Determination of Slab Participation from Weigh-In-Motion Bridge Testing

Elhelbawey, Maged; Fu, Chung C.; Şahin, Mehmet; Schelling, David R.

doi:10.1061/(asce)1084-0702(1999)4:3(165)

Cited by 12 publications

(14 citation statements)

References 2 publications

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“…These findings were also confirmed by testing previously conducted by the authors on a three-span steel girder and reinforced concrete deck superstructure (Gangone et al, 2011b). Elhelbawey et al (1999) research further showed that the NA locations are normally distributed. Therefore, a tdistribution is selected to create a 95 percent confidence interval (CI) on the mean NA location as the data set was small and extra variability in the standard variable based on the sample standard deviation is expected.…”

Section: Load Testingmentioning

confidence: 91%

“…The strains used for this computation resulted from the static positioning of the truck at the midspan as shown in Figure 8. The average values and standard deviations of the measurement locations with respect to the average location are computed on a lane by lane basis as Elhelbawey et al (1999) suggests that values of the NA are dependent on the transverse positioning of the load and a measured value near the location of loading will produce more accurate results than a measurement far away from the load. These findings were also confirmed by testing previously conducted by the authors on a three-span steel girder and reinforced concrete deck superstructure (Gangone et al, 2011b).…”

Section: Load Testingmentioning

confidence: 99%

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Experimental characterization and diagnostics of the early‐age behavior of a semi‐integral abutment FRP deck bridge

et al. 2012

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Purpose -The purpose of this paper is to further validate a wireless sensor system developed at Clarkson University for structural monitoring of highway bridges. The particular bridge monitored employs a fiber reinforced polymer (FRP) panel system which is fairly innovative in the field of civil engineering design. The superstructure was monitored on two separate occasions to determine a change in structural response and see how the structural system performs over time. Design/methodology/approach -A series of wireless sensor units was deployed at various locations of the steel superstructure, to measure both the modal response from acceleration measurements as well as quasi-static and dynamic strain response. Ambient and forced loading conditions were applied to measure the response. Data results were compared over two separate periods approximately nine months apart. Findings -The first eight mode shapes were produced from output-only system identification providing natural frequencies ranging from approximately 6 to 42 Hz. The strain response was monitored over two different testing periods to measure various performance characteristics. Neutral axis, distribution factor, impact factor and end fixity were determined. Results appeared to be different over the two testing periods. They indicate that the load rating of the superstructure decreased over the nine month period, possibly due to deterioration of the materials or composite action. Research limitations/implications -The results from the two testing periods indicate that further testing needs to be completed to validate the change in response. It is difficult to say with certainty that the significant change in response is due to bridge deterioration and not other factors such as temperature effects on load rating. The sensor system, however, proved to provide high quality data and responses indicating its successful deployment for load testing and monitoring of highway infrastructure. Originality/value -The paper provides a depiction of the change in structural behavior of a bridge superstructure using a wireless sensor system. The wireless system provided high-rate data transmission in real time. Load testing at two different points in time, eight months apart, showed a significant change in bridge behavior. The paper provides a practical and actual physical load test and rating during these two periods for quantifiable change in response. It is shown that the wireless system is capable of infrastructure monitoring and that possible deterioration is expected with this particular bridge design. Additionally, the load testing occurred during different seasons, which could create cause for temperature effects in load rating. This can provide a basis for future performance monitoring techniques and structural health monitoring.

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Section: Load Testingmentioning

confidence: 91%

Section: Load Testingmentioning

confidence: 99%

Experimental characterization and diagnostics of the early‐age behavior of a semi‐integral abutment FRP deck bridge

et al. 2012

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“…Testing of real structures can therefore improve the understanding of the behavior of the neutral axis in real structures. Slab participation in composite action between steel girders and concrete slab was studied using a crawling test truck on four highway bridges in Maryland [12]. Three strain gauges were installed in each monitored cross-section, one under the bottom flange of the steel girder, one in the center of the web and one on the web close to the top flange.…”

Section: Controlled Testing Of Real Structuresmentioning

confidence: 99%

“…Example of results for Fig. 4 The neutral axis along the length of the bridge as evaluated with four different sensor combinations [12] J Civil Struct Health Monit midspan of girder 3 is shown in Table 3; the theoretical location is at 426 mm measured from the bottom of the cross-section. The table shows the 95 % confidence interval based on a t distribution.…”

Section: Controlled Testing Of Real Structuresmentioning

confidence: 99%

The neutral axis location for structural health monitoring: an overview

Sigurdardottir

Glišić

2015

J Civil Struct Health Monit

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Structural health monitoring (SHM) is the process of continuously or periodically measuring structural parameters over time and the transformation of the acquired data into information about the actual structural behavior. The main goal of SHM is to provide quantitative and actionable information to infrastructure managers on a system-wide level. However, to achieve this goal, damage sensitive universal parameters need to be researched, identified, and implemented into SHM processes and data analysis. The neutral axis location has been identified as a potential parameter for damage detection, performance evaluation, and condition assessment of beam-like structures. This paper is intended to serve as a reference for researchers who are interested in the state of the art in evaluation of the neutral axis location, facts about the neutral axis behavior, and challenges associated with using the neutral axis for SHM purposes. Studies of the neutral axis in laboratory settings, controlled on-site testing, and on real structures in on-site conditions are presented and discussed. The main difficulties in using the neutral axis location for SHM purposes that are identified in this paper are: (1) its sensitivity to environmental effects such as temperature, humidity, and loading positions, (2) its natural variation in healthy cross-sections, (3) quantification of its sensitivity to damage, i.e. its damage detection capabilities in terms of the damage size and its distance from sensors, and (4) correlation between the neutral axis location and the capacity, performance, and condition of the monitored structure. The proof of concept has been provided with numerical modeling, and in the laboratory, and to some extent through controlled testing; however, a general onsite validation still remains a major challenge.

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“…This research builds on prior work on the field testing, experimental testing, analysis, and design of curved I-girder bridge systems, as well as diagnostic testing of straight I-girder bridges (e.g. [3][4][5]). Well-documented diagnostic field tests of curved steel girder bridges include Beal [6] on four curved steel bridges: two singlespan I-girder bridges, one two-span continuous I-girder bridge, and one two-span continuous box girder bridge were tested.…”

Section: Introductionmentioning

confidence: 99%