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We present a physics-informed deep learning model for the transient heat transfer analysis of three-dimensional functionally graded materials (FGMs) employing a Runge–Kutta discrete time scheme. Firstly, the governing equation, associated boundary conditions and the initial condition for transient heat transfer analysis of FGMs with exponential material variations are presented. Then, the deep collocation method with the Runge–Kutta integration scheme for transient analysis is introduced. The prior physics that helps to generalize the physics-informed deep learning model is introduced by constraining the temperature variable with discrete time schemes and initial/boundary conditions. Further the fitted activation functions suitable for dynamic analysis are presented. Finally, we validate our approach through several numerical examples on FGMs with irregular shapes and a variety of boundary conditions. From numerical experiments, the predicted results with PIDL demonstrate well agreement with analytical solutions and other numerical methods in predicting of both temperature and flux distributions and can be adaptive to transient analysis of FGMs with different shapes, which can be the promising surrogate model in transient dynamic analysis.
The release of the large language model-based chatbot ChatGPT 3.5 in November 2022 has brought considerable attention to the subject of artificial intelligence, not only to the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this exploratory study, the first author was asked to write his bachelor’s thesis exploiting ChatGPT. For tracing the impact of ChatGPT methodically, an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs, and second, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along with the recommendations for students regarding the use of ChatGPT suggested by a common framework. In conclusion, ChatGPT is beneficial for thesis writing during various activities, such as brainstorming, structuring, and text revision. However, there are limitations that arise, e.g., in referencing. Thus, ChatGPT requires continuous validation of the outcomes generated and thus fosters learning. Currently, ChatGPT is valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner’s meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected, and the research question needs to be reevaluated from time to time.
Die Planungsforschung hat sich spätestens seit der „kommunikativen Wende“ intensiv damit beschäftigt, wie mit Konflikten umgegangen werden soll und wird. Ansätze der „agonistischen“ Planungstheorie widersprechen der normativen Prämisse, Konsensbildung unter den Planungsbeteiligten anzustreben. Vielmehr wollen sie widerstreitende Positionen normativ für die räumliche Entwicklung fruchtbar machen. Zugleich betonen sie eine vermeintliche Dualität von Planung und Protest, die in der neueren Protesttheorie infrage gestellt wird. Dieser Beitrag zeigt aufbauend auf einer Diskussion von planungs- und protesttheoretischen Ansätzen und einer empirischen Analyse planungsbezogener Proteste in Deutschland, dass diese Proteste von den Planungsakteuren zwar immer stärker als „Normalität“ aufgefasst werden und antagonistische Partizipation trotz zunehmender Konflikthaftigkeit und vermeintlicher Infragestellung der repräsentativen Demokratie kulturell regelgebunden bleibt. Protesthandeln ist Teil ausdifferenzierter „Partizipationsbündel“, die situationsbezogen auch Teilnahme an Beteiligungsverfahren, direktdemokratische Verfahren und Klagen umfassen. Protestierende verfolgen dabei meist eine eher reformorientierte Agenda, die keiner „Zähmung“ bedarf. Allerdings können die zugrunde liegenden Konflikte häufig gar nicht „gelöst“ werden. Planenden hingegen können auch innerhalb eines agonistischen Planungsumfelds rationalistische und deliberative Ansätze zur Verfügung stehen, die sie situationsbezogen und strategisch nutzen.
Experimental Validation of Dynamic Response of Small-Scale Metaconcrete Beams at Resonance Vibration
(2023)
Structures and their components experience substantially large vibration amplitudes at resonance, which can cause their failure. The scope of this study is the utilization of silicone-coated steel balls in concrete as damping aggregates to suppress the resonance vibration. The heavy steel cores oscillate with a frequency close to the resonance frequency of the structure. Due to the phase difference between the vibrations of the cores and the structure, the cores counteract the vibration of the structure. The core-coating inclusions are randomly distributed in concrete similar to standard aggregates. This mixture is referred to as metaconcrete. The main goal of this work is to validate the ability of the inclusions to suppress mechanical vibration through laboratory experiments. For this purpose, two small-scale metaconcrete beams were cast and tested. In a free vibration test, the metaconcrete beams exhibited a larger damping ratio compared to a similar beam cast from conventional concrete. The vibration amplitudes of the metaconcrete beams at resonance were measured with a frequency sweep test. In comparison with the conventional concrete beam, both metaconcrete beams demonstrated smaller vibration amplitudes. Both experiments verified an improvement in the dynamic response of the metaconcrete beams at resonance vibration.
