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Monitoring fatigue cracks on eyebars of steel bridges using acoustic emission: A case study
Engineering Fracture Mechanics 211 (2019) 198–208
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Engineering Fracture Mechanics journal homepage: www.elsevier.com/locate/engfracmech
Monitoring fatigue cracks on eyebars of steel bridges using acoustic emission: A case study
T
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Wael A. Megida,b, , Marc-André Chaineyc, Paul Lebrund, D. Robert Haya a
TISEC Inc., Canada Faculty of Engineering, Menoufia University, Egypt Dillon Consulting Limited, Canada d Public Services and Procurement, Canada b c
A R T IC LE I N F O
ABS TRA CT
Keywords: Acoustic emission Eyebar Fatigue crack Kaiser effect Structure health monitoring
A cost-effective inspection procedure was conducted using Acoustic Emission (AE) monitoring to detect fatigue crack initiation and/or growth and to assess the crack activity in eyebars of a steel bridge. An eyebar is a straight steel bar with an eye shape hole at each end for fixing to other components. Eyebars are used in settings in which only tension is applied. Multiple AE sensors were installed on critical eyebars of a bridge. These eyebars were selected based on the bridge maintenance history and field inspection reports. The AE characteristics and source localization were dynamically monitored and recorded on-site in real-time under the impact of normal traffic operation and controlled loading test. In the loading test, a loading truck ran at different speeds and stopped at critical floor beams. The parameters of AE signal such as counts, amplitude, duration, rise time, and energy were analyzed. Analysis of the Kaiser effect for AE activities was performed to confirm or deny the initiation or propagation of possible fatigue cracks. The results of AE monitoring showed that AE is a very reliable technology for confirm or denying the fatigue cracks initiation and assessing the condition of existing cracks and is a cost-effective component in Structure Health Monitoring (SHM).
1. Introduction Steel bridges are crucial components of a healthy and productive transportation infrastructure with an ever-increasing demand for ensuring integrity and performance. Defects such as cracks and plastic deformation in steel bridge structural components may have originated during the fabrication process, creating sites for the initiation and propagation of cracks due to load demand. These cracks can be initiated and grow by a combination of factors including traffic loading leading to component fatigue, accidental and environmental loads, corrosion, upgrading the bridge to carry heavier traffic, aging beyond their original design life and poor detail design [1,2]. Fatigue and fracture are two states of steel bridges least understood in terms of design, inspection, and especially for repair and retrofit. Although a multitude of research and case studies of fatigue damage and steel bridge cracking exist in the literature, only a limited number of professional engineering short courses are offered in this topic and few reference manuals are available to practitioners. As a result, bridge owners and their consultants have to develop their own strategies. Unfortunately, experience has shown that some implemented repairs or retrofits have actually made the conditions worse due to the lack of understanding of the
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Corresponding author. E-mail address: [email protected] (W.A. Megid).
https://doi.org/10.1016/j.engfracmech.2019.02.022 Received 29 November 2018; Received in revised form 11 February 2019; Accepted 15 February 2019 Available online 19 February 2019 0013-7944/ Crown Copyright © 2019 Published by Elsevier Ltd. All rights reserved.
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development of fatigue cracks and how best to address it [3,4]. This potential deterioration requires periodic inspection and bridge evaluation in terms of structural health, integrity, safety, and proper functioning. The unpredictability introduces a degree of uncertainty in planning the frequency of inspection and repair or replacement of the affected structural parts to minimize traffic interruption and operating costs [5]. Consequently, a method of early detection of fatigue cracks is essential. The inspection process and techniques should insure that only the necessary repairs are executed and that additional flaws are not introduced and consider size and inaccessibility. 2. Background The primary structural Non-Destructive Testing (NDT) method for steel bridges is visual inspection. It is often the only economically viable method but its effectiveness depends upon well-trained and motivated inspectors and may be hindered by a lack of accessibility of many critical structural areas. Conventional NDT methods such as dye-penetrant and magnetic-particle testing as well as radiography and ultrasound enable detection and sizing of certain types of defects but require close physical access and basically detect geometric discontinuities [1]. Some discontinuities may be growing flaws that could lead to impaired operation of the bridge or failure, but many are simply benign, stable discontinuities that may never cause problems such as original fabrication defects with no active growth. The only way to separate growing from non-growing flaws through conventional NDT is to periodically re-inspect the flaw to detect size changes. In addition, foil crack gauge or an optical technique can be used. These approaches may be ineffective or expensive. Moreover, a substantial growth of the defect may be required for conclusive results. Among all NDT methods, only Acoustic Emission (AE) responds primarily to the flaws initiation as well as the growth and time varying behavior of the existing flaw. The notable advantage of AE is that large structures may be monitored with relatively few fixed sensors and less expensive operator costs in terms of inspection time. AE also has the potential for achieving a cost-effective inspection for relatively inaccessible areas [1]. AE alone can only locate defects, but cannot give information for their size and shape. The degradation conditions of structures are usually associated with crack growth, plastic deformation and corrosion. However, they may also include rubbing and scoring of surfaces as joints loosen or subsidence occurs [6]. Structural condition monitoring is referred to Structural Health Monitoring (SHM) and is an emerging field in which there is a strong interest by the end user community to assure continued safe operation in the cost-effective manner. AE has been successfully developed for testing materials, components and structures [6]. The knowledge of signal processing, AE sources, and wave propagation have improved since the work done by Kaiser in 1950 [7–9]. In addition, AE procedures have been refined and standardized [10–12]. These procedures usually require overstressing the material beyond its normal working load [13] which can easily be arranged for loaded structures [14]. Maintaining the safety and capacity of a steel bridge to ensure uninterrupted traffic operation while controlling repair and refurbishment costs is essential. In maintaining this balancing act, the steel bridge engineer relies on engineering data from structural analysis, load history, strain measurement and periodic inspection reports. These combined data still may be insufficient to make a decision to repair a bridge member or to replace a span. In many instances of fatigue cracks in bridges, the cracks propagate only a certain distance and then become dormant due to stress relaxation [2]. However, when crack propagation continues, it becomes a judgment call as to whether, or how soon, funds be allocated for repair or replacement. AE monitoring can provide the bridge engineer with this additional information. In the recent years, AE technology has been getting more widely used in laboratory investigations [15–21] as well as in active crack detection of bridge structures [22–25]. AE technology was used to monitor dynamic process of crack growth of a segment model of a full size orthotropic steel bridge structure during a 30 day fatigue test [19]. The results have shown that AE is a very valuable technology for monitoring dynamic process of crack development. It can be used not only for various tension, compression and fatigue tests in labs, but also for SHM in the fields. A comparison between piezoelectric paint sensors and commercial AE sensors was conducted during monitoring and evaluating the full-scale orthotropic steel bridge deck under cyclic loading [20]. AE monitoring results showed that typical genuine AE signals can be collected by piezoelectric paint sensor as well as commercial AE sensor under the same cyclic loading. An advanced AE system with accurate source location was used to monitor fatigue crack propagation in steel and welded steel compact tension and T-section girder test specimens [21]. It was concluded that it may be possible to predict the remaining service life of fatigue damaged structures from the results of short term AE monitoring. After the discovery of a significant crack in an eyebar on the San Francisco-Oakland Bay Bridge in California, remote AE monitoring was explored to provide the greatest possible safeguards for the some 200,000 vehicles that used the bridge daily [22]. It was found that AE monitoring allowed the detection and localization of crack initiation and growth in real time. Therefore, the AE was being used to continuously monitor 384 eyebars with 640 AE sensors. This study mainly aims to demonstrate that AE can provide an important contribution to steel bridge owners and their consultants. This contribution can be addressed as AE can provide unique positive or negative insights into damage processes. Thereby, AE can reduce the cost of the bridge maintenance by eliminating the unnecessary repairs. Moreover, AE can provide early warnings of severe or sudden failures such as fatigue cracks that they usually occur with very insignificant alerts. AE can detect the accumulation of microdamage inside components, especially under service conditions. By addressing these issues, it is believed that the data presented here can improve the effectiveness of AE technique for SHM of civil infrastructures such as bridges in cost-effective manner. 3. AE approach Structures under stress produce acoustic stress waves that a human ear may not always be able to hear. The phenomenon of acoustic stress waves generation in structures under stress is called AE. It is basically the generation and propagation of stress waves in materials due to the effects of stress such as deformation, initiation and crack growth, opening and closing of a crack, diffusion and 199
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Fig. 1. Parameters of AE signals [48].
