Parametric Design for Timber Framing Span Tables

Structural sawn timber is widely utilised in timber-framed residential housing, however, a range of short- and long-term supply chain pressures are causing market demand to significantly outpace the available supply. This paper examines how existing timber framing span tables can be improved to reduce the over-specification of timber products in housing construction and thus improve supply utilisation. A novel parametric design framework is proposed that creates structured and indexable span data sets from the decoupled evaluation of structural member capacities and building design actions. The developed framework and digitised span data are then used to investigate how span capacity is influenced by revisions to building practice and to develop alternate span table formats that facilitate cross-grade comparison and minimum-grade specification.

3D Scanning & Point Cloud Data Processing

This research investigates two methods for processing point clouds of bridge girder reinforcement cages, aiming to enhance quality control in construction. It first uses an extended slicing method for segmenting and classifying 3D rebar within complex cages. Then, a semantic enrichment method infers detailed data on individual rebar shapes, refining accuracy. Tested on two on-site collected point clouds, the methods show an average positional error under 2 mm and 0.1°, and a 5% error in rebar length, reduced to 1% post-enrichment. This methodology, focusing on rebar identification, separation, and grouping, is adaptable to other reinforcement cage types.

Related Publications

Wang, Y., Bottazzi, V. S., & Gattas, J. M. (2023). Using augmented reality for interactive value engineering of structural steel connections. In Proceedings of IASS annual symposia (Vol. 2023, pp. 1–12). International Association for Shell; Spatial Structures (IASS).

Jiang, J., Ottenhaus, L.-M., & Gattas, J. M. (2023). A parametric design framework for timber framing span tables. Australian Journal of Civil Engineering, 1–16. https://doi.org/10.1080/14488353.2023.2227432

Hodge, G., & Gattas, J. M. (2022). Geometric and semantic point cloud data for quality control of bridge girder reinforcement cages. Automation in Construction, 140, 104334. https://doi.org/10.1016/j.autcon.2022.104334

Slinger, V., Lao, D., Nguyen, V., Singh, S., & Gattas, J. (2021). Assessing the viability of visual vibrometry for use in structural engineering. In EASEC16 (pp. 1353–1363). Springer.

Luo, D., Gattas, J. M., & Tan, P. S. S. (2020). Real-time defect recognition and optimized decision making for structural timber jointing. In DigitalFUTURES 2020 international conference.