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![Lorenzo Greco, parametricism.co.uk | AKT II, lorenzogreco@gmail.com 7
Thickness > Bolt Strength > Phi > Rows
Plate thickness [5..5..40] [mm]
Rows count (subject to geometrical limits) [2..1..10]
Bolt phi [16..2..30] [mm]
Bolt strength [8.8, 9.8, 10.9]
N, Compression force [0..4..200] [kN]
Vy, Vertical shear [0..10..500] [kN]
Mx, Bending moment around the major axis [0..6..300] [kNm]
Database Generation](https://image.slidesharecdn.com/205machinelearningandoptimizationtechniquesforsteelconnections-180721192641/85/Machine-Learning-and-Optimization-Techniques-for-Steel-Connections-7-320.jpg)
![Lorenzo Greco, parametricism.co.uk | AKT II, lorenzogreco@gmail.com 8
# N [kN] Vy [kN] Mx [kNm] Rows Φ t [mm] UF
0 0 0 0 2 16 5 0.00
1 10 0 0 2 16 5 0.02
... ... ... ... ... ... ... ...
1834 200 500 300 4 22 10 0.89
Without considering multiple connections and varying angles, it was 60+ parameters.
Reduced to 6
Database Generation](https://image.slidesharecdn.com/205machinelearningandoptimizationtechniquesforsteelconnections-180721192641/85/Machine-Learning-and-Optimization-Techniques-for-Steel-Connections-8-320.jpg)
Uploaded byLorenzo Greco
Machine Learning and Optimization Techniques for Steel Connections
This document discusses using machine learning and optimization techniques to design steel connections. It generated a database of over 1,800 steel connection designs varying parameters like plate thickness, bolt size, and loading conditions. It then analyzed the data using regression and neural network models to predict connection performance. It validated the models on a real-world building project, showing the optimized connections improved utilization compared to the original design. Going forward, it aims to further automate steel connection design and incorporate insights from real projects into guidelines and codes.
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Machine Learning and Optimization Techniques for Steel Connections
- 1. Machine Learning andOptimization techniques for Steel Connections Lorenzo Greco, www.parametricism.co.uk, AKT II Paper number 205 lorenzogreco@gmail.com
- 2. Sberbank - ZahaHadid, 200000mq free form Lorenzo Greco, parametricism.co.uk | AKT II, lorenzogreco@gmail.com
- 3. Random boring factory- unknown Lorenzo Greco, parