Real-Time Fatigue Monitoring of Bridges Using Electrochemical ...

Real-Time Fatigue Monitoring of Bridges

Using Electrochemical Fatigue Sensor (EFS) System

Masoud Malekzadeh, Ph.D. and Sadegh Panahi, Managing Director& COO

Metal Fatigue Solutions, Las Vegas, NV, USA

INTRODUCTION

One of the major and most common challenges to steel bridges’ integrity is fatigue. Long-term repetitive

loading eventually causes the initiation of small cracks in the steel which are commonly referred to as

fatigue cracks. These cracks typically occur in areas where the stresses are higher. Most of these higher

stress areas are known and are referred to as fatigue susceptible locations or fatigue critical locations

(FCLs). As time passes, these cracks gradually grow in size and number until one moment when they

reach a critical size and cause structural failure. Given the aging US highway bridge system, the need for

reliable sensors and monitoring technologies to alert bridge owners when cracks are reaching these critical

lengths has never been greater.The main objective of this study is to introduce and discuss a unique fatigue

sensor developed by Metal Fatigue Solutions, one that has been in commercial use throughout North

America for over 5 years.

The Electrochemical Fatigue Sensor (EFS) System

First introduced in 1992 the Electrochemical Fatigue Sensor (EFS) system has proven itself valuable for

timely detection of growing fatigue cracks – typically well before the unaided eye will spot them. It serves

to:

Define Crack Growth (whether the crack is growing or not?)

How Rapid or Slow is the Growth?

Define the Presence of Micro-Plasticity (in similar details elsewhere on the same bridge)

Assist in Evaluation of Effective Retrofits (did the retrofit stop the crack growth?)

Retrofit Selection (which retrofit is most effective in stopping the crack growth?)

FUNDAMENTAL PRINCIPAL OF EFS The EFS system consists of three main components including an electrolyte-filled sensor, a potentiostat

data link (PDL) that provides a constant voltage between the sensor and the structure and acquires the

data, and a data collection and analysis software. The science behind EFS is grounded in fundamental

electro-chemical principles. The EFS system anodically polarizes the inspection area, through a small

applied voltage, creating a passive film. There are basically two sensors installed near the area of interest

including one for reference (R) and one as the crack measurement (CM) sensor. The CM sensor is

positioned at a location of interest while the R sensor is located next to the CM sensor, where the crack is

not expected. The EFS response signal (current in micro-amps) from the two sensors are collected and

compared in order to identify the possible growing fatigue crack.

Installation of Reference Sensor (R) and Crack Measurement (CM) Sensor

The EFS response signal or current flowing within the cell fluctuates as a function of the mechanical

stress. Therefore, the transient current is monitored and interpreted to determine the fatigue level in the

structure. Once growing cracks are formed within the inspected area, the passive layer is broken down

which in turn produces a change in the EFS signal. This abnormal behavior is detected through real-time

signal processing of transient signal. Basically, the more steel is exposed, the more passive film changes

which indicates fatigue status in the structure. The frequency content and magnitude of the transient signal

(EFS signal) are affected by changes in the passive layer. Currently, live data sets are being transferred

from different sites (infrastructures) to the central office (data center) using virtual private network (VPN)

as it is shown in the following Figure.

Data Transmission Plan for MFS

6

. . . . . . .

Internet

Data Processing Center (Office)

Cloud Storage and Website for data Presentation

Network 1 (Bridge in State A)

Grid Power4G LTE Modem

VPN Router

Network 2 (Bridge in State B)

Grid Power4G LTE Modem

VPN Router

Subsequently, the live stream of data is analyzed using the Fast Fourier Transform (FFT) utilizing

windowing technique to detect any possible phase difference between crack and reference sensor. Once

the crack is detected the next important stage is to identify whether the crack is growing or not. The ratio

between crack sensor and reference sensor in frequency domain (Energy ratio) is used as decision metric

to determine the rate of crack growth. For instance, three measurements form individual locations of a

real-life bridge are presented below which are indicating three different stage of crack growth.

EFS Signal in Time Domain and Signal Domain (Not growing, insignificant growing and crack growths)

The accuracy of the crack detection and rate of crack growth using EFS sensor is compared to the visual

inspection conducted by a third party inspection team and reported in the following table. This beside

several other reports of other agencies indicating the efficacy and accuracy of EFS sensor in identifying

the current condition of the crack.

Next in development, Metal Fatigue Solutions is evaluating the efficiency of the EFS for long-term

monitoring applications. It is organizing three major research projects through three universities to study

different aspects of the long-term application including battery life, continuous data collection, handling

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Energy Ratio =1 Crack Not Growing Energy Ratio =2 Crack Growth is insignificant Energy Ratio =11.4 Crack Growths

large amounts of data sets and algorithm development. For addition information or to discuss this research

report further, please contact Dr. Masoud Malekzadeh at [email protected], or Mr.

Sadegh Panahi at [email protected].