Intertwingled: The Work and Influence of Ted Nelson

History of Computing; Computer Appl. in Arts and Humanities; Data Structures; User Interfaces and Human Computer Interactio

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

Architecture and the Built Environment:

This publication provides an overview of TU Delft’s most significant research achievements in the field of architecture and the built environment during the years 2010–2012. It is the first presentation of the joint research portfolio of the Faculty of Architecture and OTB Research Institute since their integration into the Faculty of Architecture and the Built Environment. As such the portfolio holds a strong promise for the future. In a time when the economy seems to be finally picking up and in which such societal issues as energy, climate and ageing are more prominent than ever before, there are plenty of fields for us to explore in the next three years

Laboratory for Oceans

A review is made of the activities of the Laboratory for Oceans. The staff and the research activities are nearly evenly divided between engineering and scientific endeavors. The Laboratory contributes engineering design skills to aircraft and ground based experiments in terrestrial and atmospheric sciences in cooperation with scientists from labs in Earth sciences

Research Evidence on the Use of Learning Analytics: Implications for Education Policy

The evidence shows that the use of learning analytics to improve and to innovate learning and teaching in Europe is still in its infancy. The high expectations have not yet been realised. Though early adopters are already taking a lead in research and development, the evidence on practice and successful implementation is still scarce. Furthermore, though the work across Europe on learning analytics is promising, it is currently fragmented. This underlines the need for a careful build-up of research and experimentation, with both practice and policies that have a unified European vision. Therefore, the study suggests that work is needed to make links between learning analytics, the beliefs and values that underpin this field, and European priority areas for education and training 2020. As a way of guiding the discussion about further development in this area, the Action List for Learning Analytics is proposed. The Action List for Learning Analytics focuses on seven areas of activity. It outlines a set of actions for educators, researchers, developers and policymakers in which learning analytics are used to drive work in Europe’s priority areas for education and training. Strategic work should take place to ensure that each area is covered, that there is no duplication of effort, that teams are working on all actions and that their work proceeds in parallel. Policy leadership and governance practices •Develop common visions of learning analytics that address strategic objectives and priorities •Develop a roadmap for learning analytics within Europe •Align learning analytics work with different sectors of education •Develop frameworks that enable the development of analytics •Assign responsibility for the development of learning analytics within Europe •Continuously work on reaching common understanding and developing new priorities Institutional leadership and governance practices •Create organisational structures to support the use of learning analytics and help educational leaders to implement these changes •Develop practices that are appropriate to different contexts •Develop and employ ethical standards, including data protection Collaboration and networking •Identify and build on work in related areas and other countries •Engage stakeholders throughout the process to create learning analytics that have useful features •Support collaboration with commercial organisations Teaching and learning practices •Develop learning analytics that makes good use of pedagogy •Align analytics with assessment practices Quality assessment and assurance practices •Develop a robust quality assurance process to ensure the validity and reliability of tools •Develop evaluation checklists for learning analytics tools Capacity building •Identify the skills required in different areas •Train and support researchers and developers to work in this field •Train and support educators to use analytics to support achievement Infrastructure •Develop technologies that enable development of analytics •Adapt and employ interoperability standard

A machine learning based material homogenization technique for masonry structures

