Scour at Bridge Piers Protected by the Riprap Sloping Structure: A Review

Bridge piers on large rivers are often protected from scouring using launchable stone, such as a riprap sloping structure. While such scour countermeasures are effective for pier protection, they significantly alter flow conditions in the bridge opening by overtopping flow and flow contraction, deflecting the formation of the scour hole downstream and exposing the downstream riverbed to additional scour. This paper provides a comprehensive and relevant review of bridge scour estimation methods for piers with a riprap sloping structure installed as a scour countermeasure. Research on empirical methods for bridge scour estimation is reviewed and analyzed with formulae used for comparable structures—complex pier formulae and formulae for river training structures. A summary of relevant formulae applicable to piers with installed scour countermeasures is provided, as well as a discussion on the possible future research directions that could contribute to the field.

Advances in Vibration-Based Scour Monitoring for Bridge Foundations

Scouring around bridge foundations is one of the main factors causing structural damage of bridges. Traditional scour monitoring techniques generally require a large number of sensing devices set up underwater, which is difficult to be implemented for actual bridges. To address this issue, scour monitoring technology based on structural vibrations is paid attention gradually, because this technique can work well with less equipment and can be free from the influence of the submerged environment. This study presents a systematic summary and analysis of the selection of scour indicators, sensor deployment principles and other related research involved in scour monitoring technology based on structural vibration. On this basis, the research status of the bridge scour monitoring method based on vehicle excitation is further summarized. Finally, the prospects for the application of vibration-based bridge foundation scour monitoring technology are presented, discussing the technologies that are currently missing and urgently needed for this monitoring method and the challenges faced today.

Methods to detect and measure scour around bridge foundations

Bridges are indispensable structures vital to the operation of road and rail transportation networks. Crossing rivers and artificial waterways, however, presents a risk to their foundations due to scour actions. Scour is the number one cause for bridge failures and may occur beneath any bridge, large or small, with supports located within the waterway. This paper provides a summary of present scour detection and measurement equipment and associated assessment methodologies. In this regard, particular emphasis is placed on structural health monitoring better to evaluate the presence and influence of potential scour. A Sensitivity Analysis on a newly introduced monitoring system is also assumed. Furthermore, much research has been undertaken to create a technology that can instantly identify and detect bridge scour, improving survey reliability through prior inspection and prompt intervention. This research will explore and evaluate bridge scour detection methods employed and suggest a possible path for developing the detection system to identify scour depth effectively and efficiently. Finally, our key aim is to minimize human effort in identifying and bridge scour by using a quick, easy-to-use, cost-effective process, resulting in fewer injuries and economic savings.

Benefits of Compiling and Analyzing Hydraulic-Design Data for Bridges

The hydraulic design of bridges is a discipline that requires a strong measure of engineering judgment. Developing good engineering judgment can take years of experience, and generally increases one project at a time. A supplemental tool that can promote the development of engineering knowledge and judgment is to compile, analyze, and graphically present hydraulic data associated with stream and bridge-design characteristics from previously analyzed bridges. If the data set is sufficiently large, graphs developed from such an effort can provide the engineer with an enhanced picture of stream and bridge-design characteristics, helping them further develop their engineering knowledge and judgment. Furthermore, such graphs can function as project scoping tools and hydraulic-design review tools. Using selected data from approximately 300 bridge-scour studies in South Carolina, previously conducted by the U.S. Geological Survey, and limited hydraulic bridge-design data for approximately 200 bridges in South Carolina, trends in stream and bridge-hydraulic characteristics were evaluated including channel width, floodplain width, flood flow depths, stream slopes, bridge backwater, bridge flow velocity, and bridge lengths. Selected relationships are presented in this paper and should serve as a valuable tool for better understanding stream and bridge-hydraulic characteristics in South Carolina.

Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

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.