movement of a dislocation, twining and phase transformation [1]. In addition to these sources, AE waves can be generated even during plastic deformation of the material in a highly stressed zone adjacent to the tip of the crack. In composite materials, fracture and de-lamination are considered sources of AE. In general, the sources of AE are predominantly damage-related [5]. The stress waves propagate creating AE signals detected by remote sensors that convert them into a useable electric signals [1]. Different mechanisms are responsible for AE generation in different materials [26]. During tensile deformation in a ductile material such as steel, maximum AE activity was found to be generated near the yield point [27]. Uniaxial tests were performed on steel specimens and the total duration of the test was divided into four stages; the micro-plastic deformation stage, the yielding stage, the strain hardening stage, and the necking and fracture stage [28]. It was found that the most high energy AE signals were generated during the two initial stages. The same conclusion was reported after studying the tensile deformation of annealed and cold-worked AISI 304 stainless steel [29]. When a steel bridge is subjected to repeated load cycles, discontinuities and material imperfections in load bearing members may initiate fatigue cracks and release a number of emissions. The rate of crack growth depends on a number of factors including both the load magnitude and frequency as well as the current crack length [30,31]. Basic principles involved in measurement of AE are the changes in the parameters of a wave signal through a medium. These changes in signals are amplified with the help of resonant piezoelectric devices. Two broad approaches can be identified for analysis of recorded AE data: parameter- and waveform-based approaches [26]. In parameter-based approach, only some of the parameters of the AE signal are recorded, but the signal itself is not recorded. This minimizes the amount of data stored and enables fast data recording. The signal parameters are used to assess the extent of damage. A burst detected by the AE system is known as AE hit where a signal exceeds the threshold and causes a system channel to accumulate data, thereby describing an AE event. Event rate is the number of events/hits per time. By measuring the signal parameters, shown in Fig. 1, such as counts, amplitude, duration, rise time, and energy (the area under the rectified signal envelope), a multitude of quantitative information on the magnitude of material defects, location and time of their origination, and the rate of their propagation can be obtained. These signal related parameters and the hit description including the external parameters such as the value of the applied load, the time of detection, the fatigue cycle count and the level of continuous background noise at the time of detection are essential for AE data analysis [5]. Number of hits and number of counts can be used to quantify an AE activity. The energy is often preferred to interpret the magnitude of source event over counts as it is sensitive to both amplitude and duration, and less dependent on the voltage threshold and operating frequencies [32]. Other useful parameters include average frequency (calculated by dividing counts by duration) and RA (Rise-time divided by Amplitude), which can be used to sort signals from tensile and shear cracks [32]. In waveform-based approach, the signal itself is recorded. The higher computing resources are required for this approach, to perform quick data acquisition and record complete waveforms [33]. This approach can allow the use of signal processing techniques and signal-noise discrimination [34]. Also, correlations with corresponding time and/or load readings enable discrimination between real and false signals (noise) and assists in assessing the significance of the source with respect to structural integrity of the member under examination [1]. Locating the source of significant AE is an essential goal of an inspection. With an array of resonant AE sensors, using the arrival
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times at each sensor in the array, the source can be located spatially [35]. By properly locating the sensors in this manner, it is possible to inspect an entire structure with relatively few sensors. AE sensors are used in linear or planar arrays. In linear defect location, two AE sensors are installed on the test specimen. The difference in the arrival times of the AE wave at the two sensors are used to locate the defect in relation to their positions. In planar defect location, more than two AE sensors are used where the defect is located in 2D-plan. A single sensor can be used per monitoring location to detect the AE activities without locating the source. 4. Bridge-AE monitoring overview Steel bridge members, that are monitored using AE approach, are often identified through routine periodic visual inspection. However, additional inputs to select candidate locations include bridge engineering knowledge, records of past maintenance performed on the bridge, and engineering judgment. These include the formal categorization of fracture-critical locations and experience with a specific types of bridges for which a bridge engineer has responsibility and bridge-specific experience including inspection and in-service bridge history. A combination of these resources is generally used to select the sites. Typically, the steel bridge members are susceptible to fatigue crack initiation and eventual failure due to fracture that receive stress above the threshold stress range for a designated fatigue detail type (A to E’) [2]. For railroad bridges that are normally subject to high live loads relative to their design loads, loading is provided by normal rail traffic. The design loads reflect the worst loading that can be caused on the bridge by traffic. For highway bridges, normal traffic can be used. In addition, proof loads with a loading truck have been used to apply static and a variety of dynamic loads [36,37]. Also, traffic can be managed for stopping, accumulating, and proceeding in specific lanes. A combination of regular traffic, controlled loading and traffic management can be utilized to accurately capture the maximum load effect of the structure. Guidelines [38] for AE testing under controlled stimulation can be adapted to bridge inspection. AE activity is defined by the relationship between a stimulus that is conventionally a controlled monotonically increasing load and an activity metric such as AE counts. Under normal bridge traffic load, the stimulus is the number of fatigue cycles introduced by the passing traffic. The correlations between strain and AE provide a basis for assessing the AE activities using the fatigue cycles as the stimulus [25]. The AE activities are recorded and the intensity is determined to generate an AE source index (AEI). This is further complemented with information from other NDT testing information to provide series of recommendations for follow-up such as scheduling the next inspection or performing immediate repair [25]. 5. Case study bridge An investigation was conducted on Alexandra bridge to determine the feasibility of detecting fatigue crack initiation and/or propagation on steel eyebars using AE monitoring. Alexandra bridge (Fig. 2) is a 574.8 m-long steel truss bridge. The bridge provides an important commuter link between Ottawa and Gatineau, Canada. Single traffic lanes are located on the centre and east decks. The west deck is used by cyclists and pedestrians. The bridge was constructed by the Canadian Pacific Railway in the period from 1898 to 1901 using over 600 individual eyebars in the construction of the trusses. The bridge consists of a north approach trestle, two simply supported trusses, a 320.0 m long three span cantilevered truss bridge with a 169.4 m long center span, and a south trestle. The
Eyebar
Fig. 2. Alexandra Bridge, Ontario/Quebec, Canada (top) and eyebar (bottom). 201
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Table 1 AE monitoring program. Loads
Deck
Bridge members
Phase1
Session1 Session2
Normal Traffic Loading Truck Normal Traffic
Center and East Center East
16 eyebars
Phase2
Session1 Session2
Normal Traffic Loading Truck
Center and East
3 eyebars + 2 pins
Phase3
Session1 Session2
Normal Traffic Loading Truck
Center and East
3 eyebars
bridge's main cantilever centre span was, at the time of construction, the longest in Canada and the fourth longest in the world. 6. AE monitoring program The AE monitoring program is presented in Table 1. The monitoring program was conducted in three phases. In Phase1, AE monitoring was conducted to search and detect possible fatigue crack on eyebars at critical areas of the bridge. In Phase2, AE was used to monitor in details the sources of AE activities recorded in Phase1 and to monitor the potential growth of fatigue crack. In Phase3, AE was used to monitor the growth of fatigue cracks indications detected visually and confirmed by Magnetic Particles Inspection (MPI). The process of MPI put a magnetic field into the eyebar. It was indirectly magnetized by applying a magnetic field from an outside source. The presence of a surface or subsurface discontinuity in the eyebar such as cracks allows the magnetic flux to leak, since air cannot support as much magnetic field per unit volume as metals. To identify a leak, dry ferrous particles were applied to the eyebar. Sixteen eyebars were monitored in Phase1. Three eyebars and two pins were monitored in Phase2. Three eyebars were monitored in Phase3. Each phase consisted of two sessions. In Session1, the bridge members were monitored under the impact of normal traffic running on the center and east decks. However in Session2, the bridge members were monitored under the action of a loading truck (60 tonnes) running on the center and east decks. This was not the case in Phase1-Session2 where the east deck was opened for traffic while the loading truck was running on the center deck. In loading tests, the loading truck ran at different speeds as well as stopping at critical floor beams (FB). The sequence of loading test conducted in Phase2 is listed in Table 2. A Vallen AMSY4 (16 channel) digital AE monitoring system was employed. A National Instrument SC-2043-SG 8-channel strain gauge signal conditioning was incorporated into the parametric channels of the AE system. It was equipped with Vallen VS375-RIC, 375 kHz AE sensors, with built-in preamplifiers. The logging settings of AE channels are reported in Table 3. 7. Identifying possible crack locations The bridge was constructed using over 600 individual eyebars. A cost-effective approach was required to scan critical eyebars where fatigue cracks are likely without interrupting the bridge operation. Among NDT approaches, the developed AE approach was expected to be ideal for this study and to demonstrate the feasibility of monitoring the entire bridge. AE activity in terms of number of hits and related relative strain of sixteen eyebars were recorded in Phase1 to search for and detect possible fatigue crack initiation or propagation activity. These eyebars were selected as critical areas of the bridge based on the bridge maintenance history and field inspection reports. All monitored eyebars are on the main cantilever centre span of the east truss at the bottom chord level. The east truss of the structure is subjected to more load than the west truss because vehicular traffic is running on the center and east decks only. The AE monitoring system was setup to check for AE activity while the bridge was dynamically loaded with regular traffic load and/or during a loading test for four days and half day, respectively. Around 15,000 vehicles use the bridge daily. The AE monitoring Table 2 Loading test sequence of Phase2.
