Tower crane safety comprehensive evaluation based on SVM

This paper focuses on the issues tower crane safety hard to be comprehensive evaluated, the machine learning theory is introduced to the tower crane safety analysis. Firstly, collect the tower crane operating parameters as samples, the SVM classifier make supervised-learning on the samples data, established classification model and running parameters are used to evaluate the secure state. In addition, the effect of different kernel functions and parameters on SVM classifier is discussed, parameters are selected after optimization by mixed method of cross-validation and grid search. Experiments show that optimized SVM model can judge Tower crane safety correctly.