Towards Assembly Information Modeling (AIM)

Nowadays digital tools support architects, engineers and constructors in many specific tasks in the construction industry. While these tools are covering almost all aspects of design and manufacturing, the planning and design for the assembly of buildings remain an unexplored area. This research aims to lay the foundations of a new framework for the design for assembly in architectural applications entitled Assembly Information Modeling. In practice, it is a central digital model containing the structure architectural design, construction details, three dimensional representations, assembly sequences, issue management and others. This framework forms the base for a multitude of novel applications for assembly design, planning and execution, such as assembly simulation and strategies communication, problem detections in the early design phases and interdisciplinary coordination. This paper describes the specifications of the digital assembly model and illustrate two use cases: collaborative assembly design using AEC cloud-based platforms and Augmented Assembly using Augmented reality devices

Statistically modelling the curing of cellulose-based 3d printed components: methods for material dataset composition, augmentation and encoding

Machine-Learning models thrive on data. The more data available, or creatable, the more defined is the problem representation, and the more accurate is the obtained prediction. This presents a challenge for physical, material datasets, specifically those related to fabrication systems, in which data is tied to physical artefacts which necessitate fabrication, digitisation and formatting to be used as input for predictive models. In this paper we present a design-based methodology to producing a material dataset for statistically modelling the curing of cellulose-based 3d-printed components, as well as associated methods for geometric data encoding, tolerance-informed data augmentation and statistical modelling. The focus of the paper is on the digital workflows and considerations for dataset composition - the material case of 3d-printing cellulose is secondary. We use a built 3d-printed demonstrator wall as a material dataset, through which we generate datapoints that stem from a real design-scenario and inform the fabrication model. Using a feature-engineering approach, select geometrical features are encoded numerically. We perform statistical analysis on the data, and test different shallow models and neural networks. We report on the successful training of a Polynomial Kernel Ridge Regressor to predict the vertical shrinkage of the pieces from wet print to dry element