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BIM-basierte Digitalisierung von Bestandsgebäuden aus Sicht des FM am Beispiel von Heizungsanlagen (2022)
Miller, Martin
Das Ziel der Arbeit ist, für das Facility Management relevante Informationen für die mit Building Information Modeling basierende Erstellung von Bestandsgebäuden am Beispiel einer Hei- zungsanlage zu definieren. Darauf basierend sind die notwendigen Arbeitsschritte der Objek- taufnahme abgeleitet. Für die Definition der Arbeitsschritte wurden das grundlegende Vorge- hen bei einer Objektaufnahme sowie die gesetzlichen Gegebenheiten für den Betrieb einer Heizungsanlage dargelegt. Darüber hinaus sind in der vorliegenden Ausarbeitung die Vorteile und Herausforderungen hinsichtlich des Zusammenspiels von Building Information Modeling und Facility Management analysiert. Die definierten Arbeitsschritte sind anhand eines Beispiel- projektes angewendet worden. Im Rahmen des Beispielprojekts sind die entscheidenden Be- triebsdaten je Anlagenteil in Form von Informationsanforderungen nach DIN 17412 definiert. Das Gebäudemodell ist durch Parameter mit den für das Facility Management relevanten In- formationen ergänzt. Die Resultate des Beispielprojektes sind mit aussagekräftigen Schnitten, Plänen sowie 3-D-Visualisierungen dargestellt. Abschließend sind die Ergebnisse in Bezug auf das FM validiert. Aus den Arbeitsschritten und Ergebnissen ist eine Leitlinie erstellt worden für den Digitalisierungsprozess von Bestandsgebäuden für das Facility Management.
Framing Algorithm-Driven Development of Sets of Objectives Using Elementary Interactions (2022)
Söbke, Heinrich ; Lück, Andrea
Multi-criteria decision analysis (MCDA) is an established methodology to support the decision-making of multi-objective problems. For conducting an MCDA, in most cases, a set of objectives (SOO) is required, which consists of a hierarchical structure comprised of objectives, criteria, and indicators. The development of an SOO is usually based on moderated development processes requiring high organizational and cognitive effort from all stakeholders involved. This article proposes elementary interactions as a key paradigm of an algorithm-driven development process for an SOO that requires little moderation efforts. Elementary interactions are self-contained information requests that may be answered with little cognitive effort. The pairwise comparison of elements in the well-known analytical hierarchical process (AHP) is an example of an elementary interaction. Each elementary interaction in the development process presented contributes to the stepwise development of an SOO. Based on the hypothesis that an SOO may be developed exclusively using elementary interactions (EIs), a concept for a multi-user platform is proposed. Essential components of the platform are a Model Aggregator, an Elementary Interaction Stream Generator, a Participant Manager, and a Discussion Forum. While the latter component serves the professional exchange of the participants, the first three components are intended to be automatable by algorithms. The platform concept proposed has been evaluated partly in an explorative validation study demonstrating the general functionality of the algorithms outlined. In summary, the platform concept suggested demonstrates the potential to ease SOO development processes as the platform concept does not restrict the application domain; it is intended to work with little administration moderation efforts, and it supports the further development of an existing SOO in the event of changes in external conditions. The algorithm-driven development of SOOs proposed in this article may ease the development of MCDA applications and, thus, may have a positive effect on the spread of MCDA applications.
Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings (2022)
Kumari, Vandana ; Harirchian, Ehsan ; Lahmer, Tom ; Rasulzade, Shahla
The seismic vulnerability assessment of existing reinforced concrete (RC) buildings is a significant source of disaster mitigation plans and rescue services. Different countries evolved various Rapid Visual Screening (RVS) techniques and methodologies to deal with the devastating consequences of earthquakes on the structural characteristics of buildings and human casualties. Artificial intelligence (AI) methods, such as machine learning (ML) algorithm-based methods, are increasingly used in various scientific and technical applications. The investigation toward using these techniques in civil engineering applications has shown encouraging results and reduced human intervention, including uncertainties and biased judgment. In this study, several known non-parametric algorithms are investigated toward RVS using a dataset employing different earthquakes. Moreover, the methodology encourages the possibility of examining the buildings’ vulnerability based on the factors related to the buildings’ importance and exposure. In addition, a web-based application built on Django is introduced. The interface is designed with the idea to ease the seismic vulnerability investigation in real-time. The concept was validated using two case studies, and the achieved results showed the proposed approach’s potential efficiency
Stochastic multiscale modeling of polymeric nanocomposites using Data-driven techniques (2022)
Liu, Bokai
In recent years, lightweight materials, such as polymer composite materials (PNCs) have been studied and developed due to their excellent physical and chemical properties. Structures composed of these composite materials are widely used in aerospace engineering structures, automotive components, and electrical devices. The excellent and outstanding mechanical, thermal, and electrical properties of Carbon nanotube (CNT) make it an ideal filler to strengthen polymer materials’ comparable properties. The heat transfer of composite materials has very promising engineering applications in many fields, especially in electronic devices and energy storage equipment. It is essential in high-energy density systems since electronic components need heat dissipation functionality. Or in other words, in electronic devices the generated heat should ideally be dissipated by light and small heat sinks. Polymeric composites consist of fillers embedded in a polymer matrix, the first ones will significantly affect the overall (macroscopic) performance of the material. There are many common carbon-based fillers such as single-walled carbon nanotubes (SWCNT), multi-walled carbon nanotubes (MWCNT), carbon nanobuds (CNB), fullerene, and graphene. Additives inside the matrix have become a popular subject for researchers. Some extraordinary characters, such as high-performance load, lightweight design, excellent chemical resistance, easy processing, and heat transfer, make the design of polymeric nanotube composites (PNCs) flexible. Due to the reinforcing effects with different fillers on composite materials, it has a higher degree of freedom and can be designed for the structure according to specific applications’ needs. As already stated, our research focus will be on SWCNT enhanced PNCs. Since experiments are timeconsuming, sometimes expensive and cannot shed light into phenomena taking place for instance at the interfaces/interphases of composites, they are often complemented through theoretical and computational analysis. While most studies are based on deterministic approaches, there is a comparatively lower number of stochastic methods accounting for uncertainties in the input parameters. In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions. However, uncertainties in the input parameters such as aspect ratio, volume fraction, thermal properties of fiber and matrix need to be taken into account for reliable predictions. In this research, a stochastic multiscale method is provided to study the influence of numerous uncertain input parameters on the thermal conductivity of the composite. Therefore, a hierarchical multi-scale method based on computational homogenization is presented in to predict the macroscopic thermal conductivity based on the fine-scale structure. In order to study the inner mechanism, we use the finite element method and employ surrogate models to conduct a Global Sensitivity Analysis (GSA). The SA is performed in order to quantify the influence of the conductivity of the fiber, matrix, Kapitza resistance, volume fraction and aspect ratio on the macroscopic conductivity. Therefore, we compute first-order and total-effect sensitivity indices with different surrogate models. As stochastic multiscale models are computational expensive, surrogate approaches are commonly exploited. With the emergence of high performance computing and artificial intelligence, machine learning has become a popular modeling tool for numerous applications. Machine