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).

scholarly journals Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2328 ◽  
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Big Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Statistical Pattern Recognition ◽  
Statistical Decision ◽  
Structural Health ◽  
Statistical Pattern ◽  
Arma Modeling

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.

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.

A Gaussian process–based approach to cope with uncertainty in structural health monitoring

2016 ◽  
Vol 16 (2) ◽  
pp. 174-184 ◽  
Author(s):  
Hessamodin Teimouri ◽  
Abbas S. Milani ◽  
Jason Loeppky ◽  
Rudolf Seethaler
Keyword(s):  
Pattern Recognition ◽  
Structural Health Monitoring ◽  
Gaussian Process ◽  
Health Monitoring ◽  
Network Models ◽  
Neural Network Models ◽  
Structural Health ◽  
Covariance Functions ◽  
Industrial Sectors ◽  
The Impact

Structural health monitoring is widely applied in industrial sectors as it reduces costs associated with maintenance intervals and manual inspections of damage in sensitive structures, while enhancing their operation safety. A major concern and current challenge in developing “robust” structural health monitoring systems, however, is the impact of uncertainty in the input training parameters on the accuracy and reliability of predictions. The aim of this article is to adapt an advanced statistical pattern recognition technique capable of considering variations in input parameters and arriving at a new structural health monitoring system more immune to the effect of uncertainty. Gaussian processes have been implemented to predict the state of damage in a typical composite airfoil structure. Different covariance functions were evaluated during the training stage of structural health monitoring. Results through a case study showed a remarkable capability of the Gaussian process–based approach to deal with uncertainty in the pattern recognition problem in structural health monitoring of a multi-layer composite airfoil structure. To illustrate robustness advantage of the approach as compared to conventional neural network models, the damage size and location prediction accuracy of the Gaussian process structural health monitoring has been compared to multi-layer perceptron neural networks. Some practical insights and limitations of the approach have also been outlined.

Sign in / Sign up

Export Citation Format

Share Document