.png){kind=icon}
.png){kind=icon}
Return
Dynamic Prediction of Deformation of Irregular Aluminum Plate Curtain Wall Structure in Gymnasium Based on Improved RBF Neural Network
.png){kind=icon}
PRE
AI
J
Z
W
Y
DOI：10.1002/eng2.70763.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To address the issues of limited data availability, low accuracy, and weak generalization ability in predicting the deformation of curtain wall structures, a dynamic deformation prediction method for irregular aluminum plate curtain wall structures in gymnasiums based on an improved RBF neural network is proposed. The overall architecture is constructed, deformation data is collected using sensors, and it is processed within the intelligent information management platform using the empirical mode decomposition algorithm. A prediction model is developed using the RBF neural network. The intrinsic mode function components of the decomposed deformation data are used as input. The weak learners are enhanced using the AdaBoost algorithm. The number of hidden layer nodes is optimized based on the data distribution mechanism. The monitoring terminal outputs alarms and generates reports based on the model's output. Experimental results show that this method can effectively extract deformation data features through empirical mode decomposition, obtain the intrinsic mode function components, and achieve an average prediction accuracy of 94.7% for key monitoring nodes of the curtain wall structure. For the maximum deformation node G111, the prediction error is only 0.032 mm. This method can accurately predict the deformation values of each node of the curtain wall structure, thereby identifying the main nodes that cause structural instability.
Keywords:
AdaBoost algorithm
curtain wall structure
deformation prediction
hidden layer nodes
irregular aluminum plate
RBF neural network
Journal
IF:
2
Papers:
362
Citations:
1.7K
Organization
No organization information available
Cited Papers