Bridges are critical components of civil infrastructure. According to the National Bridge Inventory (NBI), there are more than ten thousand structurally deficient bridges in the United States. It becomes critical for the authorities to maintain their serviceability and reliability in order to keep the transportation system operational. Current bridge condition assessment practices are mainly based on visual inspections carried out by technical experts, which is subjective as observations and opinions may vary from one individual to another, and is expensive and prone to human errors. The main focus of this study is to help improve current inspection practices by implementing image processing algorithms to detect concrete surface cracks and integrate the results into a bridge information modeling (BrIM) based framework. Integrating crack detection algorithm results with BrIM will allow users to view and explore cracks and their properties linked to a three dimensional (3D) model of the inspected bridge component. The proposed methodology processes 2D images by adjusting pixel parameters of gray scale images and detects cracks with their dimensional aspects. It implements existing crack detection algorithms, a scaling tool to automatically measure crack dimensions, and includes a framework to integrate crack detection results with BrIM for inspecting bridges in a more efficient manner. This will enable more effective repairs and maintenance operations, saving a considerable amount of effort, time, and money.