###### Abstract  
The applications of the deep belief network (DBN) for addressing practical engineering issues have recently emerged all over the world thanks to its accuracy and availability of data. In this paper, a predictive model using DBN was employed to investigate the factors that affect the ground-penetrating radar (GPR) signals from the rebar embedded in concrete structures. Four variables, namely temperature, relative humidity, chloride contamination level, and rebar surface corrosion condition were used as the model inputs for the investigation. Comprehensive data acquired from previously published documents were used to establish the proposed DBN model. It was shown that temperature and chloride contamination level variables generated significant effects on the GPR amplitude signal from rebar. In contrast, the relative humidity and rebar surface corrosion condition parameters were found to yield a minimal influence on the output of the proposed model. The DBN model can be used to predict the amplitude of GPR signals from the four inputs with a high level of accuracy. Specifically, the coefficient of determination (\(R^{2}\)) was 0.9634 and 0.9681 for the testing dataset and the entire database, respectively. deep belief network; parameter analysis; ground-penetrating radar; chloride contamination; concrete structure +
Footnote †: journal:  
0000-0001-9999-4  
concrete structures [8; 9], estimating concrete thickness cover and rebar diameter [10; 11], and localizing/pinpointing the rebar in concrete [12]. Experimental GPR data can be collected either through the contact or contactless method. In the contact method, the GPR device was placed directly on the surface of the survey area. GPR device is manually operated, and data are collected by the users. The travel distances of the antenna/device are determined using the encoder to monitor the number of rotating cycles of the attached wheels. The greatest benefit of this method is that it can be conducted in a narrow area or a vertical direction. The method, however, is less advantageous when rapid screening is required for the survey of large areas such as bridge decks or highway road surfaces. The contactless GPR technique or auto is a widely used method to collect GPR data for large areas. In this method, the GPR device was attached to the vehicle at a distance from the surface of the survey areas. The GPR device operates while the vehicle moves at a certain speed. The data collected from the GPR device are automatically synchronized with the traveling distance of the moving vehicle. This technique can collect GPR data at the traffic speed, and as a result, can significantly decrease the inspection timelines as well as reduce the need for road closure and traffic control. The impacts of chloride contamination, surface rust on rebar, and environmental conditions on GPR signals have been investigated by various researchers [13; 14; 15; 16; 17; 18; 19; 20]. For instance, [PERSON] and [PERSON] [15] studied the effects of moisture and chloride contamination on the reflected radar signals. A measurable influence of both moisture and chloride contamination factors on the radar signal was reported in their work. In a more recent study, [PERSON] and [PERSON] [16] concluded in their study that chloride content in concrete produced a higher signal amplitude attenuation compared to that of moisture. Relating to the effects of rebar surface rust, [PERSON] [17] stated in their study that there was no significant difference in the reflection amplitude of the radar signals between rebar with a 5 mm thick layer of rust and the intact rebar. A recent study by [PERSON] et al. [18], however, reported that the rust on the rebar surface was sensitive to the radar signals, as a result, they stated that the early stage of corrosion can be detected and visualized by analyzing the reflected radar signal.