School of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel). 2023 May 4;23(9):4480. doi: 10.3390/s23094480.
The safety operation of dams after extreme load is an important frontier research topic in the field of dam engineering. The dam health monitoring provides a reliable data basis for a safety evaluation after extreme loads. This study proposes a novel data-driven fusion model for a dam safety evaluation after extreme load based on monitoring data derived by sensors. First, the relationship between dam environmental quantity and effect quantity is deeply excavated based on bidirectional long short-term memory (BiLSTM) network, which is a deeply improved LSTM model. Aiming at the parameter optimization problem of BiLSTM model, sparrow search algorithm (SSA), which is an advanced optimization algorithm, is integrated. Second, conducting the constructed SSA-BiLSTM model to estimate the change law of dam effect quantity after the extreme load. Finally, the Mann-Whitney U-test theory is introduced to establish the evaluation criterion of the dam safety state. Project case shows that the multiple quantitative prediction accuracy evaluation indicators of the proposed method are significantly superior to the comparison method, with mean absolute percentage error (MAPE) and mean absolute error (MAE) values decreasing by 30.5% and 27.8%, respectively, on average. The proposed model can accurately diagnose the dam safety state after the extreme load compared with on-site inspection results of the engineering department, which provides a new method for dam safety evaluation.
大坝在极端荷载作用下的安全运行是坝工领域的一个重要前沿研究课题。大坝健康监测为极端荷载作用后的安全评价提供了可靠的数据基础。本研究提出了一种基于传感器监测数据的大坝极端荷载后安全评价的新型数据驱动融合模型。首先，基于双向长短期记忆（BiLSTM）网络，对大坝环境量与效应量之间的关系进行了深入挖掘，BiLSTM 是一种经过深度改进的 LSTM 模型。针对 BiLSTM 模型的参数优化问题，将一种先进的优化算法——麻雀搜索算法（SSA）集成到模型中。其次，利用构建的 SSA-BiLSTM 模型对大坝在极端荷载作用后的效应量变化规律进行预测。最后，引入曼-惠特尼 U 检验理论建立大坝安全状态的评价准则。工程案例表明，与对比方法相比，所提方法的多个定量预测精度评价指标均有显著优势，平均情况下，平均绝对百分比误差（MAPE）和平均绝对误差（MAE）值分别降低了 30.5%和 27.8%。与工程部门的现场检测结果相比，所提模型能够更准确地诊断大坝在极端荷载作用后的安全状态，为大坝安全评价提供了一种新方法。
