Field-calibrated influence lines for improved axle weight identification with a bridge weigh-in-motion system
Hua Zhao, Nasim Uddin, Xudong Shao, Ping Zhu, Chengjun Tan
Abstract
Hua Zhao, Nasim Uddin, Xudong Shao, Ping Zhu, Chengjun Tan
Abstract
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 O'BrienE.J., QuilliganM.J., & KaroumiR. (2006). Calculating an IL from direct measurements. Proceedings of the Institution of Civil Engineers, Bridge Engineering, 159, 31–34. [Google Scholar]), 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.
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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 O'BrienE.J., QuilliganM.J., & KaroumiR. (2006). Calculating an IL from direct measurements. Proceedings of the Institution of Civil Engineers, Bridge Engineering, 159, 31–34. [Google Scholar]), 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.
Key concepts: Weigh in motion, Axle, Bridge (graph theory), Structural engineering, Identification (biology), Engineering, Field (mathematics), Computer science