Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks
The process of identifying the physical properties of raw construction materials is vital in several industrial and quality assurance applications. Ideally, this process needs to be performed without damaging the sample and at low-cost, while obtaining high-accurate results. In this work, a novel no...
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| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32567 |
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| _version_ | 1864513440258719745 |
|---|---|
| author | Gacem, Mohamed A. |
| author2 | Zakaria, Amer S. Ismail, Mahmoud H. Tariq, Usman Yehia, Sherif |
| author2_role | author author author author |
| author_facet | Gacem, Mohamed A. Zakaria, Amer S. Ismail, Mahmoud H. Tariq, Usman Yehia, Sherif |
| author_role | author |
| dc.creator.none.fl_str_mv | Gacem, Mohamed A. Zakaria, Amer S. Ismail, Mahmoud H. Tariq, Usman Yehia, Sherif |
| dc.date.none.fl_str_mv | 2022-12-07 2025-12-17T10:56:03Z 2025-12-17T10:56:03Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Gacem, M. A., Zakaria, A. S., Ismail, M. H., Tariq, U., & Yehia, S. (2022). Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks. IEEE Access, 10, 126100–126116. https://doi.org/10.1109/access.2022.3226248 2169-3536 https://hdl.handle.net/11073/32567 10.1109/ACCESS.2022.3226248 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/access.2022.3226248 |
| dc.rights.none.fl_str_mv | Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.subject.none.fl_str_mv | Channel State Information (CSI) Classification Construction Materials Convolutional Neural Network (CNN) Wi-Fi Sensing |
| dc.title.none.fl_str_mv | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The process of identifying the physical properties of raw construction materials is vital in several industrial and quality assurance applications. Ideally, this process needs to be performed without damaging the sample and at low-cost, while obtaining high-accurate results. In this work, a novel non- destructive construction materials classification tool is proposed. The proposed method is based on passing Wi-Fi signals through the observed samples, then analyzing the Channel State Information (CSI) magnitude and phase components. The collected CSI data packets are pre-processed by performing an averaging operation. Then, the resulting data are divided into training and validation sets and used to train Convolutional Neural Networks (CNNs). Here, the trained CNN models are formulated either as classifiers or regression models, depending on the material under test. If the objective is to sort materials within specific classes, then the CNN is formulated as a classifier. Alternatively, if the goal is to estimate a continuously varying parameter in a material, then the CNN is formulated as a regression model. Furthermore, as per the collected data, the proposed method is used to identify the construction materials based on their thickness, water content value (moisture), and compaction. The obtained experimental results effectively demonstrate the potential and merits of the proposed method. Overall, the trained CNN models achieved a 100% validation accuracy and a low validation loss, which confirms that the method is valid and highly accurate. |
| format | article |
| id | aus_0e3d845666758a52b5fd6cf23b8a7baa |
| identifier_str_mv | Gacem, M. A., Zakaria, A. S., Ismail, M. H., Tariq, U., & Yehia, S. (2022). Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks. IEEE Access, 10, 126100–126116. https://doi.org/10.1109/access.2022.3226248 2169-3536 10.1109/ACCESS.2022.3226248 |
| language_invalid_str_mv | en |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32567 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| spelling | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural NetworksGacem, Mohamed A.Zakaria, Amer S.Ismail, Mahmoud H.Tariq, UsmanYehia, SherifChannel State Information (CSI)ClassificationConstruction MaterialsConvolutional NeuralNetwork (CNN)Wi-Fi SensingThe process of identifying the physical properties of raw construction materials is vital in several industrial and quality assurance applications. Ideally, this process needs to be performed without damaging the sample and at low-cost, while obtaining high-accurate results. In this work, a novel non- destructive construction materials classification tool is proposed. The proposed method is based on passing Wi-Fi signals through the observed samples, then analyzing the Channel State Information (CSI) magnitude and phase components. The collected CSI data packets are pre-processed by performing an averaging operation. Then, the resulting data are divided into training and validation sets and used to train Convolutional Neural Networks (CNNs). Here, the trained CNN models are formulated either as classifiers or regression models, depending on the material under test. If the objective is to sort materials within specific classes, then the CNN is formulated as a classifier. Alternatively, if the goal is to estimate a continuously varying parameter in a material, then the CNN is formulated as a regression model. Furthermore, as per the collected data, the proposed method is used to identify the construction materials based on their thickness, water content value (moisture), and compaction. The obtained experimental results effectively demonstrate the potential and merits of the proposed method. Overall, the trained CNN models achieved a 100% validation accuracy and a low validation loss, which confirms that the method is valid and highly accurate.American University of SharjahIEEE2025-12-17T10:56:03Z2025-12-17T10:56:03Z2022-12-07Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfGacem, M. A., Zakaria, A. S., Ismail, M. H., Tariq, U., & Yehia, S. (2022). Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks. IEEE Access, 10, 126100–126116. https://doi.org/10.1109/access.2022.32262482169-3536https://hdl.handle.net/11073/3256710.1109/ACCESS.2022.3226248enhttps://doi.org/10.1109/access.2022.3226248Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:repository.aus.edu:11073/325672025-12-17T11:19:50Z |
| spellingShingle | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks Gacem, Mohamed A. Channel State Information (CSI) Classification Construction Materials Convolutional Neural Network (CNN) Wi-Fi Sensing |
| status_str | publishedVersion |
| title | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| title_full | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| title_fullStr | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| title_full_unstemmed | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| title_short | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| title_sort | Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks |
| topic | Channel State Information (CSI) Classification Construction Materials Convolutional Neural Network (CNN) Wi-Fi Sensing |
| url | https://hdl.handle.net/11073/32567 |