A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Field-calibrated influence lines for improved axle weight identification with a bridge weigh-in-motion system
Bridge weigh-in-motion (BWIM) systems take influence line (IL) as a reference to calculate axle weights. The calibrated ILs based on field measurements can acquire better conformity with the actual situation and more actually represent the characteristic of existing bridges. Following the methodology proposed by O'Brien et al. (2006), this paper presents back-calculating of the ILs from direct measurements taken on field tests for identification of axle weights of heavy vehicles. The algorithm for calibrating ILs is based on continuously measured bridge responses (strains) produced by two calibration vehicles passing across the instrumented bridge. In terms of the calibrated ILs, Moses' algorithm was applied to identify axle weights of moving vehicles by the least-square method. Field tests on highway US-78 in Alabama were conducted as a case study to evaluate the accuracy of the presented algorithms in calibrating ILs and to identify the axle weights by comparing with the static measurements, and with the measurements by the bending-plate weigh-in-motion (WIM) system on a one-to-one basis to demonstrate the accuracy of BWIM system relative to conventional pavement WIM systems. Finally, factors influencing axle weight identification, including the selection of ILs, the shapes of ILs and the scan numbers for collecting strain signals, were discussed.
Field-calibrated influence lines for improved axle weight identification with a bridge weigh-in-motion system
Bridge weigh-in-motion (BWIM) systems take influence line (IL) as a reference to calculate axle weights. The calibrated ILs based on field measurements can acquire better conformity with the actual situation and more actually represent the characteristic of existing bridges. Following the methodology proposed by O'Brien et al. (2006), this paper presents back-calculating of the ILs from direct measurements taken on field tests for identification of axle weights of heavy vehicles. The algorithm for calibrating ILs is based on continuously measured bridge responses (strains) produced by two calibration vehicles passing across the instrumented bridge. In terms of the calibrated ILs, Moses' algorithm was applied to identify axle weights of moving vehicles by the least-square method. Field tests on highway US-78 in Alabama were conducted as a case study to evaluate the accuracy of the presented algorithms in calibrating ILs and to identify the axle weights by comparing with the static measurements, and with the measurements by the bending-plate weigh-in-motion (WIM) system on a one-to-one basis to demonstrate the accuracy of BWIM system relative to conventional pavement WIM systems. Finally, factors influencing axle weight identification, including the selection of ILs, the shapes of ILs and the scan numbers for collecting strain signals, were discussed.
Field-calibrated influence lines for improved axle weight identification with a bridge weigh-in-motion system
Zhao, Hua (author) / Uddin, Nasim / Shao, Xudong / Zhu, Ping / Tan, Chengjun
2015
Article (Journal)
English
| Taylor & Francis Verlag | 2015
Bridge-weigh-in-motion for axle-load estimation
| TIBKAT | 2015
Bridge Weigh-in-Motion Algorithms Based on the Field Calibrated Simulation Model
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Bridge Weigh-in-Motion Algorithms Based on the Field Calibrated Simulation Model
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