The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not *** prediction algorithms based on tradit...
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The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not *** prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of *** order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated *** Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level *** gradientboostingalgorithm is adopted to fuse low-level and high-level features,as well as predict *** PM2.5 concentration pollution grade classification index,and grade the forecast *** expert experience knowledge is utilized to assist the optimization of the pre-warning *** experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.
In the incremental launching method employed for steel bridge construction, the girder is subjected to patch loading which occurs at the piers' position. This loading significantly affects the girder resistance in...
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In the incremental launching method employed for steel bridge construction, the girder is subjected to patch loading which occurs at the piers' position. This loading significantly affects the girder resistance in the construction stage. Therefore, prediction of the girder resistance under this loading is important. This paper proposes a new approach for predicting the patch load resistance of stiffened plate girders using an extreme gradient boosting algorithm (XGBoost). A total of 170 experimental data on stiffened plate girders under patch loading collected from the literature serves as the training and testing data to build the predictive model. To demonstrate the efficiency of the proposed model, its predictions were compared with those obtained from other Machine Learning (ML) methods such as support vector machines (SVM), decision tree (DT), random forest (RF), adaptive boost (Adaboost), and deep learning (DL). The accuracy of the proposed model was validated against the existing equations taken from the design standards (EN-1993-1-5 and BS 5400) as well as existing formulae in the literature. The comparative results reveal that the proposed model provides better and more accurate predictions than the existing formulae.
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