Soil-structure interaction may play a major role in the seismic response of a bridge structure. Specifically, soil layers of low stiffness and strength may result in permanent displacement of the abutments and foundations, thus imposing important kinematic conditions to the bridge structure. A study to illustrate such phenomena is undertaken based on three-dimensional nonlinear dynamic finite-element ͑FE͒ modeling and analysis ͑for a specific bridge configuration under a given seismic excitation͒. A bridge-foundationground model is developed based on the structural configuration and local soil conditions of the Humboldt Bay Middle Channel Bridge. The FE model and nonlinear solution strategy are built in the open-source software platform OpenSees of the Pacific Earthquake Engineering Research Center. Based on the simulation results, the overall system seismic response behavior is examined, as well as local deformations/stresses at selected critical locations. It is shown that permanent ground deformation may induce settlement and longitudinal/ transversal displacements of the abutments and deep foundations. The relatively massive approach ramps may also contribute to this simulated damage condition, which imposes large stresses on the bridge foundations, supporting piers, and superstructure.
Calibration, based on data from centrifuge and shake-table experiments continues to promote the development of more accurate computational models. Capabilities such as coupled solid-fluid formulations, and nonlinear incremental-plasticity approaches, allow for more realistic representations of the involved static and dynamic/seismic responses. In addition, contemporary high-performance parallel computing environments are permitting new insights, gained from analyses of entire ground-foundation-structural systems. On this basis, the horizon is expanding for large-scale numerical simulations to further contribute towards the evolution of more accurate analysis and design strategies. The studies presented herein address this issue through recently conducted three-dimensional (3D) representative research efforts that simulate the seismic response of: i) a shallow-foundation liquefaction countermeasure, ii) a pile-supported wharf, and iii) a full bridge-ground system. A discussion of enabling tools for routine usage of such 3D simulation environments is also presented, as an important element in support of wider adoption and practical applications. In this regard, graphical user interfaces and visualization approaches can play a critical role.
A damage detection approach is developed using nonlinear autoregressive with exogenous inputs (NARX) neural networks and a statistical inference technique. Within a large spatially extended dynamic system, an instrumented local substructure may be represented by a neural network, to predict the dynamic response of a given sensor from that of its neighbors. Without change in the system properties, the network prediction error will follow a stable statistical distribution. To infer damage, change in the prediction error variance as evaluated by the statistical inference standard F test is utilized as a sensitive indicator. Validation of the described procedure is undertaken using two experimental data sets (from the Los Alamos National Laboratory in Los Alamos, NM). Reduced stiffness and nonlinear response of a massspring system is documented in the first set, while joint damage in a frame structure is explored in the second. Favorable results are obtained in both cases with linear/nonlinear and single/multidamage patterns. Overall, the proposed framework may be particularly efficient for large spatially extended sensor network situations, where local condition assessment may be conducted based on the response of a few neighboring sensors.
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