scholarly journals Laboratory Evaluation of Time-Domain Reflectometry for Bridge Scour Measurement: Comparison with the Ultrasonic Method

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Xinbao Yu ◽  
Xiong Yu
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Ultrasonic Method ◽  
Time Domain Reflectometry ◽  
Laboratory Evaluation ◽  
Bridge Scour ◽  
Analysis Algorithm ◽  
Ultrasonic Methods ◽  
Scour Monitoring ◽  
The Time Domain

Bridge scour is a major factor causing instability of bridges crossing waterways. Excessive scour contributes to their high construction and maintenance costs. Design of innovative scour-monitoring instrumentation is essential to ensure the safety of scour-critical bridges. The ability of real-time surveillance is important since the most severe scour typically happens near the peak flood discharge. A new scour-monitoring instrument based on the Time Domain Reflectometry (TDR) principle has been developed to provide real-time monitoring of scour evolution. A framework based on dielectric mixing model has been developed, which can be easily incorporated into an automatic analysis algorithm. This paper introduces a comparative study of TDR method and ultrasonic method for scour measurements. The results indicate that both TDR and ultrasonic methods can accurately estimate scour depth. TDR method, with the developed analysis algorithm, yields information on the river properties such as the electrical conductivity of river water and the density of sediments. TDR methods are also found less influenced by turbulence and air bubbles, both likely to occur during flood events.

New types of time domain reflectometry sensing waveguides for bridge scour monitoring

2017 ◽  
Vol 26 (7) ◽  
pp. 075014 ◽  
Author(s):  
Chih-Ping Lin ◽  
Kai Wang ◽  
Chih-Chung Chung ◽  
Yu-Wen Weng
Keyword(s):  
Time Domain ◽  
Time Domain Reflectometry ◽  
Bridge Scour ◽  
Scour Monitoring

Applicability and limitations of time domain reflectometry bridge scour monitoring system in general field conditions

2020 ◽  
pp. 147592172094458 ◽  
Author(s):  
Kai Wang ◽  
Chih-Ping Lin
Keyword(s):  
Numerical Simulations ◽  
Monitoring System ◽  
Time Domain ◽  
Measurement Range ◽  
Time Domain Reflectometry ◽  
Field Conditions ◽  
Storm Event ◽  
Long Distance ◽  
Scour Monitoring ◽  
The Time Domain

A real-time and durable system for scour process monitoring with sufficient spatial precision is in pressing need for bridge safety management. In light of this, an innovative bundled time domain reflectometry sensing cable was recently proposed to enhance the time domain reflectometry technique for scour monitoring. However, current development only dealt with the construction of bundled sensing cable and the corresponding new data reduction method. Before it can be put into practical use, issues related to the effect of hydrological conditions, long-distance measurement, and actual field implementation are yet to be investigated. This study used both numerical simulations and laboratory experiments to examine the time domain reflectometry signals in response to both scour and deposition in different water-level conditions. As a result, the first guideline of waveform classification and interpretation is newly proposed to validly determine scour depth under various field conditions. Since field measurements often come with significant signal attenuation from resistance loss of long cable and dielectric and conductive loss in the sensing section, numerical simulations and a series of full-scale experiments were also conducted to assess the time domain reflectometry scour measurement range. The maximum measurement range of the latest time domain reflectometry scour sensing cable was found to be about 6 m. Within this range, the maximum error of scour estimation is within 0.2 m. Considering the new findings, a field time domain reflectometry scour monitoring system using the bundled time domain reflectometry sensing cable was designed accordingly and implemented at a bridge for the first time. The monitoring system successfully captured the scour process during a storm event and revealed some practical issues for future improvement as well.

Development and evaluation of an automation algorithm for a time-domain reflectometry bridge scour monitoring system

2011 ◽  
Vol 48 (1) ◽  
pp. 26-35 ◽  
Author(s):  
X. B. Yu ◽  
X. Yu
Keyword(s):  
Monitoring System ◽  
Time Domain ◽  
Monitoring Program ◽  
Suspended Sediments ◽  
Air Entrainment ◽  
Depth Estimation ◽  
Time Domain Reflectometry ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Bridge Foundation

Bridge scour is a major threat to the safety of bridges. There is a high risk of scour-induced damage due to the catastrophic nature of bridge foundation failure. The development of an innovative bridge scour monitoring system is a pressing task for the research community. Such a system needs to be fieldworthy, which is a characteristic assessed in terms of accuracy, ruggedness, and automation. Among these criteria, an automatic signal analysis algorithm is generally a prerequisite for deploying a long-term field monitoring program. This paper describes the development and validation of an algorithm for a scour monitoring system based on the principles of guided radar: time-domain reflectometry (TDR). This algorithm is based on the extension of the classic dielectric mixing model to layered systems. The performance of this algorithm is evaluated using experiments designed to simulate different field scour conditions. These include different types of sediments and the variation of river conditions (i.e., salinity of river water, air entrainment, and amount of suspended sediments). The experiment results indicate that the developed analyses algorithm is robust and accurate for scour-depth estimation under these investigated conditions.

