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Identification of Vehicular Axle Weights with a Bridge Weigh-in-Motion System Considering Transverse Distribution of Wheel Loads
2014-03, Zhao, Hua, Uddin, Nasim, O'Brien, Eugene J., Shao, Xudong
A modified 2-D Moses algorithm for acquiring the field-calibrated influence line (IL) of an existing bridge is presented, based on strain data acquired continuously at a high scanning rate with calibration vehicles of known axle weights and axle spacings crossing an instrumented bridge. Considering the transverse distribution of the wheel loads on each girder due to two-dimensional (2-D) behavior of slab-girder bridge, the ILs of each of the girders can be calculated, which does not require the girders to possess the identical material and geometrical properties. By using the calculated ILs of each girder as references, a modified 2-D Moses algorithm was derived to identify axle weights of moving vehicles, taking into consideration the transverse distribution of the wheel loads on each girder. Mathematical equations to calculate ILs and axle weights were derived, and the proposed algorithms were implemented by a computer program written in MATLAB. The accuracy of the ILs calculation and axle weight identification was verified through a field test of a bridge on highway US-78 in Alabama. The identified axle weights showed agreement with the static measurements from weighing pads and with results from the bending-plate weigh-in-motion (BPWIM) system near the instrumented bridge.
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Use of weigh-in-motion (WIM) data for site-specific LRFR bridge rating
2012-06-04, Zhao, Hua, Uddin, Nasim, Waldron, Christopher J., O'Brien, Eugene J.
In this paper, truck weigh-in-motion (WIM) data are used to develop live load factors for use on Alabama state-owned bridges. The factors are calibrated using the same statistical methods that were used in the original development of AASHTO’s Load and Resistance Factor Rating (LRFR) Manual. This paper describes the jurisdictional and enforcement characteristics in the state, the WIM data filtering, sorting, and quality control, as well as the calibration process. Large WIM data sets from five sites were used in the calibration and included different truck volumes, seasonal and directional variations, and WIM data collection windows. Certain MATLAB programs were developed in the live load factor calibration process. The resulting state-specific live load factors are smaller than those of LRFR manual and are recommended to the Alabama Department of Transportation (ALDOT) in rating their bridges more efficiently