scholarly journals Remote structural health monitoring and corrosion-rate modeling of steel bridges : final report on Project F08-AR13

2018 ◽  
Author(s):  
Steven Sweeney ◽  
Richard Lampo ◽  
Robert Mason
Keyword(s):  
Structural Health Monitoring ◽  
Corrosion Rate ◽  
Health Monitoring ◽  
Steel Bridges ◽  
Final Report ◽  
Structural Health

scholarly journals Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xueping Fan ◽  
Zhipeng Shang ◽  
Guanghong Yang ◽  
Xiaoxiong Zhao ◽  
Yuefei Liu
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Linear Models ◽  
Steel Bridges ◽  
Steel Bridge ◽  
Structural Health ◽  
Extreme Stress ◽  
Stress Prediction ◽  
Bridge Reliability

In this article, an approach for using structural health monitoring coupled extreme stress data in dynamic extreme stress prediction of steel bridges is presented, where the coupled extreme stress data means the extreme stress data with dynamicity, randomness, and trend. Firstly, the modeling processes about dynamic coupled linear models (DCLM) are provided based on a supposed coupled time series; furthermore, the dynamic probabilistic recursion processes about DCLM are given with Bayes method; secondly, the monitoring dynamic coupled extreme stress data is taken as a time series, historical monitoring coupled extreme stress data-based DCLM and the corresponding Bayesian probabilistic recursion processes are given for predicting bridge extreme stresses; furthermore, the monitoring mechanism is provided for monitoring the prediction precision of DCLM; finally, the monitoring coupled extreme stress data of a steel bridge is used to illustrate the proposed approach which can provide the foundations for bridge reliability prediction and assessment.

Long-term gage reliability for structural health monitoring of steel bridges

2012 ◽  
Author(s):  
Vasilis A. Samaras ◽  
Jeremiah Fasl ◽  
Matt Reichenbach ◽  
Todd Helwig ◽  
Sharon Wood ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Steel Bridges ◽  
Structural Health

scholarly journals An Embedded-Sensor Approach for Concrete Resistivity Measurement in On-Site Corrosion Monitoring: Cell Constants Determination

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2481
Author(s):  
Jose Enrique Ramón ◽  
Isabel Martínez ◽  
José Manuel Gandía-Romero ◽  
Juan Soto
Keyword(s):  
Structural Health Monitoring ◽  
Corrosion Rate ◽  
Health Monitoring ◽  
Resistivity Measurement ◽  
Simple Expression ◽  
Corrosion Monitoring ◽  
Integrated Network ◽  
Structural Health ◽  
Corrosion Sensor ◽  
Concrete Resistivity

The concrete electrical resistivity is a prominent parameter in structural health monitoring, since, along with corrosion potential, it provides relevant qualitative diagnosis of the reinforcement corrosion. This study proposes a simple expression to reliable determine resistivity from the concrete electrical resistance (RE) provided by the corrosion sensor of the Integrated Network of Sensors for Smart Corrosion Monitoring (INESSCOM) we have developed. The novelty here is that distinct from common resistivity sensors, the cell constants obtained by the proposed expression are intended to be valid for any sensor implementation scenario. This was ensured by studying most significant geometrical features of the sensor in a wide set of calibration solutions. This embedded-sensor approach is intended to be applicable for RE measurements obtained both using potential step voltammetry (PSV, used in the INESSCOM sensor for corrosion rate measurement) and alternating current methods. In this regard, we present a simple protocol to reliably determine RE, and therefore resistivity, from PSV measurements. It consists in adding a very short potentiostatic pulse to the original technique. In this way, we are able to easy monitor resistivity along with corrosion rate through a single sensor, an advantage which is not usual in structural health monitoring.

scholarly journals Framework for Structural Health Monitoring of Steel Bridges by Computer Vision

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 700 ◽  
Author(s):  
Adam Marchewka ◽  
Patryk Ziółkowski ◽  
Victor Aguilar-Vidal
Keyword(s):  
Image Processing ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Good Condition ◽  
Route Planning ◽  
Well Being ◽  
Structural Condition ◽  
Steel Bridges ◽  
Structural Health

The monitoring of a structural condition of steel bridges is an important issue. Good condition of infrastructure facilities ensures the safety and economic well-being of society. At the same time, due to the continuous development, rising wealth of the society and socio-economic integration of countries, the number of infrastructural objects is growing. Therefore, there is a need to introduce an easy-to-use and relatively low-cost method of bridge diagnostics. We can achieve these benefits by the use of Unmanned Aerial Vehicle-Based Remote Sensing and Digital Image Processing. In our study, we present a state-of-the-art framework for Structural Health Monitoring of steel bridges that involves literature review on steel bridges health monitoring, drone route planning, image acquisition, identification of visual markers that may indicate a poor condition of the structure and determining the scope of applicability. The presented framework of image processing procedure is suitable for diagnostics of steel truss riveted bridges. In our considerations, we used photographic documentation of the Fitzpatrick Bridge located in Tallassee, Alabama, USA.

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