scholarly journals Structural Health Monitoring of Bridges Via Energy Harvesting Sensor Nodes

2016 ◽  
Vol 10 (1) ◽  
pp. 136-149 ◽  
Author(s):  
N. Bonessio ◽  
P. Zappi ◽  
G. Benzoni ◽  
T. Simunic Rosing ◽  
G. Lomiento
Keyword(s):  
Structural Health Monitoring ◽  
Energy Harvesting ◽  
Health Monitoring ◽  
State Of The Art ◽  
Sensor Nodes ◽  
Structural Performance ◽  
Structural Health ◽  
Random Decrement Technique ◽  
Random Decrement ◽  
Wireless Energy Harvesting

This paper deals with the application of novel sensing technologies to an existing Structural Health Monitoring (SHM) system for bridges. A vibration based SHM algorithm already in use to detect the structural performance degradation of a suspension highway bridge is modified to investigate the feasibility of replacing traditional wired accelerometers with state of the art wireless energy-harvesting sensors. The remodeled SHM algorithm benefits from the sensor nodes’ ability to support automated triggering and data pre-processing. The Random Decrement technique was included in the algorithm as a pre-processing tool to simultaneously reduce noise and amount of stored and transmitted data. Simulations based on available data were used to calibrate the triggering strategy, to verify the effectiveness of the data pre-processing, and to demonstrate power consumption improvements arising from the algorithm modification.

scholarly journals Structural health monitoring of two-way slabs based on random decrement technique

2021 ◽  
Author(s):  
Azita Pourrastegar
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Failure Modes ◽  
Ambient Vibration ◽  
Experimental Investigations ◽  
Monitoring Method ◽  
Structural Health ◽  
Random Decrement Technique ◽  
Random Decrement ◽  
Extent Of Damage

The current research attempts to explore the feasible use of a Structural Health Monitoring method for a two-way slab system through the effective vibration based damage diagnostic technique of Random Decrement (RD). Experimental investigations have been conducted on a total of four reinforced concrete two-way slab specimens. The slabs behaviour was examined under static loading. The results were presented in terms of load-deflection relationship at service and ultimate load, crack pattern and failure modes. At each stage of loading, the ambient vibration excitation test has been performed to investigate the extent of damage at the cracking, yield, and ultimate states through changes in dynamic parameters obtained from RD signatures. Additional applications of RD technique were performed on two-way slabs, first, to explore the location of damage by Multi-Channel Random Decrement using FBG sensor arrays. Secondly, RD technique was utilized to evaluate the extent of damage under successive equal dynamic impacts.

scholarly journals Structural health monitoring of two-way slabs based on random decrement technique

2021 ◽  
Author(s):  
Azita Pourrastegar
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Failure Modes ◽  
Ambient Vibration ◽  
Experimental Investigations ◽  
Monitoring Method ◽  
Structural Health ◽  
Random Decrement Technique ◽  
Random Decrement ◽  
Extent Of Damage

The current research attempts to explore the feasible use of a Structural Health Monitoring method for a two-way slab system through the effective vibration based damage diagnostic technique of Random Decrement (RD). Experimental investigations have been conducted on a total of four reinforced concrete two-way slab specimens. The slabs behaviour was examined under static loading. The results were presented in terms of load-deflection relationship at service and ultimate load, crack pattern and failure modes. At each stage of loading, the ambient vibration excitation test has been performed to investigate the extent of damage at the cracking, yield, and ultimate states through changes in dynamic parameters obtained from RD signatures. Additional applications of RD technique were performed on two-way slabs, first, to explore the location of damage by Multi-Channel Random Decrement using FBG sensor arrays. Secondly, RD technique was utilized to evaluate the extent of damage under successive equal dynamic impacts.

scholarly journals Energy Harvesting Technologies for Structural Health Monitoring of Airplane Components—A Review

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6685 ◽  
Author(s):  
Saša Zelenika ◽  
Zdenek Hadas ◽  
Sebastian Bader ◽  
Thomas Becker ◽  
Petar Gljušćić ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Energy Harvesting ◽  
Power Management ◽  
Health Monitoring ◽  
Composite Structures ◽  
Guided Wave ◽  
Sensor Nodes ◽  
Specific Power ◽  
Power Sources ◽  
Structural Health

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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