Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission

This study focuses on the clustering of the indentation-induced interlaminar and intralaminar damages in carbon/epoxy laminated composites using Acoustic Emission (AE) technique. Two quasi-isotropic specimens with layups of [60/0/-60]4S (is named dispersed specimen) and [604/04/-604]S (is named blocked specimen) were fabricated and subjected to a quasi-static indentation loading. The mechanical data, digital camera and ultrasonic C-scan images of the damaged specimens showed different damage evolution behaviors for the blocked and dispersed specimens. Then, the AE signals of the specimens were clustered for tracking the evolution behavior of different damage mechanisms. In order to select a reliable clustering method, the performance of six different clustering methods consisting of k-Means, Genetic k-Means, Fuzzy C-Means, Self-Organizing Map (SOM), Gaussian Mixture Model (GMM), and hierarchical model were compared. The results illustrated that hierarchical model has the best performance in clustering of AE signals. Finally, the evolution behavior of each damage mechanism was investigated by the clustered AE signals with hierarchical model. The results of this study show that using AE technique with an appropriate clustering method such as hierarchical model could be an applicable tool for structural health monitoring of composite structures.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Structural Integrity & Composite

Barely visible impact damage assessment in laminated composites using acoustic emission

Despite the key advantages of Fiber Reinforced Polymer (FRP) composites, they are susceptible to Barely Visible Impact Damage (BVID) under transverse loadings. This study investigates BVID in two quasi-isotropic carbon/epoxy laminates under quasi-static indentation and Low-Velocity Impact (LVI) loadings using Acoustic Emission (AE). First, the evolution of interlaminar and intralaminar damages is studied by analyzing the AE signals of the indentation test using b-value and sentry function methods. Then, the specimens are subjected to the LVI loading and the induced damages are compared with the indentation test and the percentage of each damage mechanism is calculated using Wavelet Packet Transform (WPT). In consistent with the mechanical data, ultrasonic C-scan and digital camera images of the specimens, the AE results show a considerable similarity between the induced BVID under quasi-static indentation and LVI tests. Finally, the obtained results show that AE is a powerful tool to study BVID in laminated composites under quasi-static and dynamic transverse loadings.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Structural Integrity & Composite

Acoustic Emission-Based Methodology to Evaluate Delamination Crack Growth Under Quasi-static and Fatigue Loading Conditions

The aim of this study was to investigate the applicability of acoustic emission (AE) technique to evaluate delamination crack in glass/epoxy composite laminates under quasi-static and fatigue loading. To this aim, double cantilever beam specimens were subjected to mode I quasi-static and fatigue loading conditions and the generated AE signals were recorded during the tests. By analyzing the mechanical and AE results, an analytical correlation between the AE energy with the released strain energy and the crack growth was established. It was found that there is a 3rd degree polynomial correlation between the crack growth and the cumulative AE energy. Using this correlation the delamination crack growth was predicted under both the static and fatigue loading conditions. The predicted crack growth values was were in a good agreement with the visually recorded data during the tests. The results indicated that the proposed AE-based method has good applicability to evaluate the delamination crack growth under quasi-static and fatigue loading conditions, especially when the crack is embedded within the structure and could not be seen visually.Structural Integrity & Composite

A comparison between support vector machine and water cloud model for estimating crop leaf area index

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m-2 and mean absolute error (MAE) of 0.51 m2m-2. The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m-2 and MAE of 0.61 m2m-2) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m-2 and MAE of 0.30 m2m-2). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.Water Resource