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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.
Quality is one of the most important properties of a product. Providing the optimal quality can reduce costs for rework, scrap, recall or even legal actions while satisfying customers demand for reliability. The aim is to achieve ``built-in'' quality within product development process (PDP). The common approach therefore is the robust design optimization (RDO). It uses stochastic values as constraint and/or objective to obtain a robust and reliable optimal design. In classical approaches the effort required for stochastic analysis multiplies with the complexity of the optimization algorithm. The suggested approach shows that it is possible to reduce this effort enormously by using previously obtained data. Therefore the support point set of an underlying metamodel is filled iteratively during ongoing optimization in regions of interest if this is necessary. In a simple example, it will be shown that this is possible without significant loss of accuracy.
Over the last decade, the technology of constructing buildings has been dramatically developed especially with the huge growth of CAD tools that help in modeling buildings, bridges, roads and other construction objects. Often quality control and size accuracy in the factory or on construction site are based on manual measurements of discrete points. These measured points of the realized object or a part of it will be compared with the points of the corresponding CAD model to see whether and where the construction element fits into the respective CAD model. This process is very complicated and difficult even when using modern measuring technology. This is due to the complicated shape of the components, the large amount of manually detected measured data and the high cost of manual processing of measured values. However, by using a modern 3D scanner one gets information of the whole constructed object and one can make a complete comparison against the CAD model. It gives an idea about quality of objects on the whole. In this paper, we present a case study of controlling the quality of measurement during the constructing phase of a steel bridge by using 3D point cloud technology. Preliminary results show that an early detection of mismatching between real element and CAD model could save a lot of time, efforts and obviously expenses.
Known as a sophisticated phenomenon in civil engineering problems, soil structure interaction has been under deep investigations in the field of Geotechnics. On the other hand, advent of powerful computers has led to development of numerous numerical methods to deal with this phenomenon, resulting in a wide variety of methods trying to simulate the behavior of the soil stratum. This survey studies two common approaches to model the soil’s behavior in a system consisting of a structure with two degrees of freedom, representing a two-storey frame structure made of steel, with the column resting on a pile embedded into sand in laboratory scale. The effect of soil simulation technique on the dynamic behavior of the structure is of major interest in the study. Utilized modeling approaches are the so-called Holistic method, and substitution of soil with respective impedance functions.
One of the most promising and recent advances in computer-based planning is the transition from classical geometric modeling to building information modeling (BIM). Building information models support the representation, storage, and exchange of various information relevant to construction planning. This information can be used for describing, e.g., geometric/physical properties or costs of a building, for creating construction schedules, or for representing other characteristics of construction projects. Based on this information, plans and specifications as well as reports and presentations of a planned building can be created automatically. A fundamental principle of BIM is object parameterization, which allows specifying geometrical, numerical, algebraic and associative dependencies between objects contained in a building information model. In this paper, existing challenges of parametric modeling using the Industry Foundation Classes (IFC) as a federated model for integrated planning are shown, and open research questions are discussed.
PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS
(2010)
In this paper, a parameter identification procedure using Bayesian neural networks is proposed. Based on a training set of numerical simulations, where the material parameters are simulated in a predefined range using Latin Hypercube sampling, a Bayesian neural network, which has been extended to describe the noise of multiple outputs using a full covariance matrix, is trained to approximate the inverse relation from the experiment (displacements, forces etc.) to the material parameters. The method offers not only the possibility to determine the parameters itself, but also the accuracy of the estimate and the correlation between these parameters. As a result, a set of experiments can be designed to calibrate a numerical model.
This study contributes to the identification of coupled THM constitutive model parameters via back analysis against information-rich experiments. A sampling based back analysis approach is proposed comprising both the model parameter identification and the assessment of the reliability of identified model parameters. The results obtained in the context of buffer elements indicate that sensitive parameter estimates generally obey the normal distribution. According to the sensitivity of the parameters and the probability distribution of the samples we can provide confidence intervals for the estimated parameters and thus allow a qualitative estimation on the identified parameters which are in future work used as inputs for prognosis computations of buffer elements. These elements play e.g. an important role in the design of nuclear waste repositories.
It is well known that complex quaternion analysis plays an important role in the study of higher order boundary value problems of mathematical physics. Following the ideas given for real quaternion analysis, the paper deals with certain orthogonal decompositions of the complex quaternion Hilbert space into its subspaces of null solutions of Dirac type operator with an arbitrary complex potential. We then apply them to consider related boundary value problems, and to prove the existence and uniqueness as well as the explicit representation formulae of the underlying solutions.
Steel structural design is an integral part of the building construction process. So far, various methods of design have been applied in practice to satisfy the design requirements. This paper attempts to acquire the Differential Evolution Algorithms in automatization of specific synthesis and rationalization of design process. The capacity of the Differential Evolution Algorithms to deal with continuous and/or discrete optimization of steel structures is also demonstrated. The goal of this study is to propose an optimal design of steel frame structures using built-up I-sections and/or a combination of standard hot-rolled profiles. All optimized steel frame structures in this paper generated optimization solutions better than the original solution designed by the manufacturer. Taking the criteria regarding the quality and efficiency of the practical design into consideration, the produced optimal design with the Differential Evolution Algorithms can completely replace conventional design because of its excellent performance.
The sizing of simple resonators like guitar strings or laser mirrors is directly connected to the wavelength and represents no complex optimisation problem. This is not the case with liquid-filled acoustic resonators of non-trivial geometries, where several masses and stiffnesses of the structure and the fluid have to fit together. This creates a scenario of many competing and interacting resonances varying in relative strength and frequency when design parameters change. Hence, the resonator design involves a parameter-tuning problem with many local optima. As its solution evolutionary algorithms (EA) coupled to a forced-harmonic FE simulation are presented. A new hybrid EA is proposed and compared to two state-of-theart EAs based on selected test problems. The motivating background is the search for better resonators suitable for sonofusion experiments where extreme states of matter are sought in collapsing cavitation bubbles.