## 1 Introduction  
The assessment of a bridge deck's condition requires the use of multiple nondestructive evaluation (NDE) technologies. Some commonly used techniques are the half-cell potential (HCP) and electrical resistivity (ER). The HCP is used to determine the likelihood of active corrosion in reinforcing steel bars, while ER is employed to assess the corrosive environment. However, the NDE technologies have limitations in detecting certain types of defects and deterioration as their detection and characterization are influenced by various factors. For instance, and as discussed later, the accuracy of HCP measurements can be impacted by factors such as moisture content, concrete cover, and the anode/cathode ratio. Similarly, ER measurements can be influenced by factors such as moisture content, carbonation, and temperature. Therefore, it is crucial to have a thorough understanding of the strengths and limitations of NDE technologies to make the best use of them in a complementary manner.  
NDEs play a pivotal role in bridge monitoring, ensuring the structural integrity and safety of these critical infrastructure assets. NDEs provide invaluable data without compromising a bridge's structural integrity. However, the impact of environmental effects on these tests cannot be underestimated. Variations in moisture levels, or even the presence of corrosion, can alter the values obtained from these tests, potentially leading to an inaccurate interpretation or missed signs of degradation. Therefore, careful consideration of environmental factors is essential to maintaining the reliability and effectiveness of nondestructive tests in bridge inspection.  
The primary goal of this research is to address the foregoing issue by enhancing the interpretation of the results of ER and the HCP through the use of machine learning algorithms to analyze the results of surveys via multiple NDE techniques. Additionally, this research study has the following aims:  
* To integrate information from various NDE technologies to gain a better understanding of how different factors can impact their measurements and, consequently, the detection and characterization of defects or deterioration.
* To establish connections between the results from different technologies and the various factors that can influence the measurements to further improve their interpretation.  
In summary, this research seeks to overcome the limitations of NDE technologies by conducting a joint analysis of multiple techniques and considering the influence of various factors on their results. In particular, this study introduces an approach to enhance the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements in the context of concrete bridge deck assessments. This approach is manifested via the integration of multiple nondestructive evaluation (NDE) technologies with machine learning algorithms, specifically utilizing Random Forest methodology. Furthermore, the study conducts a parametric analysis to explore the impact of various key parameters on HCP and ER measurements, including the degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. By combining these elements, the research improves the accuracy of the measurement interpretation and ensures consistency in the condition monitoring of bridge decks through periodical NDE surveys.