Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques

Abstract

Compressive strength is the most important metric of concrete quality. Various nondestructive and semi-destructive tests can be used to evaluate the compressive strength of concrete. In the present study, a new image-based machine learning method is used to predict concrete compressive strength, including evaluation of six different models. These include support-vector machine model and various deep convolutional neural network models, namely AlexNet, GoogleNet, VGG19, ResNet, and Inception-ResNet-V2. In the present investigation, cement mortar samples were prepared using each of the cement:sand ratios of 1:3, 1:4, and 1:5, and using the water:cement ratios of 0.35 and 0.55. Cement concrete was prepared using the cement:sand:coarse aggregate ratios of 1:5:10, 1:3:6, 1:2:4, 1:1.5:3 and 1:1:2, using the water:cement ratio of 0.5 for all samples. The samples were cut, and several images of the cut surfaces were captured at various zoom levels using a digital microscope. All samples were then tested destructively for compressive strength. The images and corresponding compressive strength were then used to train machine learning models to allow them to predict compressive strength based upon the image data. The Inception-ResNet-V2 models exhibited the best predictions of compressive strength among the models tested. Overall, the present findings validated the use of machine learning models as an efficient means of estimating cement mortar and concrete compressive strengths based on digital microscopic images, as an alternative nondestructive/semi-destructive test method that could be applied at relatively less expense.

Graphical abstract

Keywords

support vector machine / deep convolutional neural network / microscope / digital image / curing period

References

[1] Breysse D, Romão X, Alwash M, Sbartaï Z M, Luprano V A M. Risk evaluation on concrete strength assessment with NDT technique and conditional coring approach. Journal of Building Engineering, 2020, 32 : 101541

[2] Jafari S, Rots J G, Esposito R. Core testing method to assess nonlinear shear-sliding behaviour of brick-mortar interfaces: A comparative experimental study. Construction & Building Materials, 2020, 244 : 118236

[3] Guo H, Zhuang X, Rabczuk T. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 2019, 59( 2): 433–456

[4] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59( 1): 345–359

[5] Herzog S, Tetzlaff C, Wörgötter F. Evolving artificial neural networks with feedback. Neural Networks, 2020, 123 : 153–162

[6] Haftkhani A R, Abdoli F, Sepehr A, Mohebby B. Regression and ANN models for predicting MOR and MOE of heat-treated fir wood. Journal of Building Engineering, 2021, 42 : 102788

[7] Martini R, Carvalho J, Arêde A, Varum H. Validation of nondestructive methods for assessing stone masonry using artificial neural networks. Journal of Building Engineering, 2021, 42 : 102469

[8] Niu X X, Suen C Y. A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition, 2012, 45( 4): 1318–1325

[9] Derman E, Salah A A. Continuous real-time vehicle driver authentication using convolutional neural network based face recognition. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). Xi’an: IEEE, 2018, 577–584

[10] Ebrahimi M, Khoshtaghaza M, Minaei S, Jamshidi B. Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture, 2017, 137 : 52–58

[11] Arnal Barbedo J G. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2013, 2( 1): 1–12

[12] Xu G, Zhang F, Shah S G, Ye Y, Mao H. Use of leaf color images to identify nitrogen and potassium deficient tomatoes. Pattern Recognition Letters, 2011, 32( 11): 1584–1590

[13] Nugraha B T, Su S F. Towards self-driving car using convolutional neural network and road lane detector. In: 2017 2nd International Conference on Automation, Cognitive Science, Optics, Micro Electro-mechanical System, and Information Technology (ICACOMIT). Jakarta: IEEE, 2017, 65–69

[14] Sun W, Tseng T L B, Zhang J, Qian W. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Computerized Medical Imaging and Graphics, 2017, 57 : 4–9

[15] Dabeer S, Khan M M, Islam S. Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked, 2019, 16 : 100231

[16] Parashar J, Sumiti M. Breast cancer images classification by clustering of ROI and mapping of features by CNN with XGBOOST learning. Materials Today: Proceedings, 2020, 1–9

[17] Gopalakrishnan K, Khaitan S K, Choudhary A, Agrawal A. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction & Building Materials, 2017, 157 : 322–330

[18] Hoang N D, Nguyen Q L. A novel method for asphalt pavement crack classification based on image processing and machine learning. Engineering with Computers, 2019, 35( 2): 487–498

[19] Lin Y Z, Nie Z H, Ma H W. Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civil and Infrastructure Engineering, 2017, 32( 12): 1025–1046

[20] Cha Y, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2017, 32( 5): 361–378

[21] Başyiğit C, Çomak B, KilinçarslanŞ, SerkanÜncü I. Assessment of concrete compressive strength by image processing technique. Construction & Building Materials, 2012, 37 : 526–532

[22] Dogan G, Arslan M H, Ceylan M. Concrete compressive strength detection using image processing based new test method. Measurement, 2017, 109 : 137–148

[23] Jang Y, Ahn Y, Kim H Y. Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images. Journal of Computing in Civil Engineering, 2019, 33( 3): 04019018

[24] Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86( 11): 2278–2324

[25] Han D, Liu Q, Fan W. A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 2018, 95 : 43–56

[26] SimonyanKZisserman A. Very deep convolutional networks for large-scale image recognition. 2015, arxiv: 1409.1556

[27] Lowe D G. Object recognition from local scale-invariant features. Proceedings of the seventh IEEE International Conference on Computer Vision, 1999, 2 : 1150–1157

[28] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60( 6): 84–90

[29] Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg A C, Fei-Fei L. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115( 3): 211–252

[30] Mathworks. GoogLeNet convolutional neural network—MATLAB googlenet—MathWorks Benelux. 2021 (available at the website of Mathworks)

[31] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016, 770–778

[32] Szegedy C, Ioffe S, Vanhoucke V, Alemi A A. Inception-V4, Inception-ResNet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2017, 4278–4284

[33] Mathworks. Pretrained Inception-ResNet-v2 convolutional neural network—MATLAB inceptionresnetv2—MathWorks América Latina. 2021 (available at the website of Mathworks)

RIGHTS & PERMISSIONS

Higher Education Press