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Anti-Gewalttrainings, Gewaltwissen und die institutionelle Erzeugung gewaltbefreiter Subjekte
(2023)
Dieser Beitrag fragt danach, wie Wissen über Gewalt in Anti-Gewalttrainings produziert, vermittelt und sozial wirkmächtig wird. Auf Basis des kommunikativen Konstruktivismus werden diese Kurse als gewaltbezogene Institutionen begriffen, in denen eine Wissensordnung der Gewalt stabilisiert wird. Sie sollen Abweichungen von institutionalisierten Wirklichkeitsvorstellungen in Bezug auf Gewalt entgegenwirken. Dabei lassen sie sich als Selbsttechniken begreifen, durch welche die Kursteilnehmer*innen eine spezifische Subjektposition einüben, nämlich die des gewaltbefreiten Subjekts. Vor diesem Hintergrund wird anschließend zwischen konditionalen und konzessiven Anti-Gewalttrainings unterschieden. Erstere wenden sich an Personen, die tatsächlich von der institutionalisierten Wirklichkeitsvorstellung abgewichen sind und somit gesellschaftlich als Gewalttäter*innen eingestuft werden, während Letztere auf eine Klientel zielen, die potenziell von den gängigen Normen abweichen könnte, obwohl noch keine Gewalt aufgetreten sein muss. Abschließend wird gezeigt, dass den Kursleiter*innen eine wichtige Rolle im Subjektivierungsprozess und der Wissenskommunikation über Gewalt zukommt. Neben Wissen über Gewalt werden durch sie auch Wertbindungen, Legitimationen und Weltbilder vermittelt.
When predicting sound pressure levels induced by structure-borne sound sources and describing the sound propagation path through the building structure as exactly as possible, it is necessary to characterize the vibration behavior of the structure-borne sound sources. In this investigation, the characterization of structure-borne sound sources was performed using the two-stage method (TSM) described in EN 15657. Four different structure-borne sound sources were characterized and subsequently installed in a lightweight test stand. The resulting sound pressure levels in an adjacent receiving room were measured. In the second step, sound pressure levels were predicted according to EN 12354-5 based on the parameters of the structure-borne sound sources. Subsequently, the predicted and the measured sound pressure levels were compared to obtain reliable statements on the achievable accuracy when using source quantities determined by TSM with this prediction method.
As an optimization that starts from a randomly selected structure generally does not guarantee reasonable optimality, the use of a systemic approach, named the ground structure, is widely accepted in steel-made truss and frame structural design. However, in the case of reinforced concrete (RC) structural optimization, because of the orthogonal orientation of structural members, randomly chosen or architect-sketched framing is used. Such a one-time fixed layout trend, in addition to its lack of a systemic approach, does not necessarily guarantee optimality. In this study, an approach for generating a candidate ground structure to be used for cost or weight minimization of 3D RC building structures with included slabs is developed. A multiobjective function at the floor optimization stage and a single objective function at the frame optimization stage are considered. A particle swarm optimization (PSO) method is employed for selecting the optimal ground structure. This method enables generating a simple, yet potential, real-world representation of topologically preoptimized ground structure while both structural and main architectural requirements are considered. This is supported by a case study for different floor domain sizes.
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.