Seismic Damage Assessment of Regional RC Frame Structures Based on TCN
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摘要: 为准确评估区域RC框架结构震后损伤状态，提出了基于时序卷积神经网络(Temporal Convolutional Neural Networks，TCN)模型的结构地震损伤评估方法。首先选取几何参数中的结构高度、x向跨度和设计参数中的抗震设防烈度、场地类别作为结构特征参数，设计了48个RC框架结构模型；然后用OpenSees软件计算结构在地震过程中的加速度响应数据，采用最大层间位移角作为结构损伤指标，并建立结构损伤指标与加速度响应数据之间的映射关系，以此得到震损数据集；最后通过建立基于TCN模型的区域RC框架结构震损评估模型，利用贝叶斯优化算法找出模型中的最优参数组合，分析了TCN模型的损伤评估准确率、计算资源及在噪声作用下的泛化能力。研究结果表明，TCN模型损伤评估准确率高达86.6%，评估效果优于CNN-LSTM模型，且具有更少的参数量，在噪声作用下也有较好的鲁棒性。Abstract: To accurately evaluate the post-earthquake damage state of regional reinforced concrete (RC) frame structures, a method based on Temporal Convolutional Neural Networks (TCN) is proposed. First, 48 RC frame structure models were designed by selecting structural height, X-span, seismic fortification intensity, and site category as key geometric parameters. Next, OpenSees software was used to calculate the acceleration response data of the structures during an earthquake. The maximum inter-layer displacement angle was introduced as the structural damage index, and a mapping relationship between this index and the acceleration response data was established to create the seismic damage dataset. A regional RC frame earthquake damage assessment model based on TCN was then developed, and the Bayesian optimization algorithm was employed to identify the optimal parameter combination for the model. The model’s damage assessment accuracy, computational efficiency, and generalization ability under noisy conditions were evaluated. The results show that the TCN model achieves a damage assessment accuracy of 86.6%, outperforming the CNN-LSTM model in terms of accuracy, parameter efficiency, and robustness under noise.
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图 6 CNN-LSTM和TCN模型混淆矩阵对比
Figure 6. Comparison of confusion matrix between CNN-LSTM model and TCN model
表 1 贝叶斯优化参数范围
Table 1. Parameter range of Bayesian optimization
模型名称 参数范围 L2正则化 学习率 卷积核尺寸 卷积块数量 残差块数量 CNN-LSTM [10−4,10−1] [10−4,10−1] [27,35] [1,3] — TCN [10−4,10−1] [10−4,10−1] [3,7] — [5,8] 
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表 2 框架结构模型结构特征参数
Table 2. Structural characteristic parameters of frame structure model
项目 结构特征参数 参数取值范围 几何参数 结构层数/层 4、6、8、10 x向跨度/m 6、8 设计参数 抗震设防烈度 7、8 场地类别 Ⅰ、Ⅱ、Ⅲ
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表 3 不同性能状态下最大层间位移角限值
Table 3. Limits of maximum interlayer displacement angle under different performance states
结构性能状态 轻度损伤
（第1类）中度损伤
（第2类）重度损伤
（第3类）最大层间位移角限值/rad ≤1/200 1/200～1/100 >1/100
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表 4 各数据集中加速度响应数据量
Table 4. Data amount of acceleration response in each data set
项目 训练集 验证集 测试集 地震波/条 12 4 4 数据量/组 18 640 4 460 7 000
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表 5 CNN-LSTM和TCN模型性能对比
Table 5. Comparison of performance between CNN-LSTM model and TCN model
模型名称 参数量/kb 时间/s 最优准确率/% CNN-LSTM 15 380 099 3 042.2 80.4 TCN 12 419 1 946.0 86.6
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