Blind Prediction of Shaking Table Tests of a New Bridge Bent Design, PEER Report 2020-09

Abstract: 

Considering the importance of the transportation network and bridge structures, the associated seismic design philosophy is shifting from the basic collapse prevention objective to maintaining functionality on the community scale in the aftermath of moderate to strong earthquakes (i.e., resiliency). In addition to performance, the associated construction philosophy is also being modernized, with the utilization of accelerated bridge construction (ABC) techniques to reduce impacts of construction work on traffic, society, economy, and on-site safety during construction.

Recent years have seen several developments towards the design of low-damage bridges and ABC. According to the results of conducted tests, these systems have significant potential to achieve the intended community resiliency objectives. Taking advantage of such potential in the standard design and analysis processes requires proper modeling that adequately characterizes the behavior and response of these bridge systems.

To evaluate the current practices and abilities of the structural engineering community to model this type of resiliency-oriented bridges, the Pacific Earthquake Engineering Research Center (PEER) organized a blind prediction contest of a two-column bridge bent consisting of columns with enhanced response characteristics achieved by a well-balanced contribution of self-centering, rocking, and energy dissipation.

The parameters of this blind prediction competition are described in this report, and the predictions submitted by different teams are analyzed. In general, forces are predicted better than displacements. The post-tension bar forces and residual displacements are predicted with the best and least accuracy, respectively. Some of the predicted quantities are observed to have coefficient of variation (COV) values larger than 50%; however, in general, the scatter in the predictions amongst different teams is not significantly large.

Applied ground motions (GM) in shaking table tests consisted of a series of naturally recorded earthquake acceleration signals, where GM1 is found to be the largest contributor to the displacement error for most of the teams, and GM7 is the largest contributor to the force (hence, the acceleration) error. The large contribution of GM1 to the displacement error is due to the elastic response in GM1 and the errors stemming from the incorrect estimation of the period and damping ratio. The contribution of GM7 to the force error is due to the errors in the estimation of the base-shear capacity. Several teams were able to predict forces and accelerations with only moderate bias. Displacements, however, were systematically underestimated by almost every team. This suggests that there is a general problem either in the assumptions made or the models used to simulate the response of this type of bridge bent with enhanced response characteristics. Predictions of the best-performing teams were consistently and substantially better than average in all response quantities. The engineering community would benefit from learning details of the approach of the best teams and the factors that caused the models of other teams to fail to produce similarly good results.

Blind prediction contests provide: (1) very useful information regarding areas where current numerical models might be improved; and (2) quantitative data regarding the uncertainty of analytical models for use in performance-based earthquake engineering evaluations. Such blind prediction contests should be encouraged for other experimental research activities and are planned to be conducted annually by PEER.

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Author: 
Selim Gunay
Fan Hu
Khalid M. Mosalam
Arpit Nema
Jose Restrepo
Adam Zsarnoczay
Jack Baker
Publication date: 
October 25, 2020
Publication type: 
Technical Report
Citation: 
Gunay et al. (2020). Blind Prediction of Shaking Table Tests of a New Bridge Bent Design (Report No. 2020/09). Pacific Earthquake Engineering Research Center (PEER). https://peer.berkeley.edu/sites/default/files/gunay_2020_09.pdf