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- Membranbau, Flächentragwerk, parametrischer Entwurf, visuelle Programmierung, Interoperabilität, BIM, IFC, RF-COM, Grasshopper3D, Rhinoceros, Revit, Allplan (1)
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BIM im Membranbau
(2019)
Die fortschreitende Digitalisierung wird zurzeit zu einem der wichtigsten Themen und zugleich zu einer der größten Herausforderungen für die Bauwirtschaft. Building Information Modeling (BIM) kommt eine immer größere Bedeutung zu.
Es ist sicherlich richtig, dass derzeit nur wenige Projekte im Bauwesen den geforderten BIM-Anforderungen der Bauherrschaft genügen. Es ist zu erwarten, dass in den kommenden Jahren auch an Membranbauprojekte immer mehr Anforderungen bezüglich BIM gestellt werden. Aus diesem Grund ist es wichtig, dass sich Planungsbüros für leichte Flächentragwerke mit dem Thema BIM befassen.
Ziel dieser Arbeit ist die Ausarbeitung eines Konzepts für die effiziente durchgehende Umsetzung der Building Information Modeling-Arbeitsmethode bei der Tragwerksplanung im Membranbau.
Es werden vorhandene Funktionalitäten untersucht und alternative Interoperabilitätskomponenten entwickelt. Aufbauend auf den möglichen Werkzeugketten werden verschiedene Einsatzverfahren vorgeschlagen. Darauffolgend wird eines der möglichen Verfahren an einem realen Tragwerk implementiert. Die erhaltenen Ergebnisse werden einer kritischen Analyse unterzogen.
Abschließende Rückschlüsse, Beurteilung der angewandten Planungsmethoden und Ausblick fassen das behandelte Thema zusammen.
Bei der Arbeit verwendete Methoden schließen den parametrischen Entwurf, manuelle Modellierung in zwei verschiedenen Softwareumgebungen und textliche Programmierung mit C#-Sprache ein.
Die Relevanz des untersuchten Themas erstreckt sich überwiegend auf praktisch tätige Ingenieure aus den Bereichen leichte Flächentragwerke, Sondertragwerke, Membranbau, wird aber auch für wissenschaftliche Mitarbeiter der Forschungsinstitutionen, BIM-Spezialisten und Produkthersteller von Interesse sein.
In the field of autonomously driving vehicles the environment perception containing dynamic objects like other road users is essential. Especially, detecting other vehicles in the road traffic using sensor data is of utmost importance. As the sensor data and the applied system model for the objects of interest are noise corrupted, a filter algorithm must be used to track moving objects. Using LIDAR sensors one object gives rise to more than one measurement per time step and is therefore called extended object. This allows to jointly estimate the objects, position, as well as its orientation, extension and shape. Estimating an arbitrary shaped object comes with a higher computational effort than estimating the shape of an object that can be approximated using a basic geometrical shape like an ellipse or a rectangle. In the case of a vehicle, assuming a rectangular shape is an accurate assumption.
A recently developed approach models the contour of a vehicle as periodic B-spline function. This representation is an easy to use tool, as the contour can be specified by some basis points in Cartesian coordinates. Also rotating, scaling and moving the contour is easy to handle using a spline contour. This contour model can be used to develop a measurement model for extended objects, that can be integrated into a tracking filter. Another approach modeling the shape of a vehicle is the so-called bounding box that represents the shape as rectangle.
In this thesis the basics of single, multi and extended object tracking, as well as the basics of B-spline functions are addressed. Afterwards, the spline measurement model is established in detail and integrated into an extended Kalman filter to track a single extended object. An implementation of the resulting algorithm is compared with the rectangular shape estimator. The implementation of the rectangular shape estimator is provided. The comparison is done using long-term considerations with Monte Carlo simulations and by analyzing the results of a single run. Therefore, both algorithms are applied to the same measurements. The measurements are generated using an artificial LIDAR sensor in a simulation environment.
In a real-world tracking scenario detecting several extended objects and measurements that do not originate from a real object, named clutter measurements, is possible. Also, the sudden appearance and disappearance of an object is possible. A filter framework investigated in recent years that can handle tracking multiple objects in a cluttered environment is a random finite set based approach. The idea of random finite sets and its use in a tracking filter is recapped in this thesis. Afterwards, the spline measurement model is included in a multi extended object tracking framework. An implementation of the resulting filter is investigated in a long-term consideration using Monte Carlo simulations and by analyzing the results of a single run. The multi extended object filter is also applied to artificial LIDAR measurements generated in a simulation environment.
The results of comparing the spline based and rectangular based extended object trackers show a more stable performance of the spline extended object tracker. Also, some problems that have to be addressed in future works are discussed. The investigation of the resulting multi extended object tracker shows a successful integration of the spline measurement model in a multi extended object tracker. Also, with these results some problems remain, that have to be solved in future works.
This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements.
To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates.
In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis.
The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.