Live-Load Distribution Factors for Prestressed Concrete, Spread Box-Girder Bridge

Because the methods used to compute the live load distribution for moment and shear force in modern highway bridges subjected to vehicle loading are generally constrained by their range of applicability, refined analysis methods are necessary when this range is exceeded or new materials are used. This study developed a simplified method to calculate the live load distribution factors for skewed composite slab-on-girder bridges with high-performance-steel (HPS) girders whose parameters exceed the range of applicability defined by the American Association of State Highway and Transportation Officials (AASHTO)’s Load and Resistance Factor Design (LRFD) specifications. Bridge databases containing information on actual bridges and prototype bridges constructed from three different types of steel and structural parameters that exceeded the range of applicability were developed and the bridge modeling verified using results reported for field tests of actual bridges. The resulting simplified equations for the live load distribution factors of shear force and bending moment were based on a rigorous statistical analysis of the data. The proposed equations provided comparable results to those obtained using finite element analysis, giving bridge engineers greater flexibility when designing bridges with structural parameters that are outside the range of applicability defined by AASHTO in terms of span length, skewness, and bridge width.

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Live Load Distribution Factors for Moment in NEXT Beam Bridges

Journal of Bridge Engineering ◽

10.1061/(asce)be.1943-5592.0001202 ◽

2018 ◽

Vol 23 (3) ◽

pp. 06017010 ◽

Cited By ~ 2

Author(s):

Jianwei Huang ◽

Jonathan Davis

Keyword(s):

Load Distribution ◽

Live Load ◽

Live Load Distribution Factors ◽

Live Load Distribution ◽

Load Distribution Factors

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Live-load strain evaluation of the prestressed concrete box-girder bridge using deep learning and clustering

Structural Health Monitoring ◽

10.1177/1475921719875630 ◽

2019 ◽

Vol 19 (4) ◽

pp. 1051-1063 ◽

Cited By ~ 2

Author(s):

Hanwei Zhao ◽

Youliang Ding ◽

Aiqun Li ◽

Zhaozhao Ren ◽

Kang Yang

Keyword(s):

Deep Learning ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Prestressed Concrete ◽

Gaussian Mixture ◽

Live Load ◽

Box Girder ◽

Concrete Box Girder ◽

Box Girder Bridge ◽

Girder Bridge

The monitoring data makes it feasible to quickly evaluate the cracking of the prestressed concrete box-girder bridge. The live-load strain can accurately quantify the load effect and cracking of bridges due to its explicit datum point of signal. Based on the live-load strain data from bridge monitoring system, this study develops a comprehensive data-driven method of state evaluation and cracking early warning for the prestressed concrete box-girder bridge. The feature of vehicle-induced strain is extracted using the deep learning and classification of long short-term memory network. The vehicle-induced strain features are clustered via Gaussian mixture model, and the cracking early warning of the bridge is conducted according to the reliability of heavy vehicle clustering data. This method can be used as an indicator for the bridge inspection, truck-weight-limit and reinforcement work. The results demonstrate that (1) using the long short-term memory network, a deep learning model can be trained to intelligently classify the non-stationary and stationary sections of vehicle-induced strains, of which the test accuracy of classification surpasses 99%, and (2) according to the Gaussian mixture model probability distribution of data, the vehicle-induced strain features can be clustered by the corresponding Gaussian mixture model crest, which is the premise for reflecting relational mapping between vehicle loading and strain response.

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