Western cities are rapidly densifying, and new building typologies are beinginvented to mitigate high-rise and balance residential, commercial andrecreational functions. This vertical urbanization requires rethinking thetraditional design of public space to promote citizens’ well-being. While the scarce studies on high-rise environments indicate several risks, including social fragmentation and privatization of public functions (Henderson-Wilson 2008; Love et al., 2014), mental stress and undermining attention restoration (Mazumder et al., 2020; Lindal & Hartig 2013), evidence on the potential salutary and mitigating effects of architectural design qualities is limited (Suurenbroek & Spanjar 2023).The Building for Well-being research project combines biometric and socialdata-collection techniques to address this gap. It builds on studies investigatinghow built environments allow user engagement (Mallgrave 2013; Simpson2018) and afford important activities (Gibson 1966). This case study focuseson the experiences of predominant users of the NDSM Wharf in Amsterdamas it is transformed from a post-industrial site into a high-density, mixeduseneighborhood. Using eye-tracking, field and laboratory-based surveys, itexplores how residents, passers-by and visitors visually experience, appreciateand perceive the restorative value of the wharf’s recently developed urbanspaces.Thirty-six university students were randomly recruited as test subjects for thelaboratory test and assigned to one of the three user groups. The residentand passer-by groups were primed for familiarity. Each group was assigneda distinct walking mode and participants were told to imagine they werestrolling (residents), rushing (passers-by) or exploring (visitors). The exposuretime to visual stimuli of participants was five seconds per image. Afterwards,they reported on the perceived restorative quality of ten urban spaces,focusing on: (1) sense of being away, (2) level of complexity-compatibilityand (3) fascination, based on an adapted Restorative Components Scale (RCS,Yin et al. 2022; Laumann et al. 2001). Self-reported appreciation per scenewas measured on a 10-point Likert scale and subjects indicated elements inthe ten urban spaces they liked or disliked (see Figure 1). A semi-structuredon-site survey was also carried out to investigate user experiences furtherand for triangulation. Thirty-one users, consisting of residents, passers-byand visitors to the NDSM Wharf, rated their appreciation of the site and itsperceived restorative and design qualities (following Ewing & Clemente, 2013)on a 10-point Likert scale.The meta-data analysis of RCS statistics, appreciation values, eye-trackingmetrics and heatmaps reveals distinct visual patterns among user groups. Thispoints to the influence of environmental tasks and roles (see Figure 2). Strollingand exploring resulted in a comprehensive visual exploration of scenes with ahigher mean total fixation count and shorter mean total fixation duration thangoal-oriented walking. It suggests that walking mode determines the level ofopenness to the environment and that architectural attributes can also steervisual exploration. Scenes with the highest appreciation scores correlatedwith the RCS outcomes. They displayed coherence and opportunities forsocial engagement, contrasting with scenes with inconsistent industrial andcontemporary features. These findings provide spatial designers with insightsinto the subliminal experiences of predominant user groups to promote wellbeing in urban transformation.
Abstract-Architecture Compliance Checking (ACC) is useful to bridge the gap between architecture and implementation. ACC is an approach to verify conformance of implemented program code to high-level models of architectural design. Static ACC focuses on the modular software architecture and on the existence of rule violating dependencies between modules. Accurate tool support is essential for effective and efficient ACC. This paper presents a study on the accuracy of ACC tools regarding dependency analysis and violation reporting. Seven tools were tested and compared by means of a custom-made test application. In addition, the code of open source system Freemind was used to compare the tools on the number and precision of reported violation and dependency messages. On the average, 74 percent of 34 dependency types in our custom-made test software were reported, while 69 percent of 109 violating dependencies within a module of Freemind were reported. The test results show large differences between the tools, but all tools could improve the accuracy of the reported dependencies and violations.
Abstract-Architecture Compliance Checking (ACC) is an approach to verify the conformance of implemented program code to high-level models of architectural design. ACC is used to prevent architectural erosion during the development and evolution of a software system. Static ACC, based on static software analysis techniques, focuses on the modular architecture and especially on rules constraining the modular elements. A semantically rich modular architecture (SRMA) is expressive and may contain modules with different semantics, like layers and subsystems, constrained by rules of different types. To check the conformance to an SRMA, ACC-tools should support the module and rule types used by the architect. This paper presents requirements regarding SRMA support and an inventory of common module and rule types, on which basis eight commercial and non-commercial tools were tested. The test results show large differences between the tools, but all could improve their support of SRMA, what might contribute to the adoption of ACC in practice.
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
