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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9968266/ |
| _version_ | 1828118051861561344 |
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| author | Mohamed A. Gacem Amer S. Zakaria Mahmoud H. Ismail Usman Tariq Sherif Yehia |
| author_facet | Mohamed A. Gacem Amer S. Zakaria Mahmoud H. Ismail Usman Tariq Sherif Yehia |
| author_sort | Mohamed A. Gacem |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T13:23:47Z |
| format | Article |
| id | doaj.art-b705299978f04c13bdb93b9adc441029 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T13:23:47Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b705299978f04c13bdb93b9adc4410292022-12-22T04:22:07ZengIEEEIEEE Access2169-35362022-01-011012610012611610.1109/ACCESS.2022.32262489968266Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural NetworksMohamed A. Gacem0https://orcid.org/0000-0002-5947-9162Amer S. Zakaria1https://orcid.org/0000-0002-6655-5418Mahmoud H. Ismail2https://orcid.org/0000-0001-5413-5355Usman Tariq3https://orcid.org/0000-0002-8244-2165Sherif Yehia4Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Electrical Engineering, American University of Sharjah, Sharjah, United Arab EmiratesDepartment of Civil Engineering, American University of Sharjah, Sharjah, United Arab EmiratesThe 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.https://ieeexplore.ieee.org/document/9968266/Channel state information (CSI)classificationconstruction materialsconvolutional neural network (CNN)Wi-Fi sensing |
| spellingShingle | Mohamed A. Gacem Amer S. Zakaria Mahmoud H. Ismail Usman Tariq Sherif Yehia Measurement of Construction Materials Properties Using Wi-Fi and Convolutional Neural Networks IEEE Access Channel state information (CSI) classification construction materials convolutional neural network (CNN) Wi-Fi sensing |
| 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://ieeexplore.ieee.org/document/9968266/ |
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