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. 2022 Sep 11;15(18):6310.
doi: 10.3390/ma15186310.

Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning

Jónatas Valença  1 , Habibu Mukhandi  2 , André G Araújo  2   3 , Micael S Couceiro  2   3 , Eduardo Júlio  4
Affiliations

Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning

Jónatas Valença et al. Materials (Basel). .

Abstract

The strengthening of concrete structures with laminates of Carbon-Fiber-Reinforced Polymers (CFRP) is a widely adopted technique. retained The application is more effective if pre-stressed CFRP laminates are adopted. The measurement of the strain level during the pre-stress application usually involves laborious and time-consuming applications of instrumentation. Thus, the development of expedited approaches to accurately measure the pre-stressed application in the laminates represents an important contribution to the field. This paper proposes and benchmarks contact-free architecture for measuring the strain level of CFRP laminate based on computer vision. The main objective is to provide a solution that might be economically feasible, automated, easy to use, and accurate. The architecture is fed by digitally deformed synthetic images, generated based on a low-resolution camera. The adopted methods range from traditional machine learning to deep learning. Furthermore, dropout and cross-validation methods for quantifying traditional machine learning algorithms and neural networks are used to efficiently provide uncertainty estimates. ResNet34 deep learning architecture provided the most accurate results, reaching a root mean square error (RMSE) of 0.057‱ for strain prediction. Finally, it is important to highlight that the architecture presented is contact-free, automatic, cost-effective, and measures directly on the laminate surfaces, which allows them to be widely used in the application of pre-stressed laminates.

Keywords: CFRP laminates; computer vision; deep learning; machine learning; strain monitoring; strengthening RC.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Application of Carbon-Fiber-Reinforced Polymer (CFRP) laminates: (a) pre-stress application on the laminates; (b) final application of the laminates (images provided by S&P, Clever Reinforcement Iberica—Materiais de Construçao, Lda.).
Figure 2
Figure 2
Machine learning vs. deep learning algorithms.
Figure 3
Figure 3
Model building process in machine learning and deep learning.
Figure 4
Figure 4
Set up for image acquisition.
Figure 5
Figure 5
Synthetic images with different types of noises.
Figure 6
Figure 6
Illustration of the overall architecture.
Figure 7
Figure 7
Image before and after applying Canny Edge Detector.
Figure 8
Figure 8
ResNet34: (a) “Basic-Block” building block; (b) The structure of ResNet34 (from [41]).
Figure 9
Figure 9
Polynomial Regression and Decision Tree Regression results. (a) Polynomial Regression predictions. (b) Polynomial Regression predictions by quantile variations. (c) Decision Tree Regression predictions. (d) Decision Tree Regression predictions by quantile variations.
Figure 10
Figure 10
Fully connected neural network (FCNN) and Random Forest results. (a) Random Forest Regression predictions. (b) Random Forest Regression predictions by quantile variations. (c) FCNN with regression predictions. (d) FCNN with regression predictions by quantile variations.
Figure 11
Figure 11
Support Vector Regression (SVR) results. (a) Support Vector Regression predictions. (b) Support Vector Regression predictions by quantile variations.
Figure 12
Figure 12
ResNet and GoogLeNet results. (a) GoogLeNet with regression predictions. (b) GoogLeNet with regression predictions by quantile variations. (c) ResNet with regression predictions. (d) ResNet with regression predictions by quantile variations.
Figure 13
Figure 13
ResNet with regression vs. machine learning predictions. (a) ResNet with regression vs. FCNN with regression predictions. (b) ResNet with regression vs. Polynomial Regression predictions. (c) ResNet with regression vs. Random Forest Regression predictions. (d) ResNet with regression vs. Support Vector Regression predictions. (e) ResNet with regression vs. Decision Tree Regression predictions.
Figure 14
Figure 14
ResNet with regression RMSE vs. the rest. (a) ResNet with regression RMSE vs. Random Forest Regression RMSE. (b) ResNet with regression RMSE vs. FCNN with regression RMSE. (c) ResNet with regression RMSE vs. Polynomial Regression RMSE. (d) ResNet with Regression RMSE vs. Support Vector Regression RMSE. (e) ResNet with regression RMSE vs. Decision Tree Regression RMSE. (f) Resnet with regression RMSE vs. GoogLeNet with regression RMSE.
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References

    1. Ghadioui R., Proske T., Tran N., Graubner C.-A. Structural behaviour of CFRP reinforced concrete members under bending and shear loads. Mater. Struct. 2020;53:63. doi: 10.1617/s11527-020-01496-7. - DOI
    1. Fikry M., Ogihara S., Vinogradov V. The effect of matrix cracking on mechanical properties in FRP laminates. Mech. Adv. Mater. Mod. Process. 2018;4:3. doi: 10.1186/s40759-018-0036-6. - DOI
    1. Li C., Yin X., Wang Y., Zhang L., Zhang Z., Liu Y., Xian G. Mechanical property evolution and service life prediction of pultruded carbon/glass hybrid rod exposed in harsh oil-well condition. Compos. Struct. 2020;246:112418. doi: 10.1016/j.compstruct.2020.112418. - DOI
    1. Zhang K., Ma L.H., Song Z.Z., Gao H., Zhou W., Liu J., Tao R. Strength prediction and progressive damage analysis of carbon fiber reinforced polymer-laminate with circular holes by an efficient Artificial Neural Network. Compos. Tructures. 2022;296:115835. doi: 10.1016/j.compstruct.2022.115835. - DOI
    1. Webb G.T., Vardanega P.J., Hoult N.A., Fidler P.R.A., Bennett P.J., Middleton C.R. Analysis of fiber-optic strain-monitoring data from a prestressed concrete bridge. J. Bridge Eng. 2017;22:05017002. doi: 10.1061/(ASCE)BE.1943-5592.0000996. - DOI

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