Abstract
Scour around bridge piers is a critical challenge for infrastructures around the world. In the absence of analytical models and due to the complexity of the scour process, it is difficult for current empirical methods to achieve accurate predictions. In this paper, we exploit the power of deep learning algorithms to forecast the scour depth variations around bridge piers based on historical sensor monitoring data, including riverbed elevation, flow elevation, and flow velocity. We investigated the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for real-time scour forecasting using data collected from bridges in Alaska and Oregon from 2006 to 2021. The LSTM models achieved mean absolute error (MAE) ranging from 0.1m to 0.5m for predicting bed level variations a week in advance, showing a reasonable performance. The Fully Convolutional Network (FCN) variant of CNN outperformed other CNN configurations, showing a comparable performance to LSTMs with significantly lower computational costs. We explored various innovative random-search heuristics for hyperparameter tuning and model optimisation which resulted in reduced computational cost compared to grid-search method. The impact of different combinations of sensor features on scour prediction showed the significance of the historical time series of scour for predicting upcoming events. Overall, this study provides a greater understanding of the potential of Deep Learning (DL) for real-time scour forecasting and early warning in bridges with diverse scour and flow characteristics including riverine and tidal/coastal bridges.
Abstract (translated)
在世界各地的基础设施中，清理桥墩是一个关键的挑战。缺乏分析模型以及由于侵蚀过程的复杂性，当前的实证方法很难实现准确的预测。在本文中，我们利用深度学习算法的优势来预测基于历史传感器监测数据桥墩周围的侵蚀深度变化，包括河床高度、流速和流深。我们还研究了使用2006年至2021年阿拉斯加和俄勒冈州桥梁收集的数据来预测实时侵蚀预测的LSTM和卷积神经网络模型的性能。LSTM模型的预测床面变化平均绝对误差（MAE）在提前一周预测时从0.1米到0.5米，表现出相当不错的性能。全卷积网络（FCN）变体在其他CNN配置中表现优异，与LSTM模型的性能相当，但计算成本较低。我们研究了各种创新随机搜索策略进行超参数调整和模型优化，从而使计算成本比网格搜索方法降低。不同传感器特征组合对侵蚀预测的影响表明了历史侵蚀时间序列对于预测即将发生事件的显著性。总体而言，本研究为深入理解DL在具有多样scour和flow特性的桥梁上的实时侵蚀预测和预警提供了更大的认识。
URL
https://arxiv.org/abs/2404.16549
