Principal Investigator(s):
Cathy French, Professor, Civil, Environmental and Geo-Engineering
Co-Investigators:
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Carol Shield, Former Professor, Civil, Environmental and Geo-Engineering
Project summary:
A better understanding of the time-dependent behavior of post-tensioned concrete structures is critical for developing long-term maintenance strategies. With this in mind, the time-dependent and temperature-dependent behavior of post-tensioned concrete bridges were investigated through a case study of the St. Anthony Falls Bridge, consisting of laboratory testing of concrete time-dependent behaviors (i.e., creep and shrinkage), examination of data from the in situ instrumented bridge, and time-dependent finite element models. Laboratory results for creep and shrinkage were measured for 3.5 years after casting, and the data were best predicted by the 1978 CEB/FIP Model Code provisions. To compare the in situ readings to constant-temperature finite element models, the time-dependent behavior was extracted from the measurements using linear regression. The creep and shrinkage rates of the in situ bridge were found to depend on temperature. An adjusted age using the Arrhenius equation was used to account for the interactions between temperature and time-dependent behavior in the measured data. Results from the time-dependent finite element models incorporating the full construction sequence revealed that the 1990 CEB/FIP Model Code and ACI-209 models best predicted the in situ behavior. Finite element analysis also revealed that problems associated with excessive deflections or development of tension over the lifetime of the bridge would be unlikely. The interactions between temperature and time-dependent behavior were further investigated using a simplified finite element model, which indicated that vertical deflections and stresses can be affected by the cyclic application of thermal gradients. The findings from this study were used to develop an anomaly detection routine for the linear potentiometer data, which was successfully used to identify short-term and long-term artificial anomalies in the data.