The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information into the ML models can improve classification accuracy. To test this, we created a graph data structure where each building element is represented as a node assigned with basic geometric information. The connections between the graph nodes (edges) represent the immediate neighbors of that node, capturing the contextual information expressed in the BIM model. We devised a process for extracting graphs from BIM files and used it to construct a graph dataset of over 42,000 building elements and used the data to train several types of ML models. We compared the classification results of models that rely only on geometry, to graph neural networks (GNNs) that leverage contextual information. This work demonstrates that graph-based models for building element classification generally outperform classic ML models. Furthermore, dividing the graphs that represent complete buildings into smaller subgraphs further improves classification accuracy. These results underscore the potential of leveraging contextual information via graphs for advancing ML capabilities in the BIM environment.

In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results confirm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes.

This paper presents a framework for an analysis method and computational tool, which evaluates the fabrication parameters of complex geometries. The suggested method predicts the feasibility, material use, and machining time required for fabricating the moulds for these geometries. It achieves this by interrogating geometric properties instead of the traditional machining simulations. Using the algorithms developed in this research, the method can provide real time evaluation of computer-controlled mould fabrication techniques such as cutting and assembling sheet materials, multi-axis milling of volumetric material, and robotic hot wire cutting. In the paper, we describe the mathematical basis of the suggested method. We demonstrate how the method provides real-time visual feedback for designers and allows them to adjust their design according to fabrication constraints in the early design stages. Using architectural case studies, we show how the analysis results provide precise cost estimates and help minimize fabrication resources in manual or automatic fabrication optimization processes.

This paper presents an assessment and comparison of the effects of static and kinetic external shading elements on the dynamic measurement of daylighting. For this purpose, we used a method and parametric tool developed previously for the design and analysis of external shading elements in buildings. The proposed approach was used to compare static and dynamic movement scenarios for achieving optimal internal adjusted useful daylight illuminances (AUDI). The current paper presents the results of a methodical analysis, which compared various types of louvers in static and dynamic operation scenarios for a typical office in a Mediterranean climate. The results show that dynamically adjusted louvers perform notably better than fixed or seasonally adjusted modes of operation. The results show that dynamic operation scenarios can increase the AUDI by up to 51%. The results also show that in some conditions the existing rules of thumb fail to predict the correct design approach to louver geometry and that the use of rules of thumb in architectural daylight design needs to be revaluated.

Rationalization is widely recognized as an important design strategy in contemporary architectural projects, especially in projects with complex geometries, built using digital fabrication processes. However, an up to date review of the rationalization strategies used in these projects, their place in the design sequence and their relation to digital fabrication processes has not been conducted. The purpose of this review is to identify the rationalization strategies used in architectural projects in the practice and the academia. This paper presents the results of a systematic review of over 500 papers describing rationalization and digital fabrication in contemporary architecture. Using the data gathered in the review, we show that the capabilities of the fabrication machinery used are the most frequently encountered rationalization constraint in realized architectural projects. Additionally, we describe a new taxonomy for rationalization strategies, which incorporates functional information with the temporal information described by traditional classifications. Using this taxonomy, we identify trends within the industry and the academia and point to the growing popularity of parametric co-rationalization approaches. We conclude by discussing promising rationalization approaches for future research.

The presented research develops methods for introducing fabrication constraints into architectural design, a process often referred to as design rationalization.In the first stage of the research, a computational method for evaluating the fabrication potential of geometries was developed.The method predicts the feasibility, material use and machining time of a geometry in relation to different fabrication techniques.It uses geometric properties to mathematically estimate these parameters without simulating the actual machining.The second stage of the research describes processes for adapting architectural designs to their fabrication technique.The evaluation method previously developed is used as a fitness criterion for a computational optimization algorithm aimed at adapting concrete façade elements to the fabrication constraints of their molds.A case study demonstrates how the optimization process succeeded in improving the feasibility of different geometries within a time-frame suitable to the architectural design process, and without significant changes to the initial design.

This paper presents a design methodology for concrete façade panels that takes into consideration constraints related to digital fabrication machinery.A computational method for the real-time evaluation of industrial mold-making techniques, such as milling and hot wire cutting, was developed.The method rapidly evaluates the feasibility, material use, and machining time of complex geometry molds for architectural façade elements.Calculation speed is achieved by mathematically approximating CAM-machining operations.As results are obtained in nearly real time, the method can be easily incorporated into the architectural design process during its initial stages, when changes to the design are more effective.In the paper, we describe the algorithms of the computational evaluation method.We also show how it can be used to introduce fabrication considerations into the design process by using it to rationalize several types of panels.Additionally, we demonstrate how the method can be used in complex, large-scale architectural projects to save machining time and materials by evaluating and altering the paneling subdivision.

The paper presents a new enumerative method and tool to quantify and compare the potential contribution of kinetic and static external shading elements in buildings, in terms of dynamic measurement of daylighting. The suggested method and tool may help designers to examine the impact of various louver design and operation alternatives in the early stages of the design process. It also allows comparing the potential contribution of different dynamic louver movement scenarios to internal illuminance. This can help designers to better evaluate and compare different dynamic systems, which can be more cost effective. The method and tool are tested on a case study of an office building in a Mediterranean climate, by calculating and comparing the values of useful daylight internal illuminances in static, seasonally adjusted and dynamic louver setup scenarios in comparison to those without louvers. The results of the case study illustrate the contribution of the suggested method and tool to designing for high useful daylight levels. The case study's illuminance evaluation shows an increase of - 1.82% to 7.99%, - 1.4% to 11.67% and 10.86% to 33.6% in level of the adapted useful daylight illuminances in static, seasonally adjusted and dynamic external shadings scenarios correspondingly in comparison to the no external shading scenario.

In recent years, sustainable design methods have become a major concern within the building industry. There is also a growing awareness of the impact urban morphologies have on the overall energy and fuel consumption of a city. This paper investigates digital form-finding methods for generating an urban tissue to suit climatic conditions. In this research, a cascading series of genetic algorithms at multiple scales is coupled with environmental evaluation methods as fitness criteria. The methods devised in this paper integrate evaluation tools written with an object-oriented scripting language together with the Galapagos genetic solver in the Rhino/Grasshopper/Python platform. It is shown that the developed methods can be used to create large-scale urban layouts with improved street-level climate conditions as well as aggregations of buildings that function together to improve environmental and architectural parameters. The methodology developed in this paper is tested on a site with an area of approximately 1 km(2) in Brooklyn, New York, chosen because its climate features a large yearly variation in temperature and wind regime. The existing surrounding urban fabric, along with the local climatic conditions, is taken as the initial input in order to develop algorithmic processes with sensitivity to the site context.

The influence of urban morphology on the energy consumption of a fabric has been recently established by research into the energy use of existing cities.This paper suggests a framework for generating environmentally adapted urban tissue by using genetic algorithms as form-finding processes.A series of multi-objective optimization algorithms are described.The geometric abstractions used as a basis for these algorithms are illustrated in detail, and the results and implications of these types of simulations are discussed.The methodology developed within this paper was tested on one km2 site in three cities of varying climates, and further expanded into a detailed case study within one city.