Concrete cracks are common in concrete dams

and their classification results directly affect safety assessment and treatment decisions. To address the limitations of traditional crack classification methods

which rely heavily on expert experience and have strong subjectivity

this paper proposes a machine learning-based crack classification method. First

combining engineering practice experience with specification requirements

a classification standard system is constructed

which includes crack location

length

width

depth

and development trend. Then

the BP neural network model is adopted. By training with sample data

automatic crack classification is realized

and the accuracy and reliability of the model are verified. The study results show that the accuracy of the proposed method on the test set can reach 80%

and it is highly consistent with expert rating results

providing a fast

accurate

and efficient technical means for concrete crack classification.