Slow speed Fast speed Slow speed with Stopping Slow speed Slow speed Fast speed
Trip
Deck
Direction
T1 T2 T3 T4 T5 T6 T7 T8
Center
North South North South North South North South
to to to to to to to to
South North South North South North South North
T9 T10 T11 T12
East
North South North South
to to to to
South North South North
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Table 3 AE channels settings. Threshold Gain Range Rearm time
40 dB 34 dB 10 Vpp 3.2 ms
Duration discrimination time Pretriggre samples Sampling rate Bandwidth of AE filter
400 µs 200 40 MHz 250–700 kHz
was performed in Phase1 using a single AE inspection sensor per location. According to the bridge maintenance history, field inspection reports, and typical eyebar construction and defects, it was concluded that more attention should be given to the integrity of the members at the pinned connections. Therefore, all the sensors were installed on the eyebars close to the pinned connections as shown in Fig. 3-left. The details of a pinned connection is shown in Fig. 3-right. After removing the paint, the sensor was coupled to the eyebar surface using ultrasonic couplant and fixed using a magnetic holder as shown in Fig. 3-left. The AE system verification check was performed using the lead-break standardization process [39,40]. The temperature and relative strain were also measured and recorded in real-time. One strain gauge was installed on each selected eyebar. The results of AE monitoring for the sixteen eyebars (EB1 to EB16) are plotted in Fig. 4. The results indicated that EB2, EB7, and EB16 had the higher number of AE hits. This observation demonstrated that there is a higher potential for fatigue crack initiation or propagation on these three eyebars compared with the rest of the eyebars where less AE hits were recorded. EB7 is located at FB18 which in turn is found to be at the midpoint of the main cantilever centre span as shown in Fig. 2. EB2 and EB16 are located at FB14 and FB22, respectively, which in turn are found to be the articulated supports of the centre part of the main cantilever centre span as shown in Fig. 2. Thus, it was found that EB2, EB7, and EB16 are located at relatively critical loading areas on the bridge and were selected to receive a detailed AE monitoring in Phase2. For further integrity assessment of the pinned connections at FB14 and FB22 and taking advantage of the linear locating capabilities of AE, pins P14 and P22 were also selected to be monitored in Phase2.
8. Confirming AE source type and location In the initial program of Phase2, EB2, EB7, and EB16 associated with pins P14 and P22 were selected to perform AE source location to localize the sources and confirm if there is any possible crack initiation, propagation or any other type of source such as fretting, rubbing or thermal impact between members of pinned joints. The members of steel bridges can exhibit thermal expansion or contraction resulting from the daily or seasonally changes in the temperature. That can lead to release of stress waves at the pinned joints. The AE monitoring system was setup in Phase2 to check for AE activity in terms of events while the bridge was dynamically loaded with regular traffic load as well as during a loading test. Based on the AE data of P22 recorded under the impact of normal traffic (Fig. 5-b), it was recommend to monitor EB17 instead of EB16. This is discussed in detail below. Two AE sensors were used to monitor each pin of the 21 in. long pins. One sensor was installed on each pin end as shown in Fig. 3right. After removing the paint, the sensor was coupled to the pin surface using ultrasonic couplant and fixed using a magnetic holder. Acoustic coupling of all eyebars connected to the pin at the given eyebar cluster was confirmed through the lead-break standardization process. This configuration allows the system to indicate the eyebar with AE activity through correlation of the eyebar position with the linear location determined by AE monitoring. The results of AE monitoring for P14 conducted in Phase2 is plotted in Fig. 5left. During the normal traffic operation, the results indicated that the majority of AE events (15 of 19 events in total) were recorded and localized at the same location as EB2 as indicated in Fig. 5a. During the loading test, the same conclusion was confirmed where all the AE events (total of 5) were recorded at the same location of EB2 as shown in Fig. 5c. At this outcome, there were three causes for the source of these AE events. Cause1, the pin itself may have a wearing defect or fatigue crack. Therefore, an ultrasonic testing was performed on the P14. The ultrasonic results indicated that the pin was in good condition. Cause2, those AE events may be resulted from the growth of fatigue crack on EB2. Cause3, the AE source may be due to fretting or rubbing between EB2 and P14.
Fig. 3. AE sensor installed on EB6 at FB18 (left) and details of pinned connection at FB14 and FB22 (right), inch = 2.54 cm. 203
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Fig. 4. AE hits per eyebar recorded in Phase1.
Fig. 5. AE source location for P14 (left) and P22 (right) during the normal traffic operation and loading test of Phase2, in. = 2.54 cm.
To confirm the type of AE source and to make a distinction between Cause2 and Cause3, four AE sensors were installed to perform source location on EB2. The AE sensors were installed on EB2 around P14, as shown in Fig. 6-left. After removing the paint, the sensors were coupled to the eyebar surface using ultrasonic couplant and fixed using magnetic holders. After AE system verification check, the results of AE monitoring for EB2 during the loading test are plotted in Fig. 6-left where four AE events in total were recorded over one hour. During the loading test conducted in Phase2, the loading truck passed eight times on the centre deck and four times on the east deck with total of twelve trips over the bridge as shown in Table 2. This can be clearly noted in Fig. 6-right where the strain values recorded in the last four trips are nearly twice the strain values recorded in the first eight trips. This is because all the loads on the east deck are carried by the east truss but it carries only half of the loads on the centre deck. It was found that the four AE events were recorded at low load/strain only. The Kaiser effect is very useful phenomenon in AE monitoring [41,42]. The AE source related to the growth of fatigue crack is confirmed when the AE events recorded at a certain load value and they do not appear again until a higher value of load is applied. In the case of EB2, the Kaiser effect did not apply when the loading truck passed on the center deck (low load/strain) and then it passed on the east deck (high load/strain). All the AE events were only recorded at lower loads/ strain values. Moreover, the four AE events were recorded and located inside the perimeter of P14 as shown in Fig. 6-left. No events were recorded and located on EB2 itself. As stipulated in ASTM E1139 [43], the estimated error in source location is 5% of sensor spacing. The sensors spacing in horizontal and vertical directions was 22 and 32 in., respectively, as shown in Fig. 6-left. The average sensors spacing was 27 in.. Therefore, the estimated error in source location was 1.35 in.. By addressing this error in source location, it was found that the majority of AE events (3 of 4 events) are still located inside the perimeter of the pin and only one event may be 204
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Fig. 6. AE source location (left) and strain correlation (right) for EB2 during the loading test of Phase2, in. = 2.54 cm.
located at the contact interface between the pin and the eyebar. According to the Kaiser effect analysis and the locations of AE events recorded during monitoring EB2, it was found that the recorded AE events were not indicative of fatigue crack on EB2. In addition, these AE events were associated to rubbing between components of the pinned connection at FB14. After AE system verification check, no AE events were recorded at all during monitoring EB7 which confirmed there were no fatigue cracks on this eyebar. In addition, the AE hits recorded in Phase1 during monitoring EB7 were related to rubbing between components of the pinned connection at FB18. The results of AE monitoring for P22 conducted in Phase2 is plotted in Fig. 5-right. During normal traffic operation, the results indicated that the majority of AE events (32 of 39 events in total) were recorded and localized at the same location of EB17 as indicated in Fig. 5b. However, only one AE event were recorded at the same location of EB16. During the loading test, the same conclusion was observed where the majority of AE events (20 of 24 events in total) were recorded at the same location of EB17 as indicated in Fig. 5d. However, only one AE event was recorded at the same location of EB16. Again, the three causes mentioned previously for the source of these AE events recorded on P22 and located at the same location of EB17 were still possible. Cause1, the pin itself may have a wearing defect or fatigue crack. Therefore, an ultrasonic testing was performed on the P22 also. The ultrasonic results indicated that the pin was in good condition and free from any wear defect or fatigue crack. Cause2, those AE events may result from the growth of a fatigue crack on EB17. Thus and at this point, it is important to declare that the source of all AE activities; recorded during monitoring EB16 in Phase1 and plotted in Fig. 4; was located on EB17 and was not on EB16. This can happen when only one AE sensor is used for AE hits detection and when acoustic coupling of the different eyebar is provided by connecting to the same pin. One sensor monitors the AE activities but cannot confirm the source locations of these activities. At this conclusion, a decision was made to monitor EB17 instead of EB16 during the loading test. Cause3, the AE source may be associated with fretting or rubbing between EB17 and P22. A distinction between Cause2 and Cause3 is discussed below. The results of AE monitoring for EB17 during loading test conducted in Phase2 is plotted in Fig. 7-left where a total of 30 AE events were recorded. These AE events were recorded at high load/strain values of loading truck trips T9 to T12 as shown in Fig. 7right where loading truck ran on the east deck. Therefore, it was reported that EB17 was active in terms of AE. However, AE data under the impact of normal traffic operation was not available to confirm whether EB17 was active or not in terms of AE because
Fig. 7. AE source location (left) and strain correlation (right) for EB17 during the loading test of Phase2. 205
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Fig. 8. Crack indications on EB18, EB19, and EB20.
