Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks
Currently, artificial intelligence (AI) technologies are becoming a strategic vector for the development of companies in the construction sector. The introduction of “smart solutions” at all stages of the life cycle of building materials, products and structures is observed everywhere. Among the var...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/13/12/3014 |
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| author | Irina Razveeva Alexey Kozhakin Alexey N. Beskopylny Sergey A. Stel’makh Evgenii M. Shcherban’ Sergey Artamonov Anton Pembek Himanshu Dingrodiya |
| author_facet | Irina Razveeva Alexey Kozhakin Alexey N. Beskopylny Sergey A. Stel’makh Evgenii M. Shcherban’ Sergey Artamonov Anton Pembek Himanshu Dingrodiya |
| author_sort | Irina Razveeva |
| collection | DOAJ |
| description | Currently, artificial intelligence (AI) technologies are becoming a strategic vector for the development of companies in the construction sector. The introduction of “smart solutions” at all stages of the life cycle of building materials, products and structures is observed everywhere. Among the variety of applications of AI methods, a special place is occupied by the development of the theory and technology of creating artificial systems that process information from images obtained during construction monitoring of the structural state of objects. This paper discusses the process of developing an innovative method for analyzing the presence of cracks that arose after applying a load and delamination as a result of the technological process, followed by estimating the length of cracks and delamination using convolutional neural networks (CNN) when assessing the condition of aerated concrete products. The application of four models of convolutional neural networks in solving a problem in the field of construction flaw detection using computer vision is shown; the models are based on the U-Net and LinkNet architecture. These solutions are able to detect changes in the structure of the material, which may indicate the presence of a defect. The developed intelligent models make it possible to segment cracks and delamination and calculate their lengths using the author’s SCALE technique. It was found that the best segmentation quality was shown by a model based on the LinkNet architecture with static augmentation: precision = 0.73, recall = 0.80, F1 = 0.73 and IoU = 0.84. The use of the considered algorithms for segmentation and analysis of cracks and delamination in aerated concrete products using various convolutional neural network architectures makes it possible to improve the quality management process in the production of building materials, products and structures. |
| first_indexed | 2024-03-08T20:55:37Z |
| format | Article |
| id | doaj.art-49a7f894cb04498b81f3c8bd5a520b3e |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-08T20:55:37Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-49a7f894cb04498b81f3c8bd5a520b3e2023-12-22T13:58:11ZengMDPI AGBuildings2075-53092023-12-011312301410.3390/buildings13123014Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural NetworksIrina Razveeva0Alexey Kozhakin1Alexey N. Beskopylny2Sergey A. Stel’makh3Evgenii M. Shcherban’4Sergey Artamonov5Anton Pembek6Himanshu Dingrodiya7Department of Unique Buildings and Constructions Engineering, Don State Technical University, 344003 Rostov-on-Don, RussiaDepartment of Unique Buildings and Constructions Engineering, Don State Technical University, 344003 Rostov-on-Don, RussiaDepartment of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, RussiaDepartment of Unique Buildings and Constructions Engineering, Don State Technical University, 344003 Rostov-on-Don, RussiaDepartment of Engineering Geology, Bases, and Foundations, Don State Technical University, 344003 Rostov-on-Don, RussiaDepartment of Elasticity Theory, Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Leninskiye Gory, 1, 119991 Moscow, RussiaDepartment of Quantum Statistics and Field Theory, Faculty of Physics, Lomonosov Moscow State University, Leninskiye Gory, 1, 119991 Moscow, RussiaDepartment of Chemical Engineering, Ujjain Engineering College, Ujjain 456010, MP, IndiaCurrently, artificial intelligence (AI) technologies are becoming a strategic vector for the development of companies in the construction sector. The introduction of “smart solutions” at all stages of the life cycle of building materials, products and structures is observed everywhere. Among the variety of applications of AI methods, a special place is occupied by the development of the theory and technology of creating artificial systems that process information from images obtained during construction monitoring of the structural state of objects. This paper discusses the process of developing an innovative method for analyzing the presence of cracks that arose after applying a load and delamination as a result of the technological process, followed by estimating the length of cracks and delamination using convolutional neural networks (CNN) when assessing the condition of aerated concrete products. The application of four models of convolutional neural networks in solving a problem in the field of construction flaw detection using computer vision is shown; the models are based on the U-Net and LinkNet architecture. These solutions are able to detect changes in the structure of the material, which may indicate the presence of a defect. The developed intelligent models make it possible to segment cracks and delamination and calculate their lengths using the author’s SCALE technique. It was found that the best segmentation quality was shown by a model based on the LinkNet architecture with static augmentation: precision = 0.73, recall = 0.80, F1 = 0.73 and IoU = 0.84. The use of the considered algorithms for segmentation and analysis of cracks and delamination in aerated concrete products using various convolutional neural network architectures makes it possible to improve the quality management process in the production of building materials, products and structures.https://www.mdpi.com/2075-5309/13/12/3014computer visionconvolutional neural networksegmentationaerated concretecracks |
| spellingShingle | Irina Razveeva Alexey Kozhakin Alexey N. Beskopylny Sergey A. Stel’makh Evgenii M. Shcherban’ Sergey Artamonov Anton Pembek Himanshu Dingrodiya Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks Buildings computer vision convolutional neural network segmentation aerated concrete cracks |
| title | Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks |
| title_full | Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks |
| title_fullStr | Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks |
| title_full_unstemmed | Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks |
| title_short | Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks |
| title_sort | analysis of geometric characteristics of cracks and delamination in aerated concrete products using convolutional neural networks |
| topic | computer vision convolutional neural network segmentation aerated concrete cracks |
| url | https://www.mdpi.com/2075-5309/13/12/3014 |
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