-consuming. Recent advances in wireless sensing technology have made it feasible for structural health
monitoring; furthermore, the computational core in a wireless sensing unit offers onboard data interrogation. In addition
to wireless sensing, the authors have recently developed a mobile sensing system for providing high spatial resolution
and flexible sensor deployment in structural health monitoring. In this study, transmissibility function analysis is
embedded in the mobile sensing node to perform onboard and in-network structural damage detection. The system
implementation is validated using a laboratory 2D steel portal frame. Simulated damage is applied to the frame
structure, and the damage is successfully identified by two mobile sensing nodes that autonomously navigate through the
structure.
Wireless sensing has been widely explored in recent years for structural monitoring and dynamic testing. The
limitations of current wireless sensor networks have been identified with regard to limited power supply, communication
bandwidth, communication range, computing power, etc. The cost of most wireless structural sensors is still prohibitive
for dense instrumentation on large civil structures. To address the above challenges, this research proposes a new
methodology for structural health monitoring based upon mobile sensor networks. In this research, prototype mobile
sensing nodes have been developed using magnet-wheeled cars as the sensor carriers. These mobile sensing nodes can
maneuver upon structures built with ferromagnetic materials. Performance of the prototype mobile sensing system has
been validated on a laboratory steel frame. Modal analysis for the frame structure is conducted using the data collected
by the mobile sensing nodes. This exploratory work illustrates the flexible spatial resolutions offered by mobile sensors,
which represent a transformative change from the fixed spatial resolution provided by traditional static sensors.
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