Adaptive Structures for Structural Health Monitoring

Inman, Daniel J.; Grisso, Benjamin L.

doi:10.1002/9780470512067.ch1

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…However, it is not clear whether this assumption is correct or not. Thus, a rigorous modeling of the structure along with field tests are needed to assess the actual state of the structure at present, as well as to develop a basis for future assessments with structural health monitoring techniques (Inman et al, 2005;Inman and Grisso, 2007). EC 34,3 3.…”

Section: Description Of the Montoro Wooden Footbridgementioning

confidence: 99%

Multi-scale model updating of a timber footbridge using experimental vibration data

Castro-Triguero

García‐Macías

Flores

et al. 2017

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Purpose The purpose of this paper is to capture the actual structural behavior of the longest timber footbridge in Spain by means of a multi-scale model updating approach in conjunction with ambient vibration tests. Design/methodology/approach In a first stage, a numerical pre-test analysis of the full bridge is performed, using standard beam-type finite elements with isotropic material properties. This approach offers a first structural model in which optimal sensor placement (OSP) methodologies are applied to improve the system identification process. In particular, the effective independence (EFI) method is used to determine the optimal locations of a set of sensors. Ambient vibration tests are conducted to determine experimentally the modal characteristics of the structure. The identified modal parameters are compared with those values obtained from this preliminary model. To improve the accuracy of the numerical predictions, the material response is modeled by means of a homogenization-based multi-scale computational approach. In a second stage, the structure is modeled by means of three-dimensional solid elements with the above material definition, capturing realistically the full orthotropic mechanical properties of wood. A genetic algorithm (GA) technique is adopted to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally. Findings An overall good agreement is found between the results of the updated numerical simulations and the corresponding experimental measurements. The longitudinal and transverse Young's moduli, sliding and rolling shear moduli, density and natural frequencies are computed by the present approach. The obtained results reveal the potential predictive capabilities of the present GA/multi-scale/experimental approach to capture accurately the actual behavior of complex materials and structures. Originality/value The uniqueness and importance of this structure leads to an intensive study of its structural behavior. Ambient vibration tests are carried out under environmental excitation. Extraction of modal parameters is obtained from output-only experimental data. The EFI methodology is applied for the OSP on a large-scale structure. Information coming from several length scales, from sub-micrometer dimensions to macroscopic scales, is included in the material definition. The strong differences found between the stiffness along the longitudinal and transverse directions of wood lumbers are incorporated in the structural model. A multi-scale model updating approach is carried out by means of a GA technique to calibrate the micromechanical parameters which are either not well-known or susceptible to considerable variations when measured experimentally.

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Section: Description Of the Montoro Wooden Footbridgementioning

confidence: 99%

Multi-scale model updating of a timber footbridge using experimental vibration data

Castro-Triguero

García‐Macías

Flores

et al. 2017

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“…Such autonomic functions can be achieved by incorporating distributed network of sensors, actuators, functionalized fibres for sensing/actuation, processors, and memories to mimic the neurological network present in biological systems [10,11]. As an example of reconfiguration, consider what a trained human does in response to a fall.…”

Section: A Brief Historymentioning

confidence: 99%

“…The vision is further illustrated by 'self-sensing' and 'reconfigurable' structures that monitor their own structural integrity and recognize and respond to exogenous threats or the changes of environment in autonomous manner. Such autonomic functions can be achieved by incorporating distributed network of sensors, actuators, functionalized fibres for sensing/actuation, processors, and memories to mimic the neurological network present in biological systems [10,11]. As an example of reconfiguration, consider what a trained human does in response to a fall.…”

Section: The Integration Of Energy Harvest Capabilities Intomentioning

confidence: 99%

Multifunctional materials and structures for autonomic systems

Lee

Inman

2009

Proceedings of the Institution of Mechanical Engineers, Part I:

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The authors' vision of the way forward in multifunctional materials and structures is presented. Their opinion reflects the growth of research activities in the subject area evolved from those of smart materials and adaptive structures. Their ultimate goal is to create the knowledge to enable a new concept of multifunctional materials and structures for autonomic systems, which captures a deeper level of integration of functions between control, transduction, and structure.

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“…Referring to the SHM method listed in [5], the power requirements for autonomous sensing are shrinking due to increased research in chip based SHM computing. Here, some sample values of required power are presented.…”

Section: Power Requirements Of An Autonomous Shm Sensormentioning

confidence: 99%

Energy Harvesting for Autonomous Sensing

Inman

Farmer

Grisso

2007

KEM

Self Cite

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Autonomous, wireless structural health monitoring is one of the key goals of the damage monitoring industry. One of the main roadblocks to achieving autonomous sensing is removing all wiring to and from the sensor. Removing external connections requires that the sensor have its own power source in order to be able to broadcast/telemetry information. Furthermore if the sensor is to be autonomous in any way, it must contain some sort of computing and requires additional power to run computational algorithms. The obvious choice for wireless power is a battery. However, batteries often need periodical replacement. The work presented here focuses on using ambient energy to power an autonomous sensor system and recharge batteries and capacitors used to run an active sensing system. In particular, we examine methods of harvesting energy to run sensor systems from ambient vibration energy using piezoelectric elements.

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Adaptive Structures for Structural Health Monitoring

Cited by 4 publications

References 22 publications

Multi-scale model updating of a timber footbridge using experimental vibration data

Multi-scale model updating of a timber footbridge using experimental vibration data

Multifunctional materials and structures for autonomic systems

Energy Harvesting for Autonomous Sensing

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