learning (ML) is commonly used in regression and maps data through specific rules with algorithms to build input and output models. They are particularly useful for nonlinear input-output relationships when sufficient data is available. ML has also been used in the design of new materials and multiscale analysis. For instance, Artificial neural networks and integrated learning seem to be ideally for such a task. They can theoretically simulate any non-linear relationship through the connection of neurons. Mapping relationships are employed to carry out data-driven simulations of inputs and outputs in stochastic modeling. This research aims to develop a stochastic multi-scale computational models of PNCs in heat transfer. Multi-scale stochastic modeling with uncertainty analysis and machine learning methods consist of the following components: -Uncertainty Analysis. A surrogate based global sensitivity analysis is coupled with a hierarchical multi-scale method employing computational homogenization. The effect of the conductivity of the fibers and the matrix, the Kapitza resistance, volume fraction and aspect ratio on the ’macroscopic’ conductivity of the composite is systematically studied. All selected surrogate models yield consistently the conclusions that the most influential input parameters are the aspect ratio followed by the volume fraction. The Kapitza Resistance has no significant effect on the thermal conductivity of the PNCs. The most accurate surrogate model in terms of the R2 value is the moving least square (MLS). -Hybrid Machine Learning Algorithms. A combination of artificial neural network (ANN) and particle swarm optimization (PSO) is applied to estimate the relationship between variable input and output parameters. The ANN is used for modeling the composite while PSO improves the prediction performance through an optimized global minimum search. The thermal conductivity of the fibers and the matrix, the kapitza resistance, volume fraction and aspect ratio are selected as input parameters. The output is the macroscopic (homogenized) thermal conductivity of the composite. The results show that the PSO significantly improves the predictive ability of this hybrid intelligent algorithm, which outperforms traditional neural networks. -Stochastic Integrated Machine Learning. A stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in PNCs is developed. Seven types of machine learning models are exploited in this research, namely Multivariate Adaptive Regression Splines (MARS), Support Vector Machine (SVM), Regression Tree (RT), Bagging Tree (Bag), Random Forest (RF), Gradient Boosting Machine (GBM) and Cubist. They are used as components of stochastic modeling to construct the relationship between the variable of the inputs’ uncertainty and the macroscopic thermal conductivity of PNCs. Particle Swarm Optimization (PSO) is used for hyper-parameter tuning to find the global optimal values leading to a significant reduction in the computational cost. The advantages and disadvantages of various methods are also analyzed in terms of computing time and model complexity to finally give a recommendation for the applicability of different models.
Hitze in der Stadt Jena (2022)
Hoinkis, Jule Hannah
Die vorliegende Arbeit befasst sich mit den spezifischen Faktoren und Wechselwirkungen des städtischen Klimas und Strategien zur Prävention und Kompensation lokaler Klimaveränderungen. Problematische Merkmale des Stadtklimas werden sich infolge des Klimawandels stärker ausprägen. Insbesondere die Hitzebelastung wird zunehmen und die Lebensbedingungen in der Stadt negativ beeinflussen. Infolge höherer Temperaturen in Städten und einer höheren Temperaturdifferenz zum Umland verändern sich Windströme und die Wasserbilanz. Es sind Strategien notwendig, um den Schadstoffausstoß, die Flächeninanspruchnahme, die Abfallproduktion und den Wasser-, Energie- und Ressourcenverbrauch zu verringern, um sowohl langfristig den Klimawandel als auch dessen bereits unvermeidbaren Auswirkungen auf Städte zu begrenzen. Beispielhaft untersucht die Arbeit das Stadtklima, dessen zukünftige Veränderungen infolge des Klimawandels, bauliche Maßnahmen und Anpassungsstrategien der Stadt Jena. Jena ist die zweitgrößte Stadt im Bundesland Thüringen und gehört heute zu den wärmsten und trockensten Großstädten Deutschlands. Die Ergebnisse der Arbeit werden anschließend anhand eines städtebaulichen Konzepts und Entwurfs angewendet. Das Bachstraßenareal liegt in der Innenstadt, dem am stärksten von Hitze betroffenen Stadtteil. Als ehemaliger Hauptstandort des Jenaer Universitätsklinikums, soll es zu einem nachhaltigen Wissenschaftscampus der Lebenswissenschaften umgebaut werden, wobei ein Großteil der denkmalgeschützten, ehemaligen Klinikgebäude erhalten bleibt. Der Fokus liegt dabei auf der Umsetzung der zuvor formulierten, nachhaltigen Strategien zur Verbesserung des lokalen Stadtklimas und einer Abschwächung der Auswirkungen des Klimawandels auf den besonders stark betroffenen Innenstadtbereich Jenas.
