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http://dx.doi.org/10.5762/KAIS.2018.19.3.96

Calculation of Maximum Effective Temperature of Steel Box Girder Bridge Using Artificial Neural Network  

Lee, Seong- Haeng (Department of Civil Engineering, Pusan National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.19, no.3, 2018 , pp. 96-103 More about this Journal
Abstract
An analysis using a statistical method is generally used to determine the effective temperature based on the temperature design load of a bridge. In this study, the effective temperature was calculated by building an artificial neural network (ANN) capable of improving the statistical method. A Steel box girder bridge specimen was made with a width of 2.0 m, height of 2.0 m, and length of 3.0 m and 0.2 m the upper slab. Twenty one temperature gauges were attached to measure the temperature between 2014 and 2016 for three years. An ANN was learned using the data measured from 2014~2015 and the results were compared with the Euro codes. The error rate between the Euro code and statistical analysis values was analyzed to be 4.1 % for the total measurement point. The ANN was verified and the effective bridge temperatures were calculated using the temperature data measured in 2016. The results revealed an approximate 3.97 % difference from the statistical analysis values. This degree of error is considered to be acceptable in terms of engineering for the analysis of an ANN. An ANN can easily predict the effective temperature of a bridge by knowing the input values of the region's highest temperature, bridge type, and upper asphalt thickness when designing the bridge's temperature loads.
Keywords
Artificial neural network; Euro code; Maximum effective temperature; Steel box girder bridge specimen; Temperature measurement;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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