Field calibration of time-dependent behavior in segmental bridges using self-learning simulation | Posted on:2005-03-23 | Degree:Ph.D | Type:Thesis | University:University of Illinois at Urbana-Champaign | Candidate:Jung, Sungmoon | Full Text:PDF | GTID:2452390008479106 | Subject:Engineering | Abstract/Summary: | | Creep and shrinkage of concrete are complex phenomena that are difficult to model. Consequently, model prediction and real behavior in the field often show a significant difference. The conventional approach to improve the prediction begins with a choice of a constitutive model followed by calibration of the model parameters. However, due to the inherent error of constitutive models, the improvement is limited.; A new approach of self-learning simulation is adopted in the thesis to characterize the time-dependent behavior of concrete. The method does not require a predefined constitutive model. The material model becomes gradually more accurate during the simulation procedure using the measured displacements. A rate-dependent neural network material model is introduced and integrated with the self-learning simulation.; The proposed method is applied to both synthetic and experimental examples for verification and then applied to the field calibration of a concrete segmental bridge. Deflection measurements of the bridge during the construction are utilized to characterize the time-dependent behavior of the concrete in the field. The obtained material behavior is used to provide updated casting curves and estimation of the long-term behavior of the bridge.; The method integrates computational simulation and construction. It can be applied to characterize any material behavior, including the time-dependent behavior of concrete in segmental bridges. The study presents a scheme to improve the camber of segmental bridges using a self-learning simulation. | Keywords/Search Tags: | Behavior, Segmental bridges, Self-learning simulation, Concrete, Using, Model, Field, Calibration | | Related items |
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