Atlas der Datenkörper. Körperbilder in Kunst, Design und Wissenschaft im Zeitalter digitaler Medien (2022)
Breuer, Johannes ; Bart, Marlene ; Freier, Alex Leo ; Rünker, Maximilian ; Jakubek, Kristin ; Rubiano, Juan ; Groos, Cora ; Šálek, Martin ; Fritz, Henrieke ; Kokkinidou, Eirini ; Richter, Fabian ; Liu, Ani ; Held, Tobias ; Moses, Gabriel S ; Blasius, Clara Maria ; Spång, Fanny ; Bencicova, Evelyn ; Rückeis, Julia ; Thurow, Katharina ; Maas, Frederike ; Farfán, Vanessa ; Tikka, Emilia ; Lee, Sang ; Holzheu, Stefanie
Digitale Technologien und soziale Medien verändern die Selbst- und Körperwahrnehmung und verzerren, verstärken oder produzieren dabei spezifische Körperbilder. Die Beiträger*innen kartographieren diese Phänomene, fragen nach ihrer medialen Existenzweise sowie nach den Möglichkeiten ihrer Kritik. Dabei begegnen sie ihrer Neuartigkeit mit einer transdisziplinären Herangehensweise. Aus sowohl der Perspektive künstlerischer und gestalterischer Forschung als auch der Kunst-, Kultur- und Medienwissenschaft sowie der Psychologie und Neurowissenschaft wird die Landschaft rezenter Körperbilder und Techniken einer digitalen Körperlichkeit untersucht.
A framework for artificial coral reef design: Integrating computational modelling and high precision monitoring strategies for artificial coral reefs – an Ecosystem-aware design approach in times of climate change (2022)
Vogler, Verena
Tropical coral reefs, one of the world’s oldest ecosystems which support some of the highest levels of biodiversity on the planet, are currently facing an unprecedented ecological crisis during this massive human-activity-induced period of extinction. Hence, tropical reefs symbolically stand for the destructive effects of human activities on nature [4], [5]. Artificial reefs are excellent examples of how architectural design can be combined with ecosystem regeneration [6], [7], [8]. However, to work at the interface between the artificial and the complex and temporal nature of natural systems presents a challenge, i.a. in respect to the B-rep modelling legacy of computational modelling. The presented doctorate investigates strategies on how to apply digital practice to realise what is an essential bulwark to retain reefs in impossibly challenging times. Beyond the main question of integrating computational modelling and high precision monitoring strategies in artificial coral reef design, this doctorate explores techniques, methods, and linking frameworks to support future research and practice in ecology led design contexts. Considering the many existing approaches for artificial coral reefs design, one finds they often fall short in precisely understanding the relationships between architectural and ecological aspects (e.g. how a surface design and material composition can foster coral larvae settlement, or structural three-dimensionality enhance biodiversity) and lack an integrated underwater (UW) monitoring process. Such a process is necessary in order to gather knowledge about the ecosystem and make it available for design, and to learn whether artificial structures contribute to reef regeneration or rather harm the coral reef ecosystem. For the research, empirical experimental methods were applied: Algorithmic coral reef design, high precision UW monitoring, computational modelling and simulation, and validated through parallel real-world physical experimentation – two Artificial Reef Prototypes (ARPs) in Gili Trawangan, Indonesia (2012–today). Multiple discrete methods and sub techniques were developed in seventeen computational experiments and applied in a way in which many are cross valid and integrated in an overall framework that is offered as a significant contribution to the field. Other main contributions include the Ecosystem-aware design approach, Key Performance Indicators (KPIs) for coral reef design, algorithmic design and fabrication of Biorock cathodes, new high precision UW monitoring strategies, long-term real-world constructed experiments, new digital analysis methods and two new front-end web-based tools for reef design and monitoring reefs. The methodological framework is a finding of the research that has many technical components that were tested and combined in this way for the very first time. In summary, the thesis responds to the urgency and relevance in preserving marine species in tropical reefs during this massive extinction period by offering a differentiated approach towards artificial coral reefs – demonstrating the feasibility of digitally designing such ‘living architecture’ according to multiple context and performance parameters. It also provides an in-depth critical discussion of computational design and architecture in the context of ecosystem regeneration and Planetary Thinking. In that respect, the thesis functions as both theoretical and practical background for computational design, ecology and marine conservation – not only to foster the design of artificial coral reefs technically but also to provide essential criteria and techniques for conceiving them. Keywords: Artificial coral reefs, computational modelling, high precision underwater monitoring, ecology in design.
