scholarly journals Assessing the suitability of bridge-scour-monitoring devices

2021 ◽  
pp. 1-13
Author(s):  
Paul J Vardanega ◽  
Gianna Gavriel ◽  
Maria Pregnolato
Keyword(s):  
Bridge Scour ◽  
Scour Monitoring ◽  
Monitoring Devices

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

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.

scholarly journals Web-based real time bridge scour monitoring system for disaster management

2014 ◽  
Vol 9 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Mirosław Skibniewski ◽  
Hui-Ping Tserng ◽  
Shen-Haw Ju ◽  
Chung-Wei Feng ◽  
Chih-Ting Lin ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Disaster Management ◽  
Bridge Scour ◽  
Web Based ◽  
Scour Monitoring

A Preliminary Assessment of Low-Cost Bridge Scour Monitoring Methods and Tools

2021 ◽  
Author(s):  
Eftychia Koursari ◽  
Stuart Wallace ◽  
Panagiotis Michalis ◽  
Manousos Valyrakis ◽  
Scott Paton
Keyword(s):  
Climate Change ◽  
Low Cost ◽  
Scour Depth ◽  
Bridge Scour ◽  
Monitoring Methods ◽  
Preliminary Assessment ◽  
Medium Scale ◽  
Scour Monitoring ◽  
Surface Shaping ◽  
Scale Structures

<p>Scour is a major cause of bridge collapse worldwide.</p><p>Climate change has resulted in flood events increasing both in frequency and in magnitude. Climate change, together with the current uncertainty about maximum scour depth around structures, make scour and other hydraulic actions some of the most important challenges for engineering going forward.</p><p>This study offers a preliminary assessment of bridge scour monitoring methods considering scour as a dynamical earth surface shaping process, and discusses how these methods can be used to improve predictive models for bridge scour depth.</p><p>Current methods used to monitor scour are mostly reactive. A vast amount of research has been carried out, aiming towards the implementation of various approaches to assist in the monitoring of scour; however, most methods used are either still reactive, or extremely costly and therefore not practical to be used for small to medium scale structures. This study aims in addressing major challenges faced by establishing a new, innovative framework for the monitoring of scour, while considering relevant approaches in literature. It discusses the development of an innovative, sustainable and low-cost framework, that can be used for small to medium scale structures. This will ensure a proactive response in the event of catastrophic scour occurring, safeguarding infrastructure and the travelling public.</p>

scholarly journals Using Bayesian networks for the assessment of underwater scour for road and railway bridges

2020 ◽  
pp. 147592172095657
Author(s):  
Andrea Maroni ◽  
Enrico Tubaldi ◽  
Dimitri V Val ◽  
Hazel McDonald ◽  
Daniele Zonta
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
River Flow ◽  
Flow Characteristics ◽  
The United States ◽  
Monitoring Systems ◽  
Scour Depth ◽  
Bridge Scour ◽  
Railway Bridges ◽  
Scour Monitoring

Flood-induced scour is among the most common external causes of bridge failures worldwide. In the United States, scour is the cause of 22 bridges fails every year, whereas in the UK, it contributed significantly to the 138 collapses of bridges in the last century. Scour assessments are currently based on visual inspections, which are time-consuming and expensive. Nowadays, sensor and communication technologies offer the possibility to assess in real time the scour depth at critical bridge locations; yet, monitoring an entire infrastructure network is not economically feasible. A way to overcome this limitation is to instal scour monitoring systems at critical bridge locations, and then extend the piece of information gained to the other assets exploiting the correlations present in the system. In this article, we propose a scour hazard model for road and railway bridge scour management that utilises information from a limited number of scour monitoring systems to achieve a more confined estimate of the scour risk for a bridge network. A Bayesian network is used to describe the conditional dependencies among the involved random variables and to update the scour depth distribution using data from monitoring of scour and river flow characteristics. This study constitutes the first application of Bayesian networks to bridge scour risk assessment. The proposed probabilistic framework is applied to a case study consisting of several road bridges in Scotland. The bridges cross the same river, and only one of them is instrumented with a scour monitoring system. It is demonstrated how the Bayesian network approach allows to significantly reduce the uncertainty in the scour depth at unmonitored bridges.

Warning Time–Based Framework for Bridge Scour Monitoring

2020 ◽  
Vol 25 (7) ◽  
pp. 04020098
Author(s):  
Fae Azhari ◽  
Kenneth J. Loh
Keyword(s):  
Bridge Scour ◽  
Scour Monitoring ◽  
Warning Time

Real-time TDR Field Bridge Scour Monitoring System

2013 ◽  
Author(s):  
Junliang Tao ◽  
Xinbao Yu ◽  
Xiong (Bill) Yu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Bridge Scour ◽  
Scour Monitoring

UAV-based smart rock localization for bridge scour monitoring

2021 ◽  
Author(s):  
Haibin Zhang ◽  
Zhaochao Li ◽  
Genda Chen ◽  
Alec Reven ◽  
Buddy Scharfenberg ◽  
...  
Keyword(s):  
Bridge Scour ◽  
Scour Monitoring
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