Intelligent early warning model for structural safety of"three-in-one"self-built houses
Although a"three-in-one"self-built house is convenient for the owner to produce,store and operate,there are often many serious problems in its structural safety due to its complex functions.In order to warn the potential safety hazard of such buildings in advance,this study constructed a"three-in-one"self-built housing structure safety intelligent early warning model based on machine learning algorithm,and applied the integration idea and intelligent algorithm to improve the early warning performance twice.Firstly,the data obtained in a certain place were preprocessed by means of one-hot coding and oversampling.Secondly,the overall accuracy,recall rate and AUC value were selected to select the best base classifier.Then,the integrated ideas such as bag method and lifting method were used to improve the early warning performance.The intelligent optimization al-gorithms such as WOA,GJO and PSO were applied to further improve the early warning performance.Final-ly,based on the above model calculation results,the key indicators in the early warning indicators were mined.The results are as follows:①The Bagging(KNN)model optimized by the whale algorithm can more efficient-ly warn the structural safety of the"three-in-one"self-built houses,with a recall rate of 0.802;②the Boosting(SVM)model optimized by the whale algorithm has more stable early warning robustness,and the AUC value is 0.933;③the XGB model with default parameters has better overall early warning efficiency,and the overall accuracy rate is 0.915;④14 indicators such as building year,brick-concrete structure,number of upper floors and building area are the key indicators for the early warning of"three-in-one"self-built houses.
"three in one"self-built housestructural safetymachine learningintegrated algorithmintelligent optimization algorithmindex importance
国家科技期刊平台
NSTL
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