EB17 was monitored under the impact of the loading truck only. Such data is essential for overall conclusion and decision making since the loads generated by a loading truck are much higher than the loads generated by normal traffic. It was reported that these AE events were considered to be part of the plastic deformation that can be caused from the rubbing between EB17 and P22 resulting in a possible wearing on their contact surface. Rubbing and fretting is considered normal due to the articulated nature of the connection and the loads supported by these members. The effect of maximum load created by the loading truck provides a reliable overall conclusion based on the loading expectation. Since these AE events were recorded only at the maximum load created by the loading truck and the AE data from normal traffic load was not available, it was found that no immediate action is required. However, the annual or continued AE monitoring can be used to enhance the bridge safety and reduce the cost of required bridge repairs at this location.
9. Assessing fatigue crack condition Three fatigue crack indications were visually detected and then confirmed using MPI as shown in Fig. 8. These cracks were detected on EB18, EB19, and EB20 located at FB40, FB45, and FB46 shown in Fig. 2. Only EB19 is located on the west truss. The cracks were monitored using AE in Phase3 while the bridge was dynamically loaded with regular traffic load and during a loading test. Four AE sensors were used to monitor each crack as shown in Fig. 9. After removing the paint, the sensors were coupled to the eyebar surface using ultrasonic couplant and fixed using magnetic holders. After AE system verification check, no AE was recorded during normal traffic operation or the loading test. Therefore, it was concluded that these cracks were not active. Two possible reasons for the existence and dormant state of these cracks are presented below. Reason1; these crack indications may in fact be fabrication defects introduced during the original manufacturing process. All three eyebars monitored as part of Phase3 were loop-welded eyebars. The eyelet of loop-welded eyebars have a forge-welded lap resulting in the distinctive “Y” junction at the neck of the eyebar head [44,45]. Defects such as inclusions (scale pits), incomplete fusion (cold shut) or surface cracking can be introduced in the forging process of the forge-welded lap [46,47]. This is further supported by evidence of incomplete fusion noted on EB18 during a detailed visual inspection of the crack following the removal of the protective coating and limited excavation of the cracked material. Reason2; these cracks might be the conclusion of fatigue during the primarily use of the bridge and they may have become dormant through reducing the structural demand. The bridge was designed primarily to carry CPR trains but also had a track for local electric trolley service, as well as a lane for carriage traffic. Later, the bridge was upgraded to carry vehicular traffic on the east and center decks as well as pedestrian traffic on the west deck. The eyebars may have been subjected to higher loads resulted from the initial use of the bridge compared with the loads generated by the current use. Therefore, the crack indications on EB18, EB19, and EB20 monitored through AE have been found to be dormant. The crack indications identified on the loop welded eyebars can be detected using traditional NDT methods. However, without the additional information provided by AE monitoring, costly interventions and possible closures would be required to enhance the bridge safety to address material defects that may not be structurally relevant. However, such defects can potentially initiate sites for fatigue cracking and remain a fatigue concern. AE monitoring can be used to enhance the bridge safety and reduce the cost of required bridge repairs by confirming the dormant state of the crack indications on an annual basis.
Fig. 9. AE sensors on EB18, EB19, and EB20. 206
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10. Conclusions A comprehensive integration of complementary inspection, strain correlation and service history with AE data enabled structural health condition assessment to be carried out. A most important part of an AE monitoring program is developing a comprehensive test procedure in cooperation with the bridge owner so that the test program addresses the owner’s needs. AE monitoring can play an effective role in enhancing safety, ensuring availability and reducing repair/refurbishment costs of steel bridges. Geometric clustering as AE source localization and load cycle correlation as strain monitoring with respect to the time are important parts for AE data analysis, interpretation, and classifying crack activity. Correlation of AE to strain measurement is effective in assisting to identify AE sources such as crack propagation vs. pin fretting. Information about service history of the bridge such as loading can be helpful in assessing the significance of activity or inactivity of AE sources. The recorded AE events were not indicative of fatigue crack on the selected eyebars. In addition, these AE events were associated to rubbing between components of the pinned connections. The existing crack indications monitored have been found to be dormant. However, the annual or continued AE monitoring can be used to enhance the bridge safety and reduce the cost of repairs by assessing the conditions of these cracks. Acknowledgment Public Services and Procurement Canada, Canada and WSP Canada Group, Canada (previously MMM Group) for their cooperation in conducting this investigation on Alexandra Bridge. References [1] Hopwood II T, Prine DW. Acoustic Emission Monitoring of In-Service Bridges, Research Report, UKTRP-87-22. U.S.: Kentucky; 1987. [2] D. Nyborg, J. Cavaco, E. Nyborg, and D.R. Hay, Combined Application of Ultrasonic Inspection and Acoustic Emission Monitoring for Evaluation of Fracture Critical Members in Railway Bridges, http://citeseerx.ist.psu.edu, Accessed 12 November 2018. [3] Connor RJ, Lloyd JB. Maintenance Actions to Address Fatigue Cracking in Steel Bridge Structures: Proposed Guidelines and Commentary. NCHRP Transportation Research Board of The National Academies, Purdue University, West Lafayette, Indiana. 2017. [4] Sakagami T, Izumi Y, Shiozawa D, Fujimoto T, Mizokami Y, Hanai T. Nondestructive evaluation of fatigue cracks in steel bridges based on thermoelastic stress measurement, 21st European conference on fracture, ECF21, 20-24 June 2016, Catania, Italy. J Procedia Struct Integ 2016;2:2132–9. [5] Parmar DS, Sharp SR. Acoustic Emission for Non-Destructive Testing of Bridges and other Transportation Infrastructure. National Conference May on Transportation Infrastructure & Regulatory Policy. Denver, U.S; 2009. [6] Holroyd TJ. The Application of AE in Condition Monitoring, BINDT Organized CM2005 Conference. Cambridge; 2005. [7] Vamvakousis A, Samoilis G, Prassianakis I. Microscopic origins of acoustic emission. Insight-Non-Destruct Test Condit Monitor 2002;44:34–9. [8] Pao YH, Gajewski RR, Ceranoglu AN. Acoustic emission and transient waves in an elastic plate. J Acoust Soc Am 1979;65:96–105. [9] Egle DM. A stochastic model for transient acoustic emission signals. J Acoust Soc Am 1979;65:1198–203. [10] ASTM E1067/E1067M, Standard Practice for Acoustic Emission Examination of Fiberglass Reinforced Plastic Resin Tanks/Vessels; 2018. [11] ASME Boiler and Pressure