Seismic isolation bearing settlement recognition based on multi-input convolutional neural network
In order to avoid the settlement of seismic isolation bearings caused by uneven foundation settlement and the hidden damage to the superstructure,a vibration signal identification model based on multi-input convolutional neural network(MI-CNN)is proposed to identify the settlement of seismic isolation bearings.First,the horizontal acceleration and displacement signals of seismic isolation bearings are collected,and the samples are expanded using normalised pre-processing and data enhancement methods.Then,the samples are fed into the established network model and trained.Finally,the settlement identification is performed using the trained network model.The results show that compared with the traditional single-input CNN model,the MI-CNN model is easier to train and can maximise the ability of CNN to extract features from the settlement signals,and it has a better accuracy in identifying the settlement location,a smaller error in identifying the settlement degree,and a more stable identification effect for the unbalanced data set.The results of this study can provide new ideas for the settlement identification of seismic isolation bearings.
万方数据
维普
