Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques
Amit SHIULY, Debabrata DUTTA, Achintya MONDAL
Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques
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.
support vector machine / deep convolutional neural network / microscope / digital image / curing period
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[5] |
Herzog S, Tetzlaff C, Wörgötter F. Evolving artificial neural networks with feedback. Neural Networks, 2020, 123 : 153–162
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[8] |
Niu X X, Suen C Y. A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition, 2012, 45( 4): 1318–1325
CrossRef
Google scholar
|
[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,
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[11] |
Arnal Barbedo J G. Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2013, 2( 1): 1–12
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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,
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[15] |
Dabeer S, Khan M M, Islam S. Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked, 2019, 16 : 100231
CrossRef
Google scholar
|
[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,
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[21] |
Başyiğit C, Çomak B, KilinçarslanŞ, SerkanÜncü I. Assessment of concrete compressive strength by image processing technique. Construction & Building Materials, 2012, 37 : 526–532
CrossRef
Google scholar
|
[22] |
Dogan G, Arslan M H, Ceylan M. Concrete compressive strength detection using image processing based new test method. Measurement, 2017, 109 : 137–148
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[28] |
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60( 6): 84–90
CrossRef
Google scholar
|
[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
CrossRef
Google scholar
|
[30] |
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,
CrossRef
Google scholar
|
[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,
|
[33] |
Mathworks
|
/
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