Vessel Code, Acoustic Emission Examination of Metallic Vessels During Pressure Testing, Section V, Article 11 and 12; 2010. [12] ASTM E3100, Standard Guide for Acoustic Emission Examination of Concrete Structures; 2017. [13] Pollock AA. Loading and stress in acoustic emission testing. J Mater Evaluat 2004;62:326–33. [14] Zeng H, Hartell JA, Emerson R. Assessment of Bridge Repair with the Aid of Load Test and Acoustic Emission Monitoring, 8th Congress on Forensic Engineering. Austin, Texas; 2018. November 29 - December 2. [15] Abouhussien AA, Hassan AA. Evaluation of damage progression in concrete structures due to reinforcing steel corrosion using acoustic emission monitoring. J Civil Struct Health Monitor 2015;5:751–65. [16] ElBatanouny M, Mangual J, Ziehl P, Matta F. Early Corrosion detection in prestressed concrete girders using acoustic emission. J Mater Civ Engng 2014;26:504–11. [17] Mangual J, ElBatanouny M, Ziehl P, Matta F. Corrosion damage quantification of prestressing strands using acoustic emission. J Mater Civ Engng 2013;25:1326–34. [18] Abdelrahman M, ElBatanouny M, Ziehl P. Acoustic emission based damage assessment method for prestressed concrete structures: modified index of damage. J Engng Struct 2014;60:258–64. [19] Zhang Y, Rong Q, Kong D, Wang W, Xu F, Dong W. Dynamic Monitoring of Fatigue Crack Process of Orthotropic Steel Bridge Structure with Acoustic Emission. International Conference on Fracture. 2013http://www.gruppofrattura.it/ocs/index.php/ICF, ICF13. [20] C. Wang, L. Tian, B. Fu, and Y. Zhang, Fatigue Cracking Monitoring and Evaluation Using Smart Sensors for Steel Bridge Decks, Bridge Maintenance, Safety Management, Resilience and Sustainability, Biondini & Frangopol (Eds), Taylor & Francis Group, London, ISBN 978-0-415-62124-3, pp. 818–823; 2012. [21] Roberts T, Talebzadeh M. Acoustic emission monitoring of fatigue crack propagation. J Constr Steel Res 2003;59:695–712. [22] Johnson M, Ozevin D, Washer G, Ono K, Gostautas R, Tamutus T. Acoustic emission method for real-time detection of steel fatigue crack in eyebar, transportation research record. J Transport Res Board 2012;2313:72–9. [23] Mckeefry JA, Shield CK. Acoustic Emission Monitoring of Fatigue Cracks in Steel Bridge Girders. Minnesota DOT Report, MnDOT-1999-36. 1999. [24] Schultz A, Thompson D. Development of An Advanced Structural Monitoring System. Minnesota DOT Report, MnDOT-2010-39. 2010. [25] D.R. Hay, W.A. Megid, and T.A. Youssef, Monitoring Critical Steel Bridge Member Condition Using Acoustic Emission, 2nd International Conference on Bridge Testing, Monitoring and Assessment, BTMA2, Housing & Building National Research Center, HBRC-ISHMII, SHM51, 27-29 December 2015, Cairo, Egypt. [26] Kaphle M. Analysis of Acoustic Emission Data for Accurate Damage Assessment for Structural Health Monitoring Applications. Ph.D. Dissertation, Queensland University of Technology. 2012. [27] Moorthy V, Jayakumar T, Raj B. Acoustic emission technique for detecting micro- and macroyielding in solution-annelaed AISI type 316 austenitic stainless steel. Int J Press Vessels Pip 1995;64:161–8. [28] Han Z, Luo H, Wang H. Effects of strain rate and notch on acoustic emission during the tensile deformation of a discontinuous yielding material. J Mater Sci Engng: A 2011;528:4372–80. [29] Mukhopadhyay CK, Kasiviswanathan KV, Jayakumar T, Raj B. Acoustic emission during tensile deformation of annelaed and cold-worked AISI type 304 austenitic stainless steel. J Mater Sci 1993;28:145–54. [30] Paris PC, Gomez MP, Anderson WE. A rational analytic theory of fatigue. Trend Engng 1961;13:9–14. [31] Kim ST, Tadjiev D, Yang HT. Fatigue life prediction under random loading conditions in 7475–T7351 aluminum alloy using the RMS model. Int J Damage Mech 2006;15:89–102. [32] Grosse CU, Ohtsu M. Acoustic Emission Testing: Parameter analysis. Springer; 2008.
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Engineering Fracture Mechanics journal homepage: www.elsevier.com/locate/engfracmech
Monitoring fatigue cracks on eyebars of steel bridges using acoustic emission: A case study
T
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Wael A. Megida,b, , Marc-André Chaineyc, Paul Lebrund, D. Robert Haya a
TISEC Inc., Canada Faculty of Engineering, Menoufia University, Egypt Dillon Consulting Limited, Canada d Public Services and Procurement, Canada b c
A R T IC LE I N F O
ABS TRA CT
Keywords: Acoustic emission Eyebar Fatigue crack Kaiser effect Structure health monitoring
A cost-effective inspection procedure was conducted using Acoustic Emission (AE) monitoring to detect fatigue crack initiation and/or growth and to assess the crack activity in eyebars of a steel bridge. An eyebar is a straight steel bar with an eye shape hole at each end for fixing to other components. Eyebars are used in settings in which only tension is applied. Multiple AE sensors were installed on critical eyebars of a bridge. These eyebars were selected based on the bridge maintenance history and field inspection reports. The AE characteristics and source localization were dynamically monitored and recorded on-site in real-time under the impact of normal traffic operation and controlled loading test. In the loading test, a loading truck ran at different speeds and stopped at critical floor beams. The parameters of AE signal such as counts, amplitude, duration, rise time, and energy were analyzed. Analysis of the Kaiser effect for AE activities was performed to confirm or deny the initiation or propagation of possible fatigue cracks. The results of AE monitoring showed that AE is a very reliable technology for confirm or denying the fatigue cracks initiation and assessing the condition of existing cracks and is a cost-effective component in Structure Health Monitoring (SHM).
1. Introduction Steel bridges are crucial components of a healthy and productive transportation infrastructure with an ever-increasing demand for ensuring integrity and performance. Defects such as cracks and plastic deformation in steel bridge structural components may have originated during the fabrication process, creating sites for the initiation and propagation of cracks due to load demand. These cracks can be initiated and grow by a combination of factors including traffic loading leading to component fatigue, accidental and environmental loads, corrosion, upgrading the bridge to carry heavier traffic, aging beyond their original design life and poor detail design [1,2]. Fatigue and fracture are two states of steel bridges least understood in terms of design, inspection, and especially for repair and retrofit. Although a multitude of research and case studies of fatigue damage and steel bridge cracking exist in the literature, only a limited number of professional engineering short courses are offered in this topic and few reference manuals are available to practitioners. As a result, bridge owners and their consultants have to develop their own strategies. Unfortunately, experience has shown that some implemented repairs or retrofits have actually made the conditions worse due to the lack of understanding of the
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Corresponding author. E-mail address: [email protected] (W.A. Megid).
https://doi.org/10.1016/j.engfracmech.2019.02.022 Received 29 November 2018; Received in revised form 11 February 2019; Accepted 15 February 2019 Available online 19 February 2019 0013-7944/ Crown Copyright © 2019 Published by Elsevier Ltd. All rights reserved.
