Hurricane Katrina caused significant damage to the transportation system in the Gulf Coast region. The overall cost to repair or replace the bridges damaged during the hurricane is estimated at over $1 billion. This paper describes the observed damage patterns to bridges, including damage attributed to storm surge, wind, impact from debris, scour, and water inundation, as well as examples of repair measures used to quickly restore functionality to the bridges and transportation system. Using the data from the 44 bridges that were damaged, relationships between storm surge elevation, damage level, and repair costs are developed. The analysis reveals that, in general, regions with higher storm surge had more damage, although there were several instances where this was not the case, primarily due to damage resulting from debris impact. It is also shown that a highly nonlinear relationship exists between the normalized repair cost and the damage state. The paper concludes with a brief discussion on the effica...

Existing nonlinear static (pushover) methods of analysis establish the capacity curve of a structure with respect to the roof displacement. Disproportionate increases in the roof displacement, and even outright reversals in the case of higher mode pushover analyses, can distort the capacity curve of the “equivalent” SDOF system. Rather than viewing pushover analyses from the perspective of roof displacement, this paper considers the energy absorbed (or the work done) in the pushover analysis. Simple relations establish an energy-based displacement that is equivalent to the spectral displacement obtained by conventional pushover analysis methods within the linear elastic domain. Extensions to the nonlinear domain allow pushover curves to be established that resemble tradi-tional first mode pushover curves and which correct anomalies observed in some higher mode pushover curves. An example illustrates the application of a modified Multimode Pushover Analysis procedure using Yield Point Spectra.

Seismic fragility relationships, including the soil–structure interaction (SSI) of a common bridge configuration in central and eastern USA, are derived in this study. Four different modelling methods are adopted to represent abutments and foundations of the bridge, namely, (a) fixed abutments and foundations, (b) lumped springs developed from conventional pile analysis of piles at abutments and foundations, (c) lumped springs developed from three-dimensional finite element (3D FE) analysis of abutments and foundations and (d) 3D FE models. Seismic demand on the bridge components is estimated from inelastic response history analysis of the SSI systems. Finally, fragility curves of the components and bridge system are derived. The four different SSI approaches result in different seismic fragility. The implication of this work is that careful consideration is necessary when selecting an analytical representation of a soil and foundation system to obtain reliable earthquake impact assessment. In addition, it is found that abutment bearings are the most critical components for the studied bridge configuration.

The response of a hysteretic system is determined not only by the instantaneous external force but also by the loading history; thereby, a nonlinear time history analysis is needed for the accurate prediction of dynamic responses. The authors recently developed deep neural network (DNN) models for near‐real‐time seismic response predictions of hysteretic systems (Kim et al., 2019). The DNN models outperform existing regression‐based prediction methods for the idealized hysteretic systems used for the training. Structural systems often show complex hysteretic behavior such as degradation (in stiffness or strength) and pinching effects. In this paper, we develop DNN models for hysteretic systems having degradation and pinching. First, a new Bouc‐Wen class model, termed a modified Bouc‐Wen‐Baber‐Noori (m‐BWBN) model, is proposed to introduce the yield strength as an explicit model parameter. The feasible parameter domains are also specified to promote the practical use of the m‐BWBN model. Second, a seismic demand database is constructed by nonlinear time history analyses using the m‐BWBN model and many ground motions. Third, we propose a new DNN architecture and detailed training methodologies to learn the effects of the complex hysteretic characteristics on the peak seismic responses. Numerical examples of reinforced concrete structures are introduced to test the prediction performance and applicability of the proposed DNN model. The source codes, data, and trained models are available for download at http://ERD2.snu.ac.kr.