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 A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3521 ◽  
Author(s):  
Funa Zhou ◽  
Po Hu ◽  
Shuai Yang ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Time Domain ◽  
Feature Fusion ◽  
Rotating Machinery ◽  
Time Domain Data ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

scholarly journals CARACTERIZAÇÃO DA DISTRIBUIÇÃO DA ÁGUA NO SOLO PARA IRRIGAÇÃO POR GOTEJAMENTO

Irriga ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 564-577 ◽  
Author(s):  
Leonardo Do Nascimento Lopes ◽  
Elton Martins ◽  
Bruno De Lima Santoro ◽  
Claudinei Fonseca Souza
Keyword(s):  
Water Content ◽  
Time Domain ◽  
Water Distribution ◽  
Water Storage ◽  
Time Domain Reflectometry ◽  
Wetting Front ◽  
Applied Water ◽  
Flow Rates ◽  
Abstract Knowledge ◽  
The Time Domain

CARACTERIZAÇÃO DA DISTRIBUIÇÃO DA ÁGUA NO SOLO PARA IRRIGAÇÃO POR GOTEJAMENTO  Leonardo do Nascimento Lopes1; Elton Martins2; Bruno de Lima Santoro2; Claudinei Fonseca Souza31Universidade de Taubaté, Unitau, Taubaté, SP,  [email protected] Engenharia Civil e Ambiental, Universidade de Taubaté, Taubaté, Unitau, SP 3Departamento de Recursos Naturais e Proteção Ambiental, Universidade Federal São Carlos, São Carlos, SP  1 RESUMO O conhecimento da distribuição da água no solo é de grande importância para a agricultura, uma vez que a água é um dos fatores que mais influenciam o rendimento das culturas. Existem muitas técnicas utilizadas para o monitoramento do conteúdo de água do solo, a reflectometria domínio do tempo (TDR) tem sido difundida entre os pesquisadores por apresentar várias vantagens, entre as quais a determinação em tempo real e a possibilidade de leituras automatizadas. O principal objetivo desta pesquisa foi avaliar a distribuição da água no perfil de um Latossolo Vermelho-Amarelo. Sondas de Reflectometria no domínio do Tempo (TDR) foram utilizadas para monitorar a distribuição de água no solo aplicada através de gotejadores de fluxo constante nas taxas de 2, 4 e 8 Lh-1. Considerando os resultados de diferentes perfis, observa-se um maior armazenamento da água próximo do gotejador diminuindo progressivamente para frente de molhamento. Aproximadamente, um terço da água aplicada (33%) foi armazenado na primeira camada (0-0,10 m) para todos os ensaios. Comparando diferentes taxas de aplicação, observa-se maior armazenamento de água para o gotejador de 8L h-1, com 30, 33 e 34% de água aplicada acumulada na primeira camada (0-0.10 m) para gotejadores de 2, 4 e 8L h-1, respectivamente. Os resultados sugerem que, com base no volume e frequência utilizada neste experimento, seria vantajoso aplicar pequenas quantidades de água em intervalos mais frequentes para reduzir perdas por percolação. UNITERMOS: TDR, conteúdo de água, bulbo molhado  LOPES, L. N.; MARTINS, E.; SANTORO, B. L.; SOUZA, C. F.WATER DISTRIBUTION CHARACTERIZATION IN SOIL FOR DRIP IRRIGATION   2 ABSTRACT Knowledge of water distribution in soil is of great importance to agriculture, since water is one of the factors that most influence the yield of crops. There are many techniques used to monitor soil water content. The time domain reflectometry (TDR) has been widespread among researchers because it presents several advantages, among which the determination in real time and possibility of automated readings. The main goal of this research was to evaluatethe water distribution in a profile of Red-Yellow Oxisol. Time domain reflectometry (TDR) probes were used to monitor the water distribution from drippers discharging at constant flow rates of 2, 4 and 8 Lh-1 in soil. Considering results from different profiles, we observed greater water storage near the dripper decreasing gradually towards the wetting front. About one third of the applied water (33%) was stored in the first layer (0-0.10 m) for all experiments. Comparing different dripper flow rates, we observed higher water storage for 8 L h-1, with 30, 33 and 34% of applied water accumulating in the first layer (0-0.10m) for dripper flow rates of 2, 4 and 8 L h-1, respectively. The results suggest that based on the volume and frequency used in this experiment, it would be advantageous to apply small amounts of water at more frequent intervals to reduce deep percolation losses of applied water. KEYWORDS: TDR, water content, wetted soil volume

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
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