The DOI matches its Crossref record.
Weigh-in-Motion System Using Instrumented Bridges
Abstract
Acquisition of truck axle and gross weight information is necessary for structural and maintenance requirements of bridges and pavements. A system is described combining traffic sensors and strain gages on highway bridge girders to obtain axle and gross weights. A weight prediction algorithm is derived herein that filters out the dynamic components of bridge response and obtains the equivalent static axle weights by a least-square error minimization. It utilizes self-balancing signal conditioners, magnetic tape switches, analog-digital converters, and a minicomputer to record field data on magnetic tape. Output is subsequently obtained on a digital computer. The system showed predictions of gross truck weight and tandem axles consistent with calibration trucks and random traffic. The paper also describes modifications of the system that would allow its routine use in obtaining weigh-in-motion truck data and would also permit weight processing in the field.
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Citations
Cited by
Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 182
Zhou, Y., Pei, Y., Li, Z., Fang, L., Zhao, Y., & Yi, W. (2020) · Measurement · doi:10.1016/j.measurement.2020.107801 · Cited by 84
Martín-Sanz, H., Tatsis, K., Dertimanis, V. K., Avendaño-Valencia, L. D., Brühwiler, E., & Chatzi, E. (2020) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2019.1650079 · Cited by 22
Metadata
- Authors
- Moses, F.
- Venue
- Transportation Engineering Journal of ASCE
- Published
- 1979-05
- Volume
- 105
- Issue
- 3
- Pages
- 233-249
- ISSN
- 0569-7891 (ISSN-L)
- 2690-4098
- OpenAlex
- W1517623310
- Cited by
- 458
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
