Surface Crack Evaluation Method in Concrete Structures

Cracks in concrete structures should be measured to periodically assess potential problems in durability and serviceability. Conventional crack measurement systems depend on visual inspections and manual measurements of the crack features such as width, length, and direction using microscope and crack gage. However, conventional methods take long time as well as manpower, and lack quantitative objectivity resulted by inspectors. In this study, an evaluation technique for concrete surface cracks is developed using image processing and artificial neural network. Developed technique consists of three major parts: (1) crack detection (2) crack analysis and (3) pattern recognition. To examine validity of the technique developed in this study, crack analyzing tests were performed on the images obtained from various types of concrete surface cracks. The test results revealed that the system is highly effective in automatically analyzing concrete surface cracks in terms of features and patterns of cracks.