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.INDEX TERMS Channel state information (CSI), classification, construction materials, convolutional neural network (CNN), Wi-Fi sensing.[1], [2]. In this work, we focus on observing and measuring three physical parameters namely, thickness, moisture content, and degree of compaction, in different individual raw construction materials. The conducted experiments were designed to study the internal physical properties of the observed materials, as well as evaluate the proposed method's performance robustness to variations in physical traits such as thickness. These parameters have a substantial significance in the industry and quality assurance processes. Furthermore, the existing material classification methods that investigate the mentioned parameters often have a compromise between accuracy, cost, and deployability in practical setups. In the industry, there is considerable interest in measuring the