<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning algorithm has been applied using images of samples of materials used for road surfaces. The photographs showed cross-sections of random samples taken with a CT scanner. Historical samples were used for the analysis, located in a database collecting information over many years. The deep learning analysis was performed using some elements of the VGG16 network architecture and implemented using the R language. The learning and training data were augmented and cross-validated. This resulted in the high level of 96.4% quality identification of the sample type and its selected structural features. The photographs in the identification set were correctly identified in terms of structure, mix type and grain size. The trained model identified samples in the domain of the dataset used for training in a very good way. As a result, in the future such a methodology may facilitate the identification of the type of mixture, its basic properties and defects.</p>
</abstract>

In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning algorithm has been applied using images of samples of materials used for road surfaces. The photographs showed cross-sections of random samples taken with a CT scanner. Historical samples were used for the analysis, located in a database collecting information over many years. The deep learning analysis was performed using some elements of the VGG16 network architecture and implemented using the R language. The learning and training data were augmented and cross-validated. This resulted in the high level of 96.4% quality identification of the sample type and its selected structural features. The photographs in the identification set were correctly identified in terms of structure, mix type and grain size. The trained model identified samples in the domain of the dataset used for training in a very good way. As a result, in the future such a methodology may facilitate the identification of the type of mixture, its basic properties and defects.

Application of Deep Learning Techniques in Identification of the Structure of Selected Road Materials

Structure and Environment

This work is licensed under the Creative Commons Attribution 3.0 Public License.

In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning...

Application of Deep Learning Techniques in Identification of the Structure of Selected Road Materials

Data publikacji: 02 lis 2023

Zakres stron: 159 - 167

DOI: https://doi.org/10.30540/sae-2023-014

Słowa kluczowe
deep learning, tomograph, R programming language, classification, road surfaces, correlation, digital image

© 2023 Grzegorz Mazurek, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Application of Deep Learning Techniques in Identification of the Structure of Selected Road Materials

Data publikacji: 02 lis 2023

Zakres stron: 159 - 167

DOI: https://doi.org/10.30540/sae-2023-014

Słowa kluczowedeep learning, tomograph, R programming language, classification, road surfaces, correlation, digital image

© 2023 Grzegorz Mazurek, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 3.0 Public License.

Słowa kluczowe
deep learning, tomograph, R programming language, classification, road surfaces, correlation, digital image