Condition assessment is one of the most essential practices in bridge asset management to maintain the safety and durability of structures. Routine bridge inspection, a visual-based method, is regularly performed by qualified inspectors to determine the condition of individual bridge elements manually using bridge inspection standards. However, the quality of a visual-based condition assessment relies heavily on the inspector's knowledge and experience. The research presented here focuses on the development of an enhanced method to minimise the shortcomings of visual-based inspection. In this paper, we investigate the performance of RBF-kernel support vector machines (SVMs), a supervised machine learning technique, to increase the reliability of visual-based bridge inspection. The results of this study can contribute to minimising the shortcomings of current visual-based bridge inspection practices.