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THE INFLUENCE OF THE LOCAL CONCAVITY ON THE FUNCTIONING OF BEARING SHELL OF HIGH-RISE CONSTRUCTION
(2012)
Areas with various defects and damages, which reduce carrying capacity, were examined in a study of metal chimneys. In this work, the influence of the local dimples on the function of metal chimneys was considered. Modeling tasks were completed in the software packages LIRA and ANSYS. Parameters were identified, which characterize the local dimples, and a numerical study of the influence of local dimples on the stress-strain state of shells of metal chimneys was conducted. A distribution field of circular and meridional tension was analyzed in a researched area. Zones of influence of dimples on the bearing cover of metal chimneys were investigated. The bearing capacities of high-rise structures with various dimple geometries and various cover parameters were determined with respect to specified areas of the trunk. Dependent relationships are represented graphically for the decrease in bearing capacity of a cover with respect to dimples. Diameter and thickness of covers of metal chimneys were constructed according to the resulting data.
The process of analysis and design in structural engineering requires the consideration of different partial models, for example loading, structural materials, structural elements, and analysis types. The various partial models are combined by coupling several of their components. Due to the large number of available partial models describing similar phenomena, many different model combinations are possible to simulate the same aspects of a structure. The challenging task of an engineer is to select a model combination that ensures a sufficient, reliable prognosis. In order to achieve this reliable prognosis of the overall structural behavior, a high individual quality of the partial models and an adequate coupling of the partial models is required. Several methodologies have been proposed to evaluate the quality of partial models for their intended application, but a detailed study of the coupling quality is still lacking. This paper proposes a new approach to assess the coupling quality of partial models in a quantitative manner. The approach is based on the consistency of the coupled data and applies for uni- and bidirectional coupled partial models. Furthermore, the influence of the coupling quality on the output quantities of the partial models is considered. The functionality of the algorithm and the effect of the coupling quality are demonstrated using an example of coupled partial models in structural engineering.
Numerical simulations in the general field of civil engineering are common for the design process of structures and/or the assessment of existing buildings. The behaviour of these structures is analytically unknown and is approximated with numerical simulation methods like the Finite Element Method (FEM). Therefore the real structure is transferred into a global model (GM, e.g. concrete bridge) with a wide range of sub models (partial models PM, e.g. material modelling, creep). These partial models are coupled together to predict the behaviour of the observed structure (GM) under different conditions. The engineer needs to decide which models are suitable for computing realistically and efficiently the physical processes determining the structural behaviour. Theoretical knowledge along with the experience from prior design processes will influence this model selection decision. It is thus often a qualitative selection of different models. The goal of this paper is to present a quantitative evaluation of the global model quality according to the simulation of a bridge subject to direct loading (dead load, traffic) and indirect loading (temperature), which induce restraint effects. The model quality can be separately investigated for each partial model and also for the coupled partial models in a global structural model. Probabilistic simulations are necessary for the evaluation of these model qualities by using Uncertainty and Sensitivity Analysis. The method is applied to the simulation of a semi-integral concrete bridge with a monolithic connection between the superstructure and the piers, and elastomeric bearings at the abutments. The results show that the evaluation of global model quality is strongly dependent on the sensitivity of the considered partial models and their related quantitative prediction quality. This method is not only a relative comparison between different models, but also a quantitative representation of model quality using probabilistic simulation methods, which can support the process of model selection for numerical simulations in research and practice.
Safety operation of important civil structures such as bridges can be estimated by using fracture analysis. Since the analytical methods are not capable of solving many complicated engineering problems, numerical methods have been increasingly adopted. In this paper, a part of isotropic material which contains a crack is considered as a partial model and the proposed model quality is evaluated. EXtended IsoGeometric Analysis (XIGA) is a new developed numerical approach [1, 2] which benefits from advantages of its origins: eXtended Finite Element Method (XFEM) and IsoGeometric Analysis (IGA). It is capable of simulating crack propagation problems with no remeshing necessity and capturing singular field at the crack tip by using the crack tip enrichment functions. Also, exact representation of geometry is possible using only few elements. XIGA has also been successfully applied for fracture analysis of cracked orthotropic bodies [3] and for simulation of curved cracks [4]. XIGA applies NURBS functions for both geometry description and solution field approximation. The drawback of NURBS functions is that local refinement cannot be defined regarding that it is based on tensorproduct constructs unless multiple patches are used which has also some limitations. In this contribution, the XIGA is further developed to make the local refinement feasible by using Tspline basis functions. Adopting a recovery based error estimator in the proposed approach for evaluation of the model quality and performing the adaptive processes is in progress. Finally, some numerical examples with available analytical solutions are investigated by the developed scheme.
Non-destructive techniques for damage detection became the focus of engineering interests in the last few years. However, applying these techniques to large complex structures like civil engineering buildings still has some limitations since these types of structures are
unique and the methodologies often need a large number of specimens for reliable results. For this reason, cost and time can greatly influence the final results.
Model Assisted Probability Of Detection (MAPOD) has taken its place among the ranks of damage identification techniques, especially with advances in computer capacity and modeling tools. Nevertheless, the essential condition for a successful MAPOD is having a reliable model in advance. This condition is opening the door for model assessment and model quality problems. In this work, an approach is proposed that uses Partial Models (PM) to compute the Probability Of damage Detection (POD). A simply supported beam, that can be structurally modified and
tested under laboratory conditions, is taken as an example. The study includes both experimental and numerical investigations, the application of vibration-based damage detection approaches and a comparison of the results obtained based on tests and simulations.
Eventually, a proposal for a methodology to assess the reliability and the robustness of the models is given.
