Structural health monitoring using carbon nanotube/epoxy composites and strain-field pattern recognition

2018 ◽  
Author(s):  
Erick Giraldo-Pérez ◽  
Joham Alvarez-Montoya ◽  
Hader Vladimir Martínez Tejada ◽  
Julián Sierra-Pérez
Keyword(s):  
Pattern Recognition ◽  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Strain Field ◽  
Epoxy Composites ◽  
Field Pattern ◽  
Structural Health

Strain Field Pattern Recognition for Structural Health Monitoring Applications

2020 ◽  
pp. 1-40
Author(s):  
Julián Sierra-Pérez ◽  
Joham Alvarez-Montoya
Keyword(s):  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Strain Field ◽  
Machine Learning Techniques ◽  
Field Pattern ◽  
Strain Mapping ◽  
Engineering Structures ◽  
Structural Health ◽  
Nonlinear Pca

Strain field pattern recognition, also known as strain mapping, is a structural health monitoring approach based on strain measurements gathered through a network of sensors (i.e., strain gauges and fiber optic sensors such as FGBs or distributed sensing), data-driven modeling for feature extraction (i.e., PCA, nonlinear PCA, ANNs, etc.), and damage indices and thresholds for decision making (i.e., Q index, T2 scores, and so on). The aim is to study the correlations among strain readouts by means of machine learning techniques rooted in the artificial intelligence field in order to infer some change in the global behavior associated with a damage occurrence. Several case studies of real-world engineering structures both made of metallic and composite materials are presented including a wind turbine blade, a lattice spacecraft structure, a UAV wing section, a UAV aircraft under real flight operation, a concrete structure, and a soil profile prototype.

Structural Health Monitoring for Carbon Fiber/Carbon Nanotube (CNT)/Epoxy Composite Sensor

2006 ◽  
Vol 321-323 ◽  
pp. 290-293 ◽  
Author(s):  
Sang Il Lee ◽  
Dong Jin Yoon
Keyword(s):  
Electrical Resistivity ◽  
Carbon Nanotube ◽  
Carbon Fiber ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Epoxy Composite ◽  
Structural Safety ◽  
Structural Health ◽  
Nanotube Composites ◽  
Carbon Fiber Epoxy

Structural health monitoring for carbon nanotube (CNT)/carbon fiber/epoxy composite was verified by the measurement of electrical resistivity. This study has focused on the preparation of carbon nanotube composite sensors and their application for structural health monitoring. The change of the electrical resistance was measured by a digital multimeter under tensile loads. Although a carbon fiber was broken, the electrical connection was still kept by distributed CNT particles in the model composites. As the number of carbon fiber breakages increased, electrical resistivity was stepwise increased. The CNT composites were well responded with fiber damages during the electro-micromechnical test. Carbon nanotube composites can be useful sensors for structural health monitoring to diagnose a structural safety and to prevent a collapse.

Structural health monitoring of composites from carbon nanotube coated e-glass fibre

2021 ◽  
Vol 5 (1/2) ◽  
pp. 164
Author(s):  
Sergio Rafael Rodriguez ◽  
Sidney Wong ◽  
Omar Dwidar ◽  
Amro El Badawy ◽  
Ashraf Elbarbary ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Glass Fibre ◽  
Structural Health

scholarly journals Connecting concrete technology and machine learning: proposal for application of ANNs and CNT/concrete composites in structural health monitoring

RSC Advances ◽  
2020 ◽  
Vol 10 (39) ◽  
pp. 23038-23048
Author(s):  
Sofija Kekez ◽  
Jan Kubica
Keyword(s):  
Machine Learning ◽  
Carbon Nanotube ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Concrete Structures ◽  
Concrete Technology ◽  
Structural Health

Carbon nanotube/concrete composite possesses piezoresistivity i.e. self-sensing capability of concrete structures even in large scale.

Development of a Structural Health Monitoring Methodology in Reinforced Concrete Structures Using FBGs and Pattern Recognition Techniques

2019 ◽  
Author(s):  
Alejandra Amaya ◽  
Joham Alvarez-Montoya ◽  
Julián Sierra-Pérez
Keyword(s):  
Pattern Recognition ◽  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Engineering ◽  
Relevant Information ◽  
Fiber Optic Sensor ◽  
Structure Damage ◽  
Structural Health ◽  
External Stimuli

Abstract Structural health monitoring (SHM) is a branch of structural engineering which seeks for the development of monitoring systems that provide relevant information of any alteration that may occur in an engineering structure. This work presents the implementation of an SHM methodology in a prototype structure made of reinforced concrete by using fiber Bragg gratings (FBGs), a type of fiber optic sensor capable of measuring strain and temperature changes due to external stimuli. The SHM system includes an interrogation device and signal processing algorithms which are intended to study the physical variations on the FBGs measurements in order to detect anomalies in the structure promoted by a damage occurrence. The structure prototype is a porticoed structure which contains 48 embedded sensors: 32 of them are destinated for the strain measurement and are located in both columns and beams of the structure, 16 are temperature sensors which have been embedded for thermal compensation. Strain datasets for both pristine and damaged conditions were obtained for the structure while it was excited with a mechanical shaker which induced dynamic loading conditions resembling earthquakes. By using classification algorithms based on pattern recognition, it is intended to process the datasets with the aim of reaching the first level of SHM in the structure (damage detection).

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