| id |
caadria2022_277 |
| authors |
Akbar, Zuardin, Wood, Dylan, Kiesewetter, Laura, Menges, Achim and Wortmann, Thomas |
| year |
2022 |
| title |
A Data-Driven Workflow for Modelling Self-Shaping Wood Bilayer, Utilizing Natural Material Variations with Machine Vision and Machine Learning |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.393
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 393-402 |
| summary |
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 (R;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. |
| keywords |
data-driven model, machine learning, material programming, smart material, timber structure, SDG 12 |
| series |
CAADRIA |
| email |
zuardin.akbar@icd.uni-stuttgart.de |
| full text |
 file.pdf (814,223 bytes) |
| references |
Content-type: text/plain
|
Fragkia, V., Foged, I. W. & Pasold, A. (2021)
 Predictive Information Modeling: Machine Learning Strategies for Material Uncertainty
, Technology|Architecture + Design, 5(2), 163–176. https://doi.org/10.1080/24751448.2021.1967057Grönquist, P., Wittel, F. K., & Rüggeberg, M. (2018). Modeling and design of thin bending wooden bilayers. PLOS ONE, 13(10), e0205607. https://doi.org/10.1371/journal.pone.0205607Grönquist, P., Wood, D., Hassani, M. M., Wittel, F. K., Menges, A., & Rüggeberg, M. (2019). Analysis of hygroscopic self-shaping wood at large scale for curved mass timber structures. Science Advances, 5(9), eaax1311. https://doi.org/10.1126/sciadv.aax1311Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610–621. https://doi.org/10.1109/TSMC.1973.4309314
|
|
|
|
|
Haryanto, T., Pratama, A., Suhartanto, H., Murni, A., Kusmardi, K. & Pidanic, J. (2020)
Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration
, Journal of Computer Science, 16(3), 280–294. https://doi.org/10.3844/jcssp.2020.280.294Hassani, M. M., Wittel, F. K., Hering, S., & Herrmann, H. J. (2015). Rheological model for wood. Computer Methods in Applied Mechanics and Engineering, 283, 1032–1060. https://doi.org/10.1016/j.cma.2014.10.031
|
|
|
|
|
He, X., Grassi, G. & Paoletti, I. (2021)
Geometric Deep Learning: Prediction of Shape-Shifting Textiles
, 12th Annual Symposium on Simulation for Architecture and Urban Design (SimAUD): Human+, SimAUD 2021. The Symposium on Simulation for Architecture and Urban Design (SimAUD)
|
|
|
|
|
Hering, S., Keunecke, D. & Niemz, P. (2012)
Moisture-dependent orthotropic elasticity of beech wood
, Wood Science and Technology, 46(5), 927–938. https://doi.org/10.1007/s00226-011-0449-4Lundberg, S., & Lee, S.-I. (2017, November 24). A Unified Approach to Interpreting Model Predictions. In The 31st Conference on Neural Information Processing Systems 2017. The Conference on Neural Information Processing Systems (NeurIPS)
|
|
|
|
|
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. & Lee, S.-I. (2019)
Explainable AI for Trees: From Local Explanations to Global Understanding
, ArXiv:1905.04610 [Cs, Stat]. Retrieved January 10, 2022, from http://arxiv.org/abs/1905.04610Menges, A., & Reichert, S. (2012). Material Capacity: Embedded Responsiveness. Architectural Design, 82(2), 52–59. https://doi.org/10.1002/ad.1379Olsson, A., & Oscarsson, J. (2017). Strength grading on the basis of high resolution laser scanning and dynamic excitation: A full scale investigation of performance. European Journal of Wood and Wood Products, 75(1), 17–31. https://doi.org/10.1007/s00107-016-1102-6Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830
|
|
|
|
|
Ramage, M. H., Burridge, H., Busse-Wicher, M., Fereday, G., Reynolds, T., Shah, D. U., Wu, G., Yu, L., Fleming, P., Densley-Tingley, D., Allwood, J., Dupree, P., Linden, P. F. & Scherman, O. (2017)
The wood from the trees: The use of timber in construction
, Renewable and Sustainable Energy Reviews, 68, 333–359. https://doi.org/10.1016/j.rser.2016.09.107Rossi, G., & Nicholas, P. (2018). Re/Learning the Wheel. Methods to Utilize Neural Networks as Design Tools for Doubly Curved Metal Surfaces. In ACADIA 2018: Recalibration. On Imprecision and Infidelity, (pp. 146–155). The Association for Computer Aided Design in Architecture (ACADIA)
|
|
|
|
|
Rowell, R. M. (2021)
Handbook of Wood Chemistry and Wood Composites (2nd ed.).
, CRC Press
|
|
|
|
|
Vasques, A. N. (2021)
Utilizing U-Net shaped networks as simulation tools in form-finding processes for fabric
, 12th Annual Symposium on Simulation for Architecture and Urban Design (SimAUD): Human+, SimAUD 2021. The Symposium on Simulation for Architecture and Urban Design (SimAUD)
|
|
|
|
|
Wimmer, G., Schraml, R., Hofbauer, H., Petutschnigg, A. & Uhl, A. (2021)
Two-Stage CNN-Based Wood Log Recognition
, O. Gervasi, B. Murgante, S. Misra, C. Garau, I. Bleèiæ, D. Taniar, B. O. Apduhan, A. M. A. C. Rocha, E. Tarantino, & C. M. Torre (Eds.), Computational Science and Its Applications – ICCSA 2021 (Vol. 12955, pp. 115–125). Springer International Publishing
|
|
|
|
|
Wood, D. M., Correa, D., Krieg, O. D. & Menges, A. (2016)
Material computation—4D timber construction: Towards building-scale hygroscopic actuated, self-constructing timber surfaces
, International Journal of Architectural Computing, 14(1), 49–62. https://doi.org/10.1177/1478077115625522Wood, D., Grönquist, P., Bechert, S., Aldinger, L., Riggenbach, D., Lehmann, K., Rüggeberg, M., Burgert, I., Knippers, J., Menges, A. (2020). From Machine Control to Material Programming: Self-Shaping Wood Manufacturing of A High Performance Curved Clt Structure – Urbach Tower. In Burry, J., Sabin, J., Sheil, B., & Skavara, M. (Eds.), Fabricate 2020 (pp. 50–57). UCL Press
|
|
|
|
|
Wood, D., Brütting, J. & Menges, A. (2018)
Self-Forming Curved Timber Plates: Initial Design Modeling for Shape-Changing Material Buildups
, The Annual Symposium of the International Association for Shell and Spatial Structures, IASS 2018. The International Association for Shell and Spatial Structures (IASS)
|
|
|
|
|
Wood, D., Vailati, C., Menges, A. & Rüggeberg, M. (2018)
Hygroscopically actuated wood elements for weather responsive and self-forming building parts – Facilitating upscaling and complex shape changes
, Construction and Building Materials, 165, 782–791. https://doi.org/10.1016/j.conbuildmat.2017.12.134Zwierzycki, M., Nicholas, P., & Ramsgaard Thomsen, M. (2018). Localised and Learnt Applications of Machine Learning for Robotic Incremental Sheet Forming. In K. De Rycke, C. Gengnagel, O. Baverel, J. Burry, C. Mueller, M. M. Nguyen, P. Rahm, & M. R. Thomsen (Eds.), Humanizing Digital Reality (pp. 373–382). Springer Singapore. https://doi.org/10.1007/978-981-10-6611-5_32
|
|
|
|
| last changed |
2022/07/22 07:34 |
|