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development of fatigue cracks and how best to address it [3,4]. This potential deterioration requires periodic inspection and bridge evaluation in terms of structural health, integrity, safety, and proper functioning. The unpredictability introduces a degree of uncertainty in planning the frequency of inspection and repair or replacement of the affected structural parts to minimize traffic interruption and operating costs [5]. Consequently, a method of early detection of fatigue cracks is essential. The inspection process and techniques should insure that only the necessary repairs are executed and that additional flaws are not introduced and consider size and inaccessibility. 2. Background The primary structural Non-Destructive Testing (NDT) method for steel bridges is visual inspection. It is often the only economically viable method but its effectiveness depends upon well-trained and motivated inspectors and may be hindered by a lack of accessibility of many critical structural areas. Conventional NDT methods such as dye-penetrant and magnetic-particle testing as well as radiography and ultrasound enable detection and sizing of certain types of defects but require close physical access and basically detect geometric discontinuities [1]. Some discontinuities may be growing flaws that could lead to impaired operation of the bridge or failure, but many are simply benign, stable discontinuities that may never cause problems such as original fabrication defects with no active growth. The only way to separate growing from non-growing flaws through conventional NDT is to periodically re-inspect the flaw to detect size changes. In addition, foil crack gauge or an optical technique can be used. These approaches may be ineffective or expensive. Moreover, a substantial growth of the defect may be required for conclusive results. Among all NDT methods, only Acoustic Emission (AE) responds primarily to the flaws initiation as well as the growth and time varying behavior of the existing flaw. The notable advantage of AE is that large structures may be monitored with relatively few fixed sensors and less expensive operator costs in terms of inspection time. AE also has the potential for achieving a cost-effective inspection for relatively inaccessible areas [1]. AE alone can only locate defects, but cannot give information for their size and shape. The degradation conditions of structures are usually associated with crack growth, plastic deformation and corrosion. However, they may also include rubbing and scoring of surfaces as joints loosen or subsidence occurs [6]. Structural condition monitoring is referred to Structural Health Monitoring (SHM) and is an emerging field in which there is a strong interest by the end user community to assure continued safe operation in the cost-effective manner. AE has been successfully developed for testing materials, components and structures [6]. The knowledge of signal processing, AE sources, and wave propagation have improved since the work done by Kaiser in 1950 [7–9]. In addition, AE procedures have been refined and standardized [10–12]. These procedures usually require overstressing the material beyond its normal working load [13] which can easily be arranged for loaded structures [14]. Maintaining the safety and capacity of a steel bridge to ensure uninterrupted traffic operation while controlling repair and refurbishment costs is essential. In maintaining this balancing act, the steel bridge engineer relies on engineering data from structural analysis, load history, strain measurement and periodic inspection reports. These combined data still may be insufficient to make a decision to repair a bridge member or to replace a span. In many instances of fatigue cracks in bridges, the cracks propagate only a certain distance and then become dormant due to stress relaxation [2]. However, when crack propagation continues, it becomes a judgment call as to whether, or how soon, funds be allocated for repair or replacement. AE monitoring can provide the bridge engineer with this additional information. In the recent years, AE technology has been getting more widely used in laboratory investigations [15–21] as well as in active crack detection of bridge structures [22–25]. AE technology was used to monitor dynamic process of crack growth of a segment model of a full size orthotropic steel bridge structure during a 30 day fatigue test [19]. The results have shown that AE is a very valuable technology for monitoring dynamic process of crack development. It can be used not only for various tension, compression and fatigue tests in labs, but also for SHM in the fields. A comparison between piezoelectric paint sensors and commercial AE sensors was conducted during monitoring and evaluating the full-scale orthotropic steel bridge deck under cyclic loading [20]. AE monitoring results showed that typical genuine AE signals can be collected by piezoelectric paint sensor as well as commercial AE sensor under the same cyclic loading. An advanced AE system with accurate source location was used to monitor fatigue crack propagation in steel and welded steel compact tension and T-section girder test specimens [21]. It was concluded that it may be possible to predict the remaining service life of fatigue damaged structures from the results of short term AE monitoring. After the discovery of a significant crack in an eyebar on the San Francisco-Oakland Bay Bridge in California, remote AE monitoring was explored to provide the greatest possible safeguards for the some 200,000 vehicles that used the bridge daily [22]. It was found that AE monitoring allowed the detection and localization of crack initiation and growth in real time. Therefore, the AE was being used to continuously monitor 384 eyebars with 640 AE sensors. This study mainly aims to demonstrate that AE can provide an important contribution to steel bridge owners and their consultants. This contribution can be addressed as AE can provide unique positive or negative insights into damage processes. Thereby, AE can reduce the cost of the bridge maintenance by eliminating the unnecessary repairs. Moreover, AE can provide early warnings of severe or sudden failures such as fatigue cracks that they usually occur with very insignificant alerts. AE can detect the accumulation of microdamage inside components, especially under service conditions. By addressing these issues, it is believed that the data presented here can improve the effectiveness of AE technique for SHM of civil infrastructures such as bridges in cost-effective manner. 3. AE approach Structures under stress produce acoustic stress waves that a human ear may not always be able to hear. The phenomenon of acoustic stress waves generation in structures under stress is called AE. It is basically the generation and propagation of stress waves in materials due to the effects of stress such as deformation, initiation and crack growth, opening and closing of a crack, diffusion and 199
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Fig. 1. Parameters of AE signals [48].
movement of a dislocation, twining and phase transformation [1]. In addition to these sources, AE waves can be generated even during plastic deformation of the material in a highly stressed zone adjacent to the tip of the crack. In composite materials, fracture and de-lamination are considered sources of AE. In general, the sources of AE are predominantly damage-related [5]. The stress waves propagate creating AE signals detected by remote sensors that convert them into a useable electric signals [1]. Different mechanisms are responsible for AE generation in different materials [26]. During tensile deformation in a ductile material such as steel, maximum AE activity was found to be generated near the yield point [27]. Uniaxial tests were performed on steel specimens and the total duration of the test was divided into four stages; the micro-plastic deformation stage, the yielding stage, the strain hardening stage, and the necking and fracture stage [28]. It was found that the most high energy AE signals were generated during the two initial stages. The same conclusion was reported after studying the tensile deformation of annealed and cold-worked AISI 304 stainless steel [29]. When a steel bridge is subjected to repeated load cycles, discontinuities and material imperfections in load bearing members may initiate fatigue cracks and release a number of emissions. The rate of crack growth depends on a number of factors including both the load magnitude and frequency as well as the current crack length [30,31]. Basic principles involved in measurement of AE are the changes in the parameters of a wave signal through a medium. These changes in signals are amplified with the help of resonant piezoelectric devices. Two broad approaches can be identified for analysis of recorded AE data: parameter- and waveform-based approaches [26]. In parameter-based approach, only some of the parameters of the AE signal are recorded, but the signal itself is not recorded. This minimizes the amount of data stored and enables fast data recording. The signal parameters are used to assess the extent of damage. A burst detected by the AE system is known as AE hit where a signal exceeds the threshold and causes a system channel to accumulate data, thereby describing an AE event. Event rate is the number of events/hits per time. By measuring the signal parameters, shown in Fig. 1, such as counts, amplitude, duration, rise time, and energy (the area under the rectified signal envelope), a multitude of quantitative information on the magnitude of material defects, location and time of their origination, and the rate of their propagation can be obtained. These signal related parameters and the hit description including the external parameters such as the value of the applied load, the time of detection, the fatigue cycle count and the level of continuous background noise at the time of detection are essential for AE data analysis [5]. Number of hits and number of counts can be used to quantify an AE activity. The energy is often preferred to interpret the magnitude of source event over counts as it is sensitive to both amplitude and duration, and less dependent on the voltage threshold and operating frequencies [32]. Other useful parameters include average frequency (calculated by dividing counts by duration) and RA (Rise-time divided by Amplitude), which can be used to sort signals from tensile and shear cracks [32]. In waveform-based approach, the signal itself is recorded. The higher computing resources are required for this approach, to perform quick data acquisition and record complete waveforms [33]. This approach can allow the use of signal processing techniques and signal-noise discrimination [34]. Also, correlations with corresponding time and/or load readings enable discrimination between real and false signals (noise) and assists in assessing the significance of the source with respect to structural integrity of the member under examination [1]. Locating the source of significant AE is an essential goal of an inspection. With an array of resonant AE sensors, using the arrival
