Prediction on accident types of building construction based on Bayesian optimized XGBoost
In order to explore the correlation between different risk characteristics and construction accident types in the con-struction process of construction engineering,a prediction model of construction accident types combining the feature selection algorithm and machine learning algorithm was proposed.Based on 619 domestic construction accident reports,a construction risk feature system was established,and 26 key risk features were screened out by the conditional mutual information maximi-zation(CMIM)-Boruta method,which were used as the input variables of Bayesian optimized extreme gradient boosting(XGBoost)prediction model.The prediction accuracy of the proposed model was evaluated on the test sets.The results show that the XGBoost model has better prediction performance than other machining learning methods.Moreover,the CMIM-Boruta method and Bayesian optimization method can effectively improve the prediction performance of machine learning mod-els.Through two actual accident cases,it is proved that the model has certain practicability.The research results have refer-ence significance for the safety management personnel to more accurately identify the potential hazards at the construction site and then take more targeted prevention measures.
construction engineeringaccident predictionfeature selectionmachine learningBayesian optimization
万方数据
维普
