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Large-scale Models (large-scale + models)
Selected AbstractsCyclic tests on large-scale models of existing bridge piers with rectangular hollow cross-sectionEARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 13 2003A. V. Pinto Abstract Cyclic tests on two large-scale models of existing bridge piers with rectangular hollow cross-section were performed in the ELSA laboratory. The prototype structure is an existing reinforced concrete highway bridge constructed in Austria in 1975. The piers presented several seismic deficiencies and consequently they showed poor hysteretic behaviour and limited deformation capacity as well as undesirable failure modes that do not comply with the requirements of modern codes for seismic-resistant structures. Experimental data are compared to numerical and empirical predictions. Copyright © 2003 John Wiley & Sons, Ltd. [source] Accelerating strategies to the numerical simulation of large-scale models for sequential excavationINTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, Issue 9 2007M. Noronha Abstract In this paper, a novel combination of well-established numerical procedures is explored in order to accelerate the simulation of sequential excavation. Usually, large-scale models are used to represent these problems. Due to the high number of equations involved, the solver algorithm represents the critical aspect which makes the simulation very time consuming. The mutable nature of the excavation models makes this problem even more pronounced. To accomplish the representation of geometrical and mechanical aspects in an efficient and simple manner, the proposed solution employs the boundary element method with a multiple-region strategy. Together with this representational system, a segmented storage scheme and a time-ordered tracking of the changes form an adequate basis for the usage of fast updating methods instead of frontal solvers. The present development employs the Sherman,Morrison,Woodbury method to speed up the calculation due to sequential changes. The efficiency of the proposed framework is illustrated through the simulation of test examples of 2D and 3D models. Copyright © 2006 John Wiley & Sons, Ltd. [source] Back analysis of model parameters in geotechnical engineering by means of soft computingINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 14 2003B. Pichler Abstract In this paper, a parameter identification (PI) method for determination of unknown model parameters in geotechnical engineering is proposed. It is based on measurement data provided by the construction site. Model parameters for finite element (FE) analyses are identified such that the results of these calculations agree with the available measurement data as well as possible. For determination of the unknown model parameters, use of an artificial neural network (ANN) is proposed. The network is trained to approximate the results of FE simulations. A genetic algorithm (GA) uses the trained ANN to provide an estimate of optimal model parameters which, finally, has to be assessed by an additional FE analysis. The presented mode of PI renders back analysis of model parameters feasible even for large-scale models as used in geotechnical engineering. The advantages of theoretical developments concerning both the structure and the training of the ANN are illustrated by the identification of material properties from experimental data. Finally, the performance of the proposed PI method is demonstrated by two problems taken from geotechnical engineering. The impact of back analysis on the actual construction process is outlined. Copyright © 2003 John Wiley & Sons, Ltd. [source] | ||||||||||