The study presents a Machine Learning (ML)-based framework designed to forecast the stress-strain relationship of arc-direct energy deposited mild steel. Based on microstructural characteristics previously extracted using microscopy and X-ray diffraction, approximately 1000 new parameter sets are generated by applying the Latin Hypercube Sampling Method (LHSM). For each parameter set, a Representative Volume Element (RVE) is synthetically created via Voronoi Tessellation. Input raw data for ML-based algorithms comprises these parameter sets or RVE-images, while output raw data includes their corresponding stress-strain relationships calculated after a Finite Element (FE) procedure. Input data undergoes preprocessing involving standardization, feature selection, and image resizing. Similarly, the stress-strain curves, initially unsuitable for training traditional ML algorithms, are preprocessed using cubic splines and occasionally Principal Component Analysis (PCA). The later part of the study focuses on employing multiple ML algorithms, utilizing two main models. The first model predicts stress-strain curves based on microstructural parameters, while the second model does so solely from RVE images. The most accurate prediction yields a Root Mean Squared Error of around 5 MPa, approximately 1% of the yield stress. This outcome suggests that ML models offer precise and efficient methods for characterizing dual-phase steels, establishing a framework for accurate results in material analysis.
The imperative to transform current energy provisions is widely acknowledged. However, scant attention has hitherto been directed toward rural municipalities and their innate resources, notably biogenic resources. In this paper, a methodological framework is developed to interconnect resources from waste, wastewater, and agricultural domains for energy utilization. This entails cataloging existing resources, delineating their potential via quantitative assessments utilizing diverse technologies, and encapsulating them in a conceptual model. The formulated models underwent iterative evaluation with engagement from diverse stakeholders. Consequently, 3 main concepts, complemented by 72 sub-concepts, were delineated, all fostering positive contributions to climate protection and providing heat supply in the rural study area. The outcomes’ replicability is underscored by the study area’s generic structure and the employed methodology. Through these inquiries, a framework for the requisite energy transition, with a pronounced emphasis on the coupling of waste, wastewater, and agriculture sectors in rural environments, is robustly analyzed.
This paper outlines an important step in characterizing a novel field of robotic construction research where a cable-driven parallel robot is used to extrude cementitious material in three-dimensional space, and thus offering a comprehensive new approach to computational design and construction, and to robotic fabrication at larger scales. Developed by the Faculty of Art and Design at Bauhaus-University Weimar (Germany), the faculty of Architecture at the University of Applied Sciences Dortmund (Germany) and the Chair of Mechatronics at the University of Duisburg-Essen (Germany), this approach offers unique advantages over existing additive manufacturing methods: the system is easily transportable and scalable, it does not require additional formwork or scaffolding, and it offers digital integration and informational oversight across the entire design and building process. This paper considers 1) key research components of cable robotic 3D-printing (such as computational design, material exploration, and robotic control), and 2) the integration of these parameters into a unified design and building process. The demonstration of the approach at full-scale is of particular concern.
This article aims to develop a social theory of violence that emphasizes the role of the third party as well as the communication between the involved subjects. For this Teresa Koloma Beck’s essay ‘The Eye of the Beholder: Violence as a Social Process’ is taken as a starting point, which adopts a social-constructivist perspective. On the one hand, the basic concepts and the benefits of this approach are presented. On the other hand, social-theoretical problems of this approach are revealed. These deficits are counteracted by expanding Koloma Beck’s approach with a communicative-constructivist framework. Thus, the role of communicative action and the ‘objectification of violence’ is emphasized. These aspects impact the perception, judgement and (de-)legitimation of violence phenomena and the emergence of a ‘knowledge of violence’. Communicative actions and objectifications form a key to understanding violent interactions and the link between the micro and macro levels. Finally, the methodological consequences for the research of violence and Communicative Constructivism are discussed. Furthermore, possible research fields are outlined, which open up by looking at communicative action and the objectifications within the ‘triads of violence’.