Cutting-edge methods in the computational analysis of structures have been developed over the last decades. Such modern tools are helpful to assess the safety of existing buildings. Two main finite element (FE) modeling approaches have been developed in the field of masonry structures, i.e. micro and macro scale. While the micro modeling distinguishes between the masonry components in order to accurately represent the typical masonry damage mechanisms in the material constituents, macro modeling considers a single continuum material with smeared properties so that large scale masonry models can be analyzed. Both techniques have demonstrated their advantages in different structural applications. However, each approach comes along with some possible disadvantages. For example, the use of micro modeling is limited to small scale structures, since the computational effort becomes too expensive for large scale applications, while macro modeling cannot take into account precisely the complex interaction among masonry components (brick units and mortar joints). Multi scale techniques have been proposed to combine the accuracy of micro modeling and the computational efficiency of macro modeling. Such procedures consider linked FE analyses at both scales, and are based on the concept of a representative volume element (RVE). The analysis of a RVE takes into account the micro structural behavior of component materials, and scales it up to the macro level. In spite of being a very accurate tool for the analysis of masonry structures, multi scale techniques still exhibit high computational cost while connecting the FE analyses at the two scales. Machine learning (ML) tools have been utilized successfully to train specific models by feeding big source data from different fields, e.g. autonomous driving, face recognition, etc. This thesis proposes the use of ML to develop a novel homogenization strategy for the in-plane analysis of masonry structures, where a continuous nonlinear material law is calibrated by considering relevant data derived from micro scale analysis. The proposed method is based on a ML tool that links the macro and micro scales of the analysis, by training a macro model smeared damage constitutive law through benchmark data from numerical tests derived from RVE micro models. In this context, numerical nonlinear tests on masonry micro models executed in a virtual laboratory provide the benchmark data for feeding the ML training procedure. The adopted ML technique allows the accurate and efficient simulation of the anisotropic behavior of masonry material by means of a tensor mapping procedure. The final stage of this novel homogenization method is the definition of a calibrated continuum constitutive model for the structural application to the masonry macro scale. The developed technique is applied to the in-plane homogenization of a Flemish bond masonry wall. Evaluation examples based on the simulation of physical laboratory tests show the accuracy of the method when compared with sophisticated micro modeling of the entire structure. Finally, an application example of the novel homogenization technique is given for the pushover analysis of a masonry heritage structure.En las últimas décadas se han desarrollado diversos métodos avanzados para el análisis computacional de estructuras. Estas herramientas modernas son también útiles para evaluar la seguridad de los edificios existentes. En el campo de las estructuras de la obra de fábrica se han desarrollado principalmente dos técnicas de modelizacón por elementos finitos (FE): la modelización en escala micro y en escala macro. Mientras que en un micromodelo se distingue entre los componentes de la obra de fábrica para representar con precisión los mecanismos de daño característicos de la misma, en un macromodelo se asignan las propiedades a un único material continuo que permite analizar modelos de obra de fábrica a gran escala. Ambas técnicas han demostrado sus ventajas en diferentes aplicaciones estructurales. Sin embargo, cada enfoque viene acompañado de algunas posibles desventajas. Por ejemplo, la micromodelización se limita a estructuras de pequeña escala, puesto que el esfuerzo computacional que requieren aumenta rápidamente con el tamaño de los modelos, mientras que la macromodelización, por su parte, es un enfoque promediado que no puede por tanto tener en cuenta precisamente la interacción compleja entre los componentes de la fábrica (unidades de ladrillo y juntas de mortero). Hasta el momento, se han propuesto algunas técnicas multiescala para combinar la precisión de la micromodelización y la eficiencia computacional de la macromodelización. Estos procedimientos aplican el análisis de FE vinculado a ambas escalas y se basan en el concepto de elemento de volumen representativo (RVE). El análisis de un RVE tiene en cuenta el comportamiento microestructural de los materiales componentes y lo escala hasta el nivel macro. A pesar de ser una herramienta muy precisa para el análisis de obra de fábrica, las técnicas multiescala siguen presentando un elevado coste computacional que se produce al conectar los análisis de FE de dos escalas. Además, diversos autores han utilizado con éxito herramientas de aprendizaje automático (machine learning (ML)) para poner a punto modelos específicos alimentados con grandes fuentes de datos de diferentes campos, por ejemplo, la conducción autónoma, el reconocimiento de caras, etc. Partiendo de los anteriores conceptos, este tesis propone el uso de ML para desarrollar una novedosa estrategia de homogeneización para el análisis en plano de estructuras de mampostería, donde se calibra una ley de materiales continua no lineal considerando datos relevantes derivados del análisis a microescala. El método propuesto se basa en una herramienta de ML que vincula las escalas macro y micro del análisis mediante la puesta a punto de una ley constitutiva para el modelo macro a través de datos producidos en ensayos numéricos de un RVE micro modelo. En este contexto, los ensayos numéricos no lineales sobre micro modelos de mampostería ejecutados en un laboratorio virtual proporcionan los datos de referencia para alimentar el procedimiento de entrenamiento del ML. La técnica de ML adoptada permite la simulación precisa y eficiente del comportamiento anisotrópico del material de mampostería mediante un procedimiento de mapeo tensorial. La etapa final de este novedoso método de homogeneización es la definición de un modelo constitutivo continuo calibrado para la aplicación estructural a la macroescala de mampostería. La técnica desarrollada se aplica a la homogeneización en el plano de un muro de obra de fábrica construido con aparejo flamenco. Ejemplos de evaluación basados en la simulación de pruebas físicas de laboratorio muestran la precisión del método en comparación con una sofisticada micro modelización de toda la estructura. Por último, se ofrece un ejemplo de aplicación de la novedosa técnica de homogeneización para el análisis pushover de una estructura patrimonial de obra de fábrica.Postprint (published version