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times at each sensor in the array, the source can be located spatially [35]. By properly locating the sensors in this manner, it is possible to inspect an entire structure with relatively few sensors. AE sensors are used in linear or planar arrays. In linear defect location, two AE sensors are installed on the test specimen. The difference in the arrival times of the AE wave at the two sensors are used to locate the defect in relation to their positions. In planar defect location, more than two AE sensors are used where the defect is located in 2D-plan. A single sensor can be used per monitoring location to detect the AE activities without locating the source. 4. Bridge-AE monitoring overview Steel bridge members, that are monitored using AE approach, are often identified through routine periodic visual inspection. However, additional inputs to select candidate locations include bridge engineering knowledge, records of past maintenance performed on the bridge, and engineering judgment. These include the formal categorization of fracture-critical locations and experience with a specific types of bridges for which a bridge engineer has responsibility and bridge-specific experience including inspection and in-service bridge history. A combination of these resources is generally used to select the sites. Typically, the steel bridge members are susceptible to fatigue crack initiation and eventual failure due to fracture that receive stress above the threshold stress range for a designated fatigue detail type (A to E’) [2]. For railroad bridges that are normally subject to high live loads relative to their design loads, loading is provided by normal rail traffic. The design loads reflect the worst loading that can be caused on the bridge by traffic. For highway bridges, normal traffic can be used. In addition, proof loads with a loading truck have been used to apply static and a variety of dynamic loads [36,37]. Also, traffic can be managed for stopping, accumulating, and proceeding in specific lanes. A combination of regular traffic, controlled loading and traffic management can be utilized to accurately capture the maximum load effect of the structure. Guidelines [38] for AE testing under controlled stimulation can be adapted to bridge inspection. AE activity is defined by the relationship between a stimulus that is conventionally a controlled monotonically increasing load and an activity metric such as AE counts. Under normal bridge traffic load, the stimulus is the number of fatigue cycles introduced by the passing traffic. The correlations between strain and AE provide a basis for assessing the AE activities using the fatigue cycles as the stimulus [25]. The AE activities are recorded and the intensity is determined to generate an AE source index (AEI). This is further complemented with information from other NDT testing information to provide series of recommendations for follow-up such as scheduling the next inspection or performing immediate repair [25]. 5. Case study bridge An investigation was conducted on Alexandra bridge to determine the feasibility of detecting fatigue crack initiation and/or propagation on steel eyebars using AE monitoring. Alexandra bridge (Fig. 2) is a 574.8 m-long steel truss bridge. The bridge provides an important commuter link between Ottawa and Gatineau, Canada. Single traffic lanes are located on the centre and east decks. The west deck is used by cyclists and pedestrians. The bridge was constructed by the Canadian Pacific Railway in the period from 1898 to 1901 using over 600 individual eyebars in the construction of the trusses. The bridge consists of a north approach trestle, two simply supported trusses, a 320.0 m long three span cantilevered truss bridge with a 169.4 m long center span, and a south trestle. The
Eyebar
Fig. 2. Alexandra Bridge, Ontario/Quebec, Canada (top) and eyebar (bottom). 201
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Table 1 AE monitoring program. Loads
Deck
Bridge members
Phase1
Session1 Session2
Normal Traffic Loading Truck Normal Traffic
Center and East Center East
16 eyebars
Phase2
Session1 Session2
Normal Traffic Loading Truck
Center and East
3 eyebars + 2 pins
Phase3
Session1 Session2
Normal Traffic Loading Truck
Center and East
3 eyebars
bridge's main cantilever centre span was, at the time of construction, the longest in Canada and the fourth longest in the world. 6. AE monitoring program The AE monitoring program is presented in Table 1. The monitoring program was conducted in three phases. In Phase1, AE monitoring was conducted to search and detect possible fatigue crack on eyebars at critical areas of the bridge. In Phase2, AE was used to monitor in details the sources of AE activities recorded in Phase1 and to monitor the potential growth of fatigue crack. In Phase3, AE was used to monitor the growth of fatigue cracks indications detected visually and confirmed by Magnetic Particles Inspection (MPI). The process of MPI put a magnetic field into the eyebar. It was indirectly magnetized by applying a magnetic field from an outside source. The presence of a surface or subsurface discontinuity in the eyebar such as cracks allows the magnetic flux to leak, since air cannot support as much magnetic field per unit volume as metals. To identify a leak, dry ferrous particles were applied to the eyebar. Sixteen eyebars were monitored in Phase1. Three eyebars and two pins were monitored in Phase2. Three eyebars were monitored in Phase3. Each phase consisted of two sessions. In Session1, the bridge members were monitored under the impact of normal traffic running on the center and east decks. However in Session2, the bridge members were monitored under the action of a loading truck (60 tonnes) running on the center and east decks. This was not the case in Phase1-Session2 where the east deck was opened for traffic while the loading truck was running on the center deck. In loading tests, the loading truck ran at different speeds as well as stopping at critical floor beams (FB). The sequence of loading test conducted in Phase2 is listed in Table 2. A Vallen AMSY4 (16 channel) digital AE monitoring system was employed. A National Instrument SC-2043-SG 8-channel strain gauge signal conditioning was incorporated into the parametric channels of the AE system. It was equipped with Vallen VS375-RIC, 375 kHz AE sensors, with built-in preamplifiers. The logging settings of AE channels are reported in Table 3. 7. Identifying possible crack locations The bridge was constructed using over 600 individual eyebars. A cost-effective approach was required to scan critical eyebars where fatigue cracks are likely without interrupting the bridge operation. Among NDT approaches, the developed AE approach was expected to be ideal for this study and to demonstrate the feasibility of monitoring the entire bridge. AE activity in terms of number of hits and related relative strain of sixteen eyebars were recorded in Phase1 to search for and detect possible fatigue crack initiation or propagation activity. These eyebars were selected as critical areas of the bridge based on the bridge maintenance history and field inspection reports. All monitored eyebars are on the main cantilever centre span of the east truss at the bottom chord level. The east truss of the structure is subjected to more load than the west truss because vehicular traffic is running on the center and east decks only. The AE monitoring system was setup to check for AE activity while the bridge was dynamically loaded with regular traffic load and/or during a loading test for four days and half day, respectively. Around 15,000 vehicles use the bridge daily. The AE monitoring Table 2 Loading test sequence of Phase2.
Slow speed Fast speed Slow speed with Stopping Slow speed Slow speed Fast speed
Trip
Deck
Direction
T1 T2 T3 T4 T5 T6 T7 T8
Center
North South North South North South North South
to to to to to to to to
South North South North South North South North
T9 T10 T11 T12
East
North South North South
to to to to
South North South North
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Table 3 AE channels settings. Threshold Gain Range Rearm time
40 dB 34 dB 10 Vpp 3.2 ms
Duration discrimination time Pretriggre samples Sampling rate Bandwidth of AE filter
400 µs 200 40 MHz 250–700 kHz
was performed in Phase1 using a single AE inspection sensor per location. According to the bridge maintenance history, field inspection reports, and typical eyebar construction and defects, it was concluded that more attention should be given to the integrity of the members at the pinned connections. Therefore, all the sensors were installed on the eyebars close to the pinned connections as shown in Fig. 3-left. The details of a pinned connection is shown in Fig. 3-right. After removing the paint, the sensor was coupled to the eyebar surface using ultrasonic couplant and fixed using a magnetic holder as shown in Fig. 3-left. The AE system verification check was performed using the lead-break standardization process [39,40]. The temperature and relative strain were also measured and recorded in real-time. One strain gauge was installed on each selected eyebar. The results of AE monitoring for the sixteen eyebars (EB1 to EB16) are plotted in Fig. 4. The results indicated that EB2, EB7, and EB16 had the higher number of AE hits. This observation demonstrated that there is a higher potential for fatigue crack initiation or propagation on these three eyebars compared with the rest of the eyebars where less AE hits were recorded. EB7 is located at FB18 which in turn is found to be at the midpoint of the main cantilever centre span as shown in Fig. 2. EB2 and EB16 are located at FB14 and FB22, respectively, which in turn are found to be the articulated supports of the centre part of the main cantilever centre span as shown in Fig. 2. Thus, it was found that EB2, EB7, and EB16 are located at relatively critical loading areas on the bridge and were selected to receive a detailed AE monitoring in Phase2. For further integrity assessment of the pinned connections at FB14 and FB22 and taking advantage of the linear locating capabilities of AE, pins P14 and P22 were also selected to be monitored in Phase2.
8. Confirming AE source type and location In the initial program of Phase2, EB2, EB7, and EB16 associated with pins P14 and P22 were selected to perform AE source location to localize the sources and confirm if there is any possible crack initiation, propagation or any other type of source such as fretting, rubbing or thermal impact between members of pinned joints. The members of steel bridges can exhibit thermal expansion or contraction resulting from the daily or seasonally changes in the temperature. That can lead to release of stress waves at the pinned joints. The AE monitoring system was setup in Phase2 to check for AE activity in terms of events while the bridge was dynamically loaded with regular traffic load as well as during a loading test. Based on the AE data of P22 recorded under the impact of normal traffic (Fig. 5-b), it was recommend to monitor EB17 instead of EB16. This is discussed in detail below. Two AE sensors were used to monitor each pin of the 21 in. long pins. One sensor was installed on each pin end as shown in Fig. 3right. After removing the paint, the sensor was coupled to the pin surface using ultrasonic couplant and fixed using a magnetic holder. Acoustic coupling of all eyebars connected to the pin at the given eyebar cluster was confirmed through the lead-break standardization process. This configuration allows the system to indicate the eyebar with AE activity through correlation of the eyebar position with the linear location determined by AE monitoring. The results of AE monitoring for P14 conducted in Phase2 is plotted in Fig. 5left. During the normal traffic operation, the results indicated that the majority of AE events (15 of 19 events in total) were recorded and localized at the same location as EB2 as indicated in Fig. 5a. During the loading test, the same conclusion was confirmed where all the AE events (total of 5) were recorded at the same location of EB2 as shown in Fig. 5c. At this outcome, there were three causes for the source of these AE events. Cause1, the pin itself may have a wearing defect or fatigue crack. Therefore, an ultrasonic testing was performed on the P14. The ultrasonic results indicated that the pin was in good condition. Cause2, those AE events may be resulted from the growth of fatigue crack on EB2. Cause3, the AE source may be due to fretting or rubbing between EB2 and P14.
Fig. 3. AE sensor installed on EB6 at FB18 (left) and details of pinned connection at FB14 and FB22 (right), inch = 2.54 cm. 203
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Fig. 4. AE hits per eyebar recorded in Phase1.
