基于深度學(xué)習(xí)的結(jié)構(gòu)位移場(chǎng)預(yù)測(cè)方法

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中圖分類號(hào)：TP181；TP391.92 文獻(xiàn)標(biāo)志碼：B

Structural displacement field prediction method based on deep learning

HU Yezhi' ZHANG Yaqi2 LU Changhong （1.DNE Technology Co.，Ltd.，Shanghai 200030，China；2.Allbright Lawofices（Hefei），Hefei 230001，China； 3.Hefei Hanwang Software Technology Co.，Ltd.，Hefei 23OOO1，China）

Abstract：Based on the basic equations of finite element method，the feature extension layer is embedded into the deep learning model，and a training set generator is developed using Abaqus software interface to achieve single model prediction of the full displacement component of the structure. Using the Keras API under the TensorFlow framework to train a deep learning model for spatial thin shell structures，a quantitative analysis of the prediction performance is conducted. The results show that： the computational efficiency of the deep learning model is significantly improved compared to the simulation model，and the prediction of the maximum displacement and distribution pattern is basically consistent with the simulation results，however there is an increase in error at the O displacement boundary.

Key words： deep learning；model training；displacement field；prediction； spatial thin shell structure

0 引言

將現(xiàn)實(shí)世界的幾何形態(tài)映射到虛擬世界并建立系統(tǒng)進(jìn)行分析管理，是目前數(shù)字孿生研究的主流方向，但這尚未跳出BIM的工作范疇。（剩余8494字）