基于层叠式残差LSTM网络的桥梁非线性地震响应预测

A STACKED RESIDUAL LSTM NETWORK FOR NONLINEAR SEISMIC RESPONSE PREDICTION OF BRIDGES

提出了一种基于层叠式残差长短时记忆神经网络(residual long short-term memory neural network,ResLSTM)的数据驱动建模方法,实现桥梁非线性地震响应预测。该方法利用长短时记忆(long short-term memory, LSTM)网络在长序列回归中的优势,并采用残差连接结构降低深度神经网络中的梯度回传难度,提高了有限数据下的深度网络预测性能。同时,通过采用层叠式序列结构,降低深度神经网络隐藏层节点数目,进一步提升深度神经网络的预测精度。随后,通过两跨预应力混凝土连续梁桥与组合梁斜拉桥的数值算例对该方法进行验证。神经网络的训练样本与测试样本均源自桥梁有限元模型的增量动力分析结果。此外,采用该方法成功预测了美国Meloland Overpass桥的地震响应,并与历史监测数据进行对比验证。结果表明:ResLSTM网络是一种鲁棒性良好、计算效率高的非线性地震响应预测方法,能够利用少量数据快速准确地预测桥梁结构在地震作用下的动力响应,在桥梁抗震性能评价中具有重要的应用潜力。

A data-driven deep learning method using the stacked residual long short-term memory network(ResLSTM)is proposed to predict bridge seismic responses.The proposed method uses the advantage of the Long-Short Term Memory(LSTM)network for long-term sequence regression,and the residual connection structure is used to reduce the difficulty of gradient back-propagation in the deep neural network and to improve the prediction performance of the deep network with limited samples.Meanwhile,a stacked sequence structure is applied to decrease the hidden nodes of the deep neural network,further improving its prediction accuracy.Then,the proposed method is verified by numerical examples of a two-span prestressed concrete continuous girder bridge and a cable-stayed bridge with composite girder.All training and testing samples are taken from the results of incremental dynamic analysis(IDA).In addition,the proposed approach successfully predict the seismic responses of a concrete girder bridge called Meloland Overpass in USA,and the predicted responses are compared with the historical monitoring data.The results indicate that the ResLSTM network is a robust and efficient method for predicting dynamic responses of bridges under earthquake excitations with great potential in fast and accurate evaluation of seismic vulnerability of bridges.