The present research analyses the error on prediction obtained under different data availability scenarios to determine which measurements contribute to an improvement of model prognosis and which not. A fully coupled 2D hydromechanical model of a water retaining dam is taken as an example. Here, the mean effective stress in the porous skeleton is reduced due to an increase in pore water pressure under drawdown conditions. Relevant model parameters are ranked by scaled sensitivities, Particle Swarm Optimization is applied to determine the optimal parameter values and model validation is performed to determine the magnitude of error forecast. We compare the predictions of the optimized models with results from a forward run of the reference model to obtain actual prediction errors.
The analyses presented here were performed to 31 data sets of 100 observations of varying data types. Calibrating with multiple information types instead of only one sort, brings better calibration results and improvement in model prognosis. However, when using several types of information the number of observations have to be increased to be able to cover a representative part of the model domain; otherwise a compromise between data availability and domain
coverage prove best. Which type of information for calibration contributes to the best prognoses, could not be determined in advance. For the error in model prognosis does not depends on the error in calibration, but on the parameter error, which unfortunately can not be determined in reality since we do not know its real value. Excellent calibration fits with parameters’ values near the limits of reasonable physical values, provided the highest prognosis errors. While models which included excess pore pressure values for calibration provided the best prognosis, independent of the calibration fit.
Long-span cable supported bridges are prone to aerodynamic instabilities caused by wind and this phenomenon is usually a major design criterion. If the wind speed exceeds the critical flutter speed of the bridge, this constitutes an Ultimate Limit State. The prediction of the flutter boundary therefore requires accurate and robust models. This paper aims at studying various combinations of models to predict the flutter phenomenon.
Since flutter is a coupling of aerodynamic forcing with a structural dynamics problem, different types and classes of models can be combined to study the interaction. Here, both numerical approaches and analytical models are utilised and coupled in different ways to assess the prediction quality of the hybrid model. Models for aerodynamic forces employed are the analytical Theodorsen expressions for the motion-enduced aerodynamic forces of a flat plate and Scanlan derivatives as a Meta model. Further, Computational Fluid Dynamics (CFD) simulations using the Vortex Particle Method (VPM) were used to cover numerical models.
The structural representations were dimensionally reduced to two degree of freedom section models calibrated from global models as well as a fully three-dimensional Finite Element (FE) model. A two degree of freedom system was analysed analytically as well as numerically.
Generally, all models were able to predict the flutter phenomenon and relatively close agreement was found for the particular bridge. In conclusion, the model choice for a given practical analysis scenario will be discussed in the context of the analysis findings.
Reinforced concrete walls are commonly selected as the lateral resisting systems in seismic design of buildings. The design procedure requires reliable/robust models to predict the wall response. Many researchers, thus, have focused on using the available experimental data to be able to comment on the quality of models at hand. What is missing though is an uncertain attitude towards the experimental data since such data can be affected by different sources of uncertainty. In this paper, we introduce the database created for model quality evaluation purposes considering the uncertainties in the experimental data. This is the first step of a larger study on experience-based model quality evaluation of reinforced concrete walls. Here, we briefly present the database as well as six sample validations of the developed numerical model (the quality of which is to be assessed). The database contains the information on nearly 300 wall specimens from about 50 sources. Both the database and the numerical model, built for uncertainty/sensitivity analysis purposes, are mainly based on ten parameters. These include geometry, material, reinforcement layout and loading properties. The validation results prove that the model is able to predict the wall response satisfactorily. Consequently, the validated numerical model could be used in further quality evaluation studies.
The planning process in civil engineering is highly complex and not manageable in its entirety.
The state of the art decomposes complex tasks into smaller, manageable sub-tasks. Due to the close interrelatedness of the sub-tasks, it is essential to couple them. However, from a software engineering point of view, this is quite challenging to do because of the numerous incompatible software applications on the market. This study is concerned with two main objectives: The first is the generic formulation of coupling strategies in order to support engineers in the implementation and selection of adequate coupling strategies. This has been achieved by the use of a coupling pattern language combined with a four-layered, metamodel architecture, whose applicability has been performed on a real coupling scenario. The second one is the quality assessment of coupled software. This has been developed based on the evaluated schema mapping. This approach has been described using mathematical expressions derived from the set theory and graph theory by taking the various mapping patterns into account. Moreover, the coupling quality has been evaluated within the formalization process by considering the uncertainties that arise during mapping and has resulted in global quality values, which can be used by the user to assess the exchange. Finally, the applicability of the proposed approach has been shown using an engineering case study.
Methods for model quality assessment are aiming to find the most appropriate model with respect to accuracy and computational effort for a structural system under investigation. Model error estimation techniques can be applied for this purpose when kinematical models are investigated. They are counted among the class of white box models, which means that the model hierarchy and therewith the best model is known. This thesis gives an overview of discretisation error estimators. Deduced from these, methods for model error estimation are presented. Their general goal is to make a prediction of the inaccuracies that are introduced using the simpler model without knowing the solution of a more complex model. This information can be used to steer an adaptive process. Techniques for linear and non-linear problems as well as global and goal-oriented errors are introduced. The estimation of the error in local quantities is realised by solving a dual problem, which serves as a weight for the primal error. So far, such techniques have mainly been applied in
material modelling and for dimensional adaptivity. Within the scope of this thesis, available model error estimators are adapted for an application to kinematical models. Their applicability is tested regarding the question of whether a geometrical non-linear calculation is necessary or not. The analysis is limited to non-linear estimators due to the structure of the underlying differential equations. These methods often involve simplification, e.g linearisations. It is investigated to which extent such assumptions lead to meaningful results, when applied to kinematical models.