%0 Conference Paper %1 Akbar_2022 %A Akbar, Zuardin %A Wood, Dylan %A Kiesewetter, Laura %A Menges, Achim %A Wortmann, Thomas %B Post Carbon - Proceedings of the 27th International Conference on Computer-Aided Architectural Design Research in Asia %D 2022 %E van Ameijde, Jeroen %E Gardner, Nicole %E Hyun, Kyung Hoon %E Dan, Luo %E Sheth, Urvi %I CAADRIA %K P266 ai from:zuardinakbar matpro myown peer %P 393-402 %R 10.52842/conf.caadria.2022.1.393 %T A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning %U https://doi.org/10.52842%2Fconf.caadria.2022.1.393 %V 1 %X This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R²>0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weather-responsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community. %@ 978-988-78917-7-2

@inproceedings{Akbar_2022, abstract = {This paper develops a workflow to train machine learning (ML) models with a small dataset from physical samples to predict the curvatures of self-shaping wood bilayers based on local variations in the grain. In contrast to state-of-the-art predictive models, specifically 1.) a 2D Timoshenko model and 2.) a 3D numerical model with a rheological model, our method accounts for natural and unavoidable material variations. In this paper, we only focus on local grain variations as the main driver for curvatures in small-scale material samples. We extracted a feature matrix from grain images of active and passive layers as a Grey Level Co-Occurrence Matrix and used it as the input for our ML models. We also analysed the impact of grain variations on the feature matrix. We trained and tested several tree-based regression models with different features. The models achieved very accurate predictions for curvatures in each sample (R²>0.9) and extend the range of parameters that is incalculable by a Timoshenko model. This research contributes to the material-efficient design of weather-responsive shape-changing wood structures by further leveraging the use of natural material features and explainable data-driven modelling and extends the topic in ML for material behaviour-driven design among the CAADRIA community.}, added-at = {2023-01-16T14:38:26.000+0100}, author = {Akbar, Zuardin and Wood, Dylan and Kiesewetter, Laura and Menges, Achim and Wortmann, Thomas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2aa2298e7d59ec644844f9e5ee6afc366/icd}, booktitle = {Post Carbon - Proceedings of the 27th International Conference on Computer-Aided Architectural Design Research in Asia}, doi = {10.52842/conf.caadria.2022.1.393}, editor = {van Ameijde, Jeroen and Gardner, Nicole and Hyun, Kyung Hoon and Dan, Luo and Sheth, Urvi}, eventdate = {9-15 April 2022}, interhash = {0a074ddf73e648573166e2d3e23500ac}, intrahash = {aa2298e7d59ec644844f9e5ee6afc366}, isbn = {978-988-78917-7-2}, issn = {2710-4257 (Print) / 2710-4265 (Online)}, keywords = {P266 ai from:zuardinakbar matpro myown peer}, language = {English}, organization = {The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong}, pages = {393-402}, preprinturl = {https://caadria2022.org/wp-content/uploads/2022/04/277-1.pdf}, publisher = {{CAADRIA}}, timestamp = {2023-02-05T22:58:05.000+0100}, title = {A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning}, url = {https://doi.org/10.52842%2Fconf.caadria.2022.1.393}, venue = {Sydney, Australia}, volume = 1, year = 2022 }