A process-oriented decision model for determining the permitability of construction projects (2021)
Fauth, Judith
In recent years, the discussion of digitalization has arrived in the media, at conferences, and in committees of the construction and real estate industry. While some areas are producing innovations and some contributors can be described as pioneers, other topics still show deficits with regard to digital transformation. The building permit process can also be counted in this category. Regardless of how architects and engineers in planning offices rely on innovative methods, building documents have so far remained in paper form in too many cases, or are printed out after electronic submission to the authority. Existing resources – for example in the form of a building information model, which could provide support in the building permit process – are not being taken advantage of. In order to use digital tools to support decision-making by the building permit authorities, it is necessary to understand the current situation and to question conditions before pursuing the overall automation of internal authority processes as the sole solution. With a substantive-organizational consideration of the relevant areas that influence building permit determination, an improvement of the building permit procedure within authorities is proposed. Complex areas – such as legal situations, the use of technology, as well as the subjective alternative action – are determined and structured. With the development of a model for the determination of building permitability, both an understanding of influencing factors is conveyed and an increase in transparency for all parties involved is created. In addition to an international literature review, an empirical study served as the research method. The empirical study was conducted in the form of qualitative expert interviews in order to determine the current state in the field of building permit procedures. The collected data material was processed and subsequently subjected to a software-supported content analysis. The results were processed, in combination with findings from the literature review, in various analyses to form the basis for a proposed model. The result of the study is a decision model that closes the gap between the current processes within the building authorities and an overall automation of the building permit review process. The model offers support to examiners and applicants in determining building permit eligibility, through its process-oriented structuring of decision-relevant facts. The theoretical model could be transferred into practice in the form of a web application.
Generalized Beam Theory for the analysis of thin-walled circular pipe members (2022)
Habtemariam, Abinet Kifle
The detailed structural analysis of thin-walled circular pipe members often requires the use of a shell or solid-based finite element method. Although these methods provide a very good approximation of the deformations, they require a higher degree of discretization which causes high computational costs. On the other hand, the analysis of thin-walled circular pipe members based on classical beam theories is easy to implement and needs much less computation time, however, they are limited in their ability to approximate the deformations as they cannot consider the deformation of the cross-section. This dissertation focuses on the study of the Generalized Beam Theory (GBT) which is both accurate and efficient in analyzing thin-walled members. This theory is based on the separation of variables in which the displacement field is expressed as a combination of predetermined deformation modes related to the cross-section, and unknown amplitude functions defined on the beam's longitudinal axis. Although the GBT was initially developed for long straight members, through the consideration of complementary deformation modes, which amend the null transverse and shear membrane strain assumptions of the classical GBT, problems involving short members, pipe bends, and geometrical nonlinearity can also be analyzed using GBT. In this dissertation, the GBT formulation for the analysis of these problems is developed and the application and capabilities of the method are illustrated using several numerical examples. Furthermore, the displacement and stress field results of these examples are verified using an equivalent refined shell-based finite element model. The developed static and dynamic GBT formulations for curved thin-walled circular pipes are based on the linear kinematic description of the curved shell theory. In these formulations, the complex problem in pipe bends due to the strong coupling effect of the longitudinal bending, warping and the cross-sectional ovalization is handled precisely through the derivation of the coupling tensors between the considered GBT deformation modes. Similarly, the geometrically nonlinear GBT analysis is formulated for thin-walled circular pipes based on the nonlinear membrane kinematic equations. Here, the initial linear and quadratic stress and displacement tangent stiffness matrices are built using the third and fourth-order GBT deformation mode coupling tensors. Longitudinally, the formulation of the coupled GBT element stiffness and mass matrices are presented using a beam-based finite element formulation. Furthermore, the formulated GBT elements are tested for shear and membrane locking problems and the limitations of the formulations regarding the membrane locking problem are discussed.
