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

Amit SHIULY, Debabrata DUTTA, Achintya MONDAL

PDF(4837 KB)
PDF(4837 KB)
Front. Struct. Civ. Eng. ›› 2022, Vol. 16 ›› Issue (3) : 347-358. DOI: 10.1007/s11709-022-0819-z
RESEARCH ARTICLE

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

Author information +
History +

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

Cite this article

Download citation ▾
Amit SHIULY, Debabrata DUTTA, Achintya MONDAL. Assessing compressive strengths of mortar and concrete from digital images by machine learning techniques. Front. Struct. Civ. Eng., 2022, 16(3): 347‒358 https://doi.org/10.1007/s11709-022-0819-z

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
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, 577–584
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, 65–69
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, 1–9
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. 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
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, 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

2022 Higher Education Press
AI Summary AI Mindmap
PDF(4837 KB)

Accesses

Citations

Detail

Sections
Recommended

/