ABSTRACT
In the current small and medium-sized bridges in the daily safety inspections, such as high dependence on inspectors and lack of quantifiable scientific basis. In practice, Professional engineers spend an enormous amount of time check the textual description and looking at image data of damages to bridge members, such as girders, bearings, expansion joints, and piers that are acquired from periodic inspections, and then determining make a rating of the bridge condition. Therefore, it is necessary to develop a practical method for the evaluation of bridge soundness to assess the degree of structural health. In this paper, the maintenance priority evaluation of bridge integrity of small and medium span bridges is examined using the Support Vector Machines (SVM), Decision Tree (DT), and Random Forest (RF) of artificial intelligence (Al) techniques. Based on the model, three types of bridge is used, an algorithm is proposed as useful feature to provide the engineering expertise for the inspection of bridges. This proposed method was able to substitute engineer judgement to distinguish the health rating of I. The input data were the degrees of deterioration of the structural parts and the output data were the soundness of the structure. The results showed that the inspection item of crack location and exposed direction of steel bars gives good assessment on whether bridge maintenance works are needed or not.
