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The paper proposes a new method for general 3D measurement and 3D point reconstruction. Looking at its features, the method explicitly aims at practical applications. These features especially cover low technical expenses and minimal user interaction, a clear problem separation into steps that are solved by simple mathematical methods (direct, stable and optimal with respect to least error squares), and scalability. The method expects the internal and radial distortion parameters of the used camera(s) as inputs, and a plane quadrangle with known geometry within the scene. At first, for each single picture the 3D position of the reference quadrangle (with respect to each camera coordinate frame) is calculated. These 3D reconstructions of the reference quadrangle are then used to yield the relative external parameters of each camera regarding the first one. With known external parameters, triangulation is finally possible. The differences from other known procedures are outlined, paying attention to the stable mathematical methods (no usage of nonlinear optimization) and the low user interaction with good results at the same time.
Summer overheating in buildings is a common problem, especially in office buildings with large glazed facades, high internal loads and low thermal mass. Phase change materials (PCM) that undergo a phase transition in the temperature range of thermal comfort can add thermal mass without increasing the structural load of the building. The investigated PCM were micro-encapsulated and mixed into gypsum plaster. The experiments showed a reduction of indoor-temperature of up to 4 K when using a 3 cm layer of PCM-plaster with micro-encapsulated paraffin. The measurement results could validate a numerical model that is based on a temperature dependent function for heat capacity. Thermal building simulation showed that a 3 cm layer of PCM-plaster can help to fulfil German regulations concerning heat protection of buildings in summer for most office rooms.
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.
Traffic simulation is a valuable tool for the design and evaluation of road networks. Over the years, the level of detail to which urban and freeway traffic can be simulated has increased steadily, shifting from a merely qualitative macroscopic perspective to a very detailed microscopic view, where the behavior of individual vehicles is emulated realistically. With the improvement of behavioral models, however, the computational complexity has also steadily increased, as more and more aspects of real-life traffic have to be considered by the simulation environment. Despite the constant increase in computing power of modern personal computers, microscopic simulation stays computationally expensive, limiting the maximum network size than can be simulated on a single-processor computer in reasonable time. Parallelization can distribute the computing load from a single computer system to a cluster of several computing nodes. To this end, the exisiting simulation framework had to be adapted to allow for a distributed approach. As the simulation is ultimately targeted to be executed in real-time, incorporating real traffic data, only a spatial partition of the simulation was considered, meaning the road network has to be partitioned into subnets of comparable complexity, to ensure a homogenous load balancing. The partition process must also ensure, that the division between subnets does only occur in regions, where no strong interaction between the separated road segments occurs (i.e. not in the direct vicinity of junctions). In this paper, we describe a new microscopic reasoning voting strategy, and discuss in how far the increasing computational costs of these more complex behaviors lend themselves to a parallelized approach. We show the parallel architecture employed, the communication between computing units using MPIJava, and the benefits and pitfalls of adapting a single computer application to be used on a multi-node computing cluster.
In civil engineering it is very difficult and often expensive to excite constructions such as bridges and buildings with an impulse hammer or shaker. This problem can be avoided with the output-only method as special feature of stochastic system identification. The permanently existing ambient noise (e.g. wind, traffic, waves) is sufficient to excite the structures in their operational conditions. The output-only method is able to estimate the observable part of a state-space-model which contains the dynamic characteristics of the measured mechanical system. Because of the assumption that the ambient excitation is white there is no requirement to measure the input. Another advantage of the output-only method is the possibility to get high detailed models by a special method, called polyreference setup. To pretend the availability of a much larger set of sensors the data from varying sensor locations will be collected. Several successive data sets are recorded with sensors at different locations (moving sensors) and fixed locations (reference sensors). The covariance functions of the reference sensors are bases to normalize the moving sensors. The result of the following subspace-based system identification is a high detailed black-box-model that contains the weighting function including the well-known dynamic parameters eigenfrequencies and mode shapes of the mechanical system. Emphasis of this lecture is the presentation of an extensive damage detection experiment. A 53-year old prestressed concrete tied-arch-bridge in Hünxe (Germany) was deconstructed in 2005. Preliminary numerous vibration measurements were accomplished. The first experiment for system modification was an additional support near the bridge bearing of one main girder. During a further experiment one hanger from one tied arch was cut through as an induced damage. Some first outcomes of the described experiments will be presented.
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.
This paper deals with the development of a new multi-objective evolution strategy in combination with an integrated pollution-load and water-quality model. The optimization algorithm combines the advantages of the Non-Dominated Sorting Genetic Algorithm and Self-Adaptive Evolution Strategies. The identification of a good spread of solutions on the pareto-optimum front and the optimization of a large number of decision variables equally demands numerous simulation runs. In addition, statements with regard to the frequency of critical concentrations and peak discharges require continuous long-term simulations. Therefore, a fast operating integrated simulation model is needed providing the required precision of the results. For this purpose, a hydrological deterministic pollution-load model has been coupled with a river water-quality and a rainfall-runoff model. Wastewater treatment plants are simulated in a simplified way. The functionality of the optimization and simulation tool has been validated by analyzing a real catchment area including sewer system, WWTP, water body and natural river basin. For the optimization/rehabilitation of the urban drainage system, both innovative and approved measures have been examined and used as decision variables. As objective functions, investment costs and river water quality criteria have been used.
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.