Zu den diversen Unternehmungen sozialbewegter „Gegenwissenschaft“, die um 1980 auf der Bildfläche der BRD erschienen, zählte der 1982 gegründete Berliner Wissenschaftsladen e. V., kurz WILAB – eine Art „alternatives“ Spin-off der Technischen Universität Berlin. Der vorliegende Beitrag situiert die Ausgründung des „Ladens“ im Kontext zeitgenössischer Fortschritte der (regionalen) Forschungs- und Technologiepolitik. Gezeigt wird, wie der deindustrialisierenden Inselstadt, qua „innovationspolitischer“ Gegensteuerung, dabei sogar eine gewisse Vorreiterrolle zukam: über die Stadtgrenzen hinaus sichtbare Neuerungen wie die Gründermesse BIG TECH oder das 1983 eröffnete Berliner Innovations- und Gründerzentrum (BIG), der erste „Incubator“ [sic] der BRD, etwa gingen auf das Konto der 1977/78 lancierten Technologie-Transferstelle der TU Berlin, TU-transfer.
Anders gesagt: tendenziell bekam man es hier nun mit Verhältnissen zu tun, die immer weniger mit den Träumen einer „kritischen“, nicht-fremdbestimmten (Gegen‑)Wissenschaft kompatibel waren. Latent konträr zur historiographischen Prominenz des wissenschaftskritischen Zeitgeists fristeten „alternativen“ Zielsetzungen verpflichtete Unternehmungen wie „WILAB“ ein relativ marginalisiertes Nischendasein. Dennoch wirft das am WILAB verfolgte, so gesehen wenig aussichtsreiche Anliegen, eine andere, nämlich „humanere“ Informationstechnologie in die Wege zu leiten, ein instruktives Licht auf die Aufbrüche „unternehmerischer“ Wissenschaft in der BRD um 1980.
Object-Oriented Damage Information Modeling Concepts and Implementation for Bridge Inspection
(2022)
Bridges are designed to last for more than 50 years and consume up to 50% of their life-cycle costs during their operation phase. Several inspections and assessment actions are executed during this period. Bridge and damage information must be gathered, digitized, and exchanged between different stakeholders. Currently, the inspection and assessment practices rely on paper-based data collection and exchange, which is time-consuming and error-prone, and leads to loss of information. Storing and exchanging damage and building information in a digital format may lower costs and errors during inspection and assessment and support future needs, for example, immediate simulations regarding performance assessment, automated maintenance planning, and mixed reality inspections. This study focused on the concept for modeling damage information to support bridge reviews and structural analysis. Starting from the definition of multiple use cases and related requirements, the data model for damage information is defined independently from the subsequent implementation. In the next step, the implementation via an established standard is explained. Functional tests aim to identify problems in the concept and implementation. To show the capability of the final model, two example use cases are illustrated: the inspection review of the entire bridge and a finite-element analysis of a single component. Main results are the definition of necessary damage data, an object-oriented damage model, which supports multiple use cases, and the implementation of the model in a standard. Furthermore, the tests have shown that the standard is suitable to deliver damage information; however, several software programs lack proper implementation of the standard.
Quantification of cracks in concrete thin sections considering current methods of image analysis
(2022)
Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2. As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.