Fig. 5. AE source location for P14 (left) and P22 (right) during the normal traffic operation and loading test of Phase2, in. = 2.54 cm.
To confirm the type of AE source and to make a distinction between Cause2 and Cause3, four AE sensors were installed to perform source location on EB2. The AE sensors were installed on EB2 around P14, as shown in Fig. 6-left. After removing the paint, the sensors were coupled to the eyebar surface using ultrasonic couplant and fixed using magnetic holders. After AE system verification check, the results of AE monitoring for EB2 during the loading test are plotted in Fig. 6-left where four AE events in total were recorded over one hour. During the loading test conducted in Phase2, the loading truck passed eight times on the centre deck and four times on the east deck with total of twelve trips over the bridge as shown in Table 2. This can be clearly noted in Fig. 6-right where the strain values recorded in the last four trips are nearly twice the strain values recorded in the first eight trips. This is because all the loads on the east deck are carried by the east truss but it carries only half of the loads on the centre deck. It was found that the four AE events were recorded at low load/strain only. The Kaiser effect is very useful phenomenon in AE monitoring [41,42]. The AE source related to the growth of fatigue crack is confirmed when the AE events recorded at a certain load value and they do not appear again until a higher value of load is applied. In the case of EB2, the Kaiser effect did not apply when the loading truck passed on the center deck (low load/strain) and then it passed on the east deck (high load/strain). All the AE events were only recorded at lower loads/ strain values. Moreover, the four AE events were recorded and located inside the perimeter of P14 as shown in Fig. 6-left. No events were recorded and located on EB2 itself. As stipulated in ASTM E1139 [43], the estimated error in source location is 5% of sensor spacing. The sensors spacing in horizontal and vertical directions was 22 and 32 in., respectively, as shown in Fig. 6-left. The average sensors spacing was 27 in.. Therefore, the estimated error in source location was 1.35 in.. By addressing this error in source location, it was found that the majority of AE events (3 of 4 events) are still located inside the perimeter of the pin and only one event may be 204
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Fig. 6. AE source location (left) and strain correlation (right) for EB2 during the loading test of Phase2, in. = 2.54 cm.
located at the contact interface between the pin and the eyebar. According to the Kaiser effect analysis and the locations of AE events recorded during monitoring EB2, it was found that the recorded AE events were not indicative of fatigue crack on EB2. In addition, these AE events were associated to rubbing between components of the pinned connection at FB14. After AE system verification check, no AE events were recorded at all during monitoring EB7 which confirmed there were no fatigue cracks on this eyebar. In addition, the AE hits recorded in Phase1 during monitoring EB7 were related to rubbing between components of the pinned connection at FB18. The results of AE monitoring for P22 conducted in Phase2 is plotted in Fig. 5-right. During normal traffic operation, the results indicated that the majority of AE events (32 of 39 events in total) were recorded and localized at the same location of EB17 as indicated in Fig. 5b. However, only one AE event were recorded at the same location of EB16. During the loading test, the same conclusion was observed where the majority of AE events (20 of 24 events in total) were recorded at the same location of EB17 as indicated in Fig. 5d. However, only one AE event was recorded at the same location of EB16. Again, the three causes mentioned previously for the source of these AE events recorded on P22 and located at the same location of EB17 were still possible. Cause1, the pin itself may have a wearing defect or fatigue crack. Therefore, an ultrasonic testing was performed on the P22 also. The ultrasonic results indicated that the pin was in good condition and free from any wear defect or fatigue crack. Cause2, those AE events may result from the growth of a fatigue crack on EB17. Thus and at this point, it is important to declare that the source of all AE activities; recorded during monitoring EB16 in Phase1 and plotted in Fig. 4; was located on EB17 and was not on EB16. This can happen when only one AE sensor is used for AE hits detection and when acoustic coupling of the different eyebar is provided by connecting to the same pin. One sensor monitors the AE activities but cannot confirm the source locations of these activities. At this conclusion, a decision was made to monitor EB17 instead of EB16 during the loading test. Cause3, the AE source may be associated with fretting or rubbing between EB17 and P22. A distinction between Cause2 and Cause3 is discussed below. The results of AE monitoring for EB17 during loading test conducted in Phase2 is plotted in Fig. 7-left where a total of 30 AE events were recorded. These AE events were recorded at high load/strain values of loading truck trips T9 to T12 as shown in Fig. 7right where loading truck ran on the east deck. Therefore, it was reported that EB17 was active in terms of AE. However, AE data under the impact of normal traffic operation was not available to confirm whether EB17 was active or not in terms of AE because
Fig. 7. AE source location (left) and strain correlation (right) for EB17 during the loading test of Phase2. 205
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Fig. 8. Crack indications on EB18, EB19, and EB20.
EB17 was monitored under the impact of the loading truck only. Such data is essential for overall conclusion and decision making since the loads generated by a loading truck are much higher than the loads generated by normal traffic. It was reported that these AE events were considered to be part of the plastic deformation that can be caused from the rubbing between EB17 and P22 resulting in a possible wearing on their contact surface. Rubbing and fretting is considered normal due to the articulated nature of the connection and the loads supported by these members. The effect of maximum load created by the loading truck provides a reliable overall conclusion based on the loading expectation. Since these AE events were recorded only at the maximum load created by the loading truck and the AE data from normal traffic load was not available, it was found that no immediate action is required. However, the annual or continued AE monitoring can be used to enhance the bridge safety and reduce the cost of required bridge repairs at this location.
9. Assessing fatigue crack condition Three fatigue crack indications were visually detected and then confirmed using MPI as shown in Fig. 8. These cracks were detected on EB18, EB19, and EB20 located at FB40, FB45, and FB46 shown in Fig. 2. Only EB19 is located on the west truss. The cracks were monitored using AE in Phase3 while the bridge was dynamically loaded with regular traffic load and during a loading test. Four AE sensors were used to monitor each crack as shown in Fig. 9. After removing the paint, the sensors were coupled to the eyebar surface using ultrasonic couplant and fixed using magnetic holders. After AE system verification check, no AE was recorded during normal traffic operation or the loading test. Therefore, it was concluded that these cracks were not active. Two possible reasons for the existence and dormant state of these cracks are presented below. Reason1; these crack indications may in fact be fabrication defects introduced during the original manufacturing process. All three eyebars monitored as part of Phase3 were loop-welded eyebars. The eyelet of loop-welded eyebars have a forge-welded lap resulting in the distinctive “Y” junction at the neck of the eyebar head [44,45]. Defects such as inclusions (scale pits), incomplete fusion (cold shut) or surface cracking can be introduced in the forging process of the forge-welded lap [46,47]. This is further supported by evidence of incomplete fusion noted on EB18 during a detailed visual inspection of the crack following the removal of the protective coating and limited excavation of the cracked material. Reason2; these cracks might be the conclusion of fatigue during the primarily use of the bridge and they may have become dormant through reducing the structural demand. The bridge was designed primarily to carry CPR trains but also had a track for local electric trolley service, as well as a lane for carriage traffic. Later, the bridge was upgraded to carry vehicular traffic on the east and center decks as well as pedestrian traffic on the west deck. The eyebars may have been subjected to higher loads resulted from the initial use of the bridge compared with the loads generated by the current use. Therefore, the crack indications on EB18, EB19, and EB20 monitored through AE have been found to be dormant. The crack indications identified on the loop welded eyebars can be detected using traditional NDT methods. However, without the additional information provided by AE monitoring, costly interventions and possible closures would be required to enhance the bridge safety to address material defects that may not be structurally relevant. However, such defects can potentially initiate sites for fatigue cracking and remain a fatigue concern. AE monitoring can be used to enhance the bridge safety and reduce the cost of required bridge repairs by confirming the dormant state of the crack indications on an annual basis.
Fig. 9. AE sensors on EB18, EB19, and EB20. 206
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10. Conclusions A comprehensive integration of complementary inspection, strain correlation and service history with AE data enabled structural health condition assessment to be carried out. A most important part of an AE monitoring program is developing a comprehensive test procedure in cooperation with the bridge owner so that the test program addresses the owner’s needs. AE monitoring can play an effective role in enhancing safety, ensuring availability and reducing repair/refurbishment costs of steel bridges. Geometric clustering as AE source localization and load cycle correlation as strain monitoring with respect to the time are important parts for AE data analysis, interpretation, and classifying crack activity. Correlation of AE to strain measurement is effective in assisting to identify AE sources such as crack propagation vs. pin fretting. 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