Developments in Isogeometric Analysis and Application to High-Order Phase-Field Models of Biomembranes (2022)
Valizadeh, Navid
Isogeometric analysis (IGA) is a numerical method for solving partial differential equations (PDEs), which was introduced with the aim of integrating finite element analysis with computer-aided design systems. The main idea of the method is to use the same spline basis functions which describe the geometry in CAD systems for the approximation of solution fields in the finite element method (FEM). Originally, NURBS which is a standard technology employed in CAD systems was adopted as basis functions in IGA but there were several variants of IGA using other technologies such as T-splines, PHT splines, and subdivision surfaces as basis functions. In general, IGA offers two key advantages over classical FEM: (i) by describing the CAD geometry exactly using smooth, high-order spline functions, the mesh generation process is simplified and the interoperability between CAD and FEM is improved, (ii) IGA can be viewed as a high-order finite element method which offers basis functions with high inter-element continuity and therefore can provide a primal variational formulation of high-order PDEs in a straightforward fashion. The main goal of this thesis is to further advance isogeometric analysis by exploiting these major advantages, namely precise geometric modeling and the use of smooth high-order splines as basis functions, and develop robust computational methods for problems with complex geometry and/or complex multi-physics. As the first contribution of this thesis, we leverage the precise geometric modeling of isogeometric analysis and propose a new method for its coupling with meshfree discretizations. We exploit the strengths of both methods by using IGA to provide a smooth, geometrically-exact surface discretization of the problem domain boundary, while the Reproducing Kernel Particle Method (RKPM) discretization is used to provide the volumetric discretization of the domain interior. The coupling strategy is based upon the higher-order consistency or reproducing conditions that are directly imposed in the physical domain. The resulting coupled method enjoys several favorable features: (i) it preserves the geometric exactness of IGA, (ii) it circumvents the need for global volumetric parameterization of the problem domain, (iii) it achieves arbitrary-order approximation accuracy while preserving higher-order smoothness of the discretization. Several numerical examples are solved to show the optimal convergence properties of the coupled IGA–RKPM formulation, and to demonstrate its effectiveness in constructing volumetric discretizations for complex-geometry objects. As for the next contribution, we exploit the use of smooth, high-order spline basis functions in IGA to solve high-order surface PDEs governing the morphological evolution of vesicles. These governing equations are often consisted of geometric PDEs, high-order PDEs on stationary or evolving surfaces, or a combination of them. We propose an isogeometric formulation for solving these PDEs. In the context of geometric PDEs, we consider phase-field approximations of mean curvature flow and Willmore flow problems and numerically study the convergence behavior of isogeometric analysis for these problems. As a model problem for high-order PDEs on stationary surfaces, we consider the Cahn–Hilliard equation on a sphere, where the surface is modeled using a phase-field approach. As for the high-order PDEs on evolving surfaces, a phase-field model of a deforming multi-component vesicle, which consists of two fourth-order nonlinear PDEs, is solved using the isogeometric analysis in a primal variational framework. Through several numerical examples in 2D, 3D and axisymmetric 3D settings, we show the robustness of IGA for solving the considered phase-field models. Finally, we present a monolithic, implicit formulation based on isogeometric analysis and generalized-alpha time integration for simulating hydrodynamics of vesicles according to a phase-field model. Compared to earlier works, the number of equations of the phase-field model which need to be solved is reduced by leveraging high continuity of NURBS functions, and the algorithm is extended to 3D settings. We use residual-based variational multi-scale method (RBVMS) for solving Navier–Stokes equations, while the rest of PDEs in the phase-field model are treated using a standard Galerkin-based IGA. We introduce the resistive immersed surface (RIS) method into the formulation which can be employed for an implicit description of complex geometries using a diffuse-interface approach. The implementation highlights the robustness of the RBVMS method for Navier–Stokes equations of incompressible flows with non-trivial localized forcing terms including bending and tension forces of the vesicle. The potential of the phase-field model and isogeometric analysis for accurate simulation of a variety of fluid-vesicle interaction problems in 2D and 3D is demonstrated.
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