In this paper, we present an open-source code for the first-order and higher-order nonlocal operator method (NOM) including a detailed description of the implementation. The NOM is based on so-called support, dual-support, nonlocal operators, and an operate energy functional ensuring stability. The nonlocal operator is a generalization of the conventional differential operators. Combined with the method of weighed residuals and variational principles, NOM establishes the residual and tangent stiffness matrix of operate energy functional through some simple matrix without the need of shape functions as in other classical computational methods such as FEM. NOM only requires the definition of the energy drastically simplifying its implementation. The implementation in this paper is focused on linear elastic solids for sake of conciseness through the NOM can handle more complex nonlinear problems. The NOM can be very flexible and efficient to solve partial differential equations (PDEs), it’s also quite easy for readers to use the NOM and extend it to solve other complicated physical phenomena described by one or a set of PDEs. Finally, we present some classical benchmark problems including the classical cantilever beam and plate-with-a-hole problem, and we also make an extension of this method to solve complicated problems including phase-field fracture modeling and gradient elasticity material.
In this study, we propose a nonlocal operator method (NOM) for the dynamic analysis of (thin) Kirchhoff plates. The nonlocal Hessian operator is derived based on a second-order Taylor series expansion. The NOM does not require any shape functions and associated derivatives as ’classical’ approaches such as FEM, drastically facilitating the implementation. Furthermore, NOM is higher order continuous, which is exploited for thin plate analysis that requires C1 continuity. The nonlocal dynamic governing formulation and operator energy functional for Kirchhoff plates are derived from a variational principle. The Verlet-velocity algorithm is used for the time discretization. After confirming the accuracy of the nonlocal Hessian operator, several numerical examples are simulated by the nonlocal dynamic Kirchhoff plate formulation.
We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost.
In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems.
In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations.
Subscription-based news platforms (such as “Apple News+” or “Readly”) that bundle content from different publishers into one comprehensive package and offer it to media users at a fixed monthly rate are a new way of accessing and consuming digital journalism. These services have received little attention in journalism studies, although they differ greatly from traditional media products and distribution channels. This article empirically investigates the perception of journalism platforms based on eight qualitative focus group discussions with 55 German news consumers.
Results show that the central characteristics these platforms should fulfill in order to attract users are strikingly similar to the characteristics of media platforms from the music and video industries, in particular regarding price points, contract features, and modes of usage. Against this background, the potential and perspectives of a subscription-based news platform for journalism’s societal role are discussed.
Immanuel Kant’s thought is a central historical and theoretical reference in Hans Blumenberg’s metaphorological project. This is demonstrated by the fact that in the Paradigms the author outlines the concept of absolute metaphor by explicitly referring to §59 of the Critique of the Power of Judgment and recognizing in the Kantian symbol a model for his own metaphorics. However, Kant’s name also appears in the chapter on the metaphor of the “terra incognita” that not only did he theorize the presence of symbolic hypotyposis in our language [...] but also made extensive use of metaphors linked to “determinate historical experiences”. In particular: geographical metaphors. In my essay, I would like to start from the analysis of Kant’s geographical metaphors in order to try to rethink Blumenberg’s archaeological method as an archaeology of media that grounds the study of metaphors in the materiality of communication and the combination of tools, agents and media.
Real-world labs hold the potential to catalyse rapid urban transformations through real-world experimentation. Characterised by a rather radical, responsive, and location-specific nature, real-world labs face constraints in the scaling of experimental knowledge. To make a significant contribution to urban transformation, the produced knowledge must go beyond the level of a building, street, or small district where real-world experiments are conducted. Thus, a conflict arises between experimental boundaries and the stimulation of broader implications. The challenges of scaling experimental knowledge have been recognised as a problem, but remain largely unexplained. Based on this, the article will discuss the applicability of the “typology of amplification processes” by Lam et al. (2020) to explore and evaluate the potential of scaling experimental knowledge from real-world labs. The application of the typology is exemplified in the case of the Bauhaus.MobilityLab. The Bauhaus.MobilityLab takes a unique approach by testing and developing cross-sectoral mobility, energy, and logistics solutions with a distinct focus on scaling knowledge and innovation. For this case study, different qualitative research techniques are combined according to “within-method triangulation” and synthesised in a strengths, weaknesses, opportunities, and threats (SWOT) analysis. The analysis of the Bauhaus.MobilityLab proves that the “typology of amplification processes” is useful as a systematic approach to identifying and evaluating the potential of scaling experimental knowledge.