| 引用本文: | 王铁虎,刘平安,欧玉鹏,等.基于LSTM神经网络的渡槽温度场预测[J].灌溉排水学报,2026,45(2):73-81. |
| WANG Tiehu,LIU Pingan,OU Yupeng,et al.基于LSTM神经网络的渡槽温度场预测[J].灌溉排水学报,2026,45(2):73-81. |
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| 摘要: |
| 【目的】准确预测渡槽的离散点温度及内外表面温差，基于长短期记忆神经网络（LSTM）提出一种渡槽温度场预测模型。【方法】以某大跨度简支U形渡槽为依托，基于实测数据分析了渡槽内部温度以及渡槽各部位内外表面温差的变化规律；根据结构内部温度传感器返回的数据建立温度时序数据库，训练神经网络模型实现未来各测点温度及渡槽内外表面温差预测。【结果】渡槽表面及内部温度呈日周期及年周期变化，跨中与支座截面温度在8月达到最高值，分别为45 ℃与40 ℃，1月降至最低值，接近0 ℃与5 ℃；渡槽内部温度达到峰值的时间随着深度的增加而推迟，表面达到最低温度时渡槽出现负温度梯度，表面达到最高温度时渡槽出现正温度梯度；LSTM神经网络预测结果相对CNN及MLP神经网络的平均绝对误差（MAE）更小、决定系数（R2）更接近于1；基于LSTM神经网络的预测温度曲线与实测曲线基本一致，误差不超过1.681 ℃；渡槽各部位内外温差的预测误差值不超过2.22 ℃/m，预测结果准确性较高。【结论】LSTM神经网络预测方法性能优异，可为渡槽未来安全性预测提供参考。 |
| 关键词: 渡槽结构；温度变化；温度场预测；长短期（LSTM）神经网络 |
| DOI：10.13522/j.cnki.ggps.2025174 |
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| A proposed neural network model for predicting temperature distribution in aqueducts |
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WANG Tiehu, LIU Pingan, OU Yupeng, YUE Fei, ZHU Zezhong, ZHANG Xun
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1. Power China Chengdu Engineering Corporation Limited, Chengdu 611130, China；
2. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
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| Abstract: |
| 【Objective】Temperature fluctuations in aqueducts can induce thermal stress and structural deformation, affecting long-term safety and durability. Accurate prediction of temperature and internal-external surface temperature differences at specific points in an aqueduct is critical for structural monitoring and maintenance. This study proposes a model to address these challenges.【Method】The model was based on the long short-term memory (LSTM) neural network and was applied to a large-span simply supported U-section aqueduct. Measured data from the aqueduct were used to analyze the internal temperature variations and temperature differences between internal and external surfaces at different points in the aqueduct. Using the measured spatiotemporal variations of temperature, we trained the LSTM neural network to predict future temperatures at multiple measurement points, as well as the associated internal-external surface temperature differences.【Result】The internal and surface temperatures of the aqueduct showed noticeable daily and annual variations. Temperatures at the middle of the span and the support sections peaked in August at 45 ℃ and 40 ℃, respectively, while reaching the lowest in January, near 0 ℃ and 5 ℃. The time at which the internal temperature peaked was delayed with increasing depth. A negative temperature gradient occurred when the surface temperatures were low, while a positive gradient was observed when the surface temperatures were high. Compared to the measured data, the coefficient of determination of the LSTM model was close to 1; its mean absolute error was also smaller than those calculated from the CNN and MLP neural networks. The maximum errors of the LSTM model for temperature and internal-external surface temperature difference were 1.681 ℃ and 2.220 ℃/m, respectively.【Conclusion】The LSTM-based model can accurately predict temperature distribution and internal-external surface temperature differences in aqueducts. It can be applied for structural monitoring and safety management of flumes. |
| Key words: aqueduct structure; temperature change; temperature prediction; long and short-term (LSTM) neural network |
