Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

遍及美国和其他国家/地区的桥冲是一个挑战。尽管问题的规模很大，但仍然仍然缺乏可靠的冲刷预测方法来支持可靠的，基于风险的管理和决策。在过去的十年中，实时冲刷监控系统的使用在美国各州交通运输部门中引起了越来越多的兴趣。本文介绍了三种基于先进人工智能（AI）/机器学习（ML）技术的冲刷预测方法实时冲刷监控数据。冲刷监测数据包括从各种来源收集的桥墩处的河床和河段高程时间序列。深度学习算法早在一周前就可以预测床层高度和水位变化。集成神经网络被证明可以成功预测最大即将到来的冲刷深度，利用冲刷发作开始时观察到的传感器数据，并基于桥墩，水流和河床特征，可以预测出最大的冲刷深度。此外，使用贝叶斯推断方法基于观察到的传感器数据对两个常见的经验冲刷模型进行了校准，显示了预测准确性的显着提高。总体而言，本文通过将新兴的AI / ML算法与实时监控系统集成以进行早期冲刷预测，提出了一种新颖的冲刷风险管理方法。使用贝叶斯推断方法基于观察到的传感器数据对两个常见的经验冲刷模型进行了校准，显示了预测准确性的显着提高。总体而言，本文通过将新兴的AI / ML算法与实时监控系统集成以进行早期冲刷预测，提出了一种新颖的冲刷风险管理方法。使用贝叶斯推断方法基于观察到的传感器数据对两个常见的经验冲刷模型进行了校准，显示了预测准确性的显着提高。总体而言，本文通过将新兴的AI / ML算法与实时监控系统集成以进行早期冲刷预测，提出了一种新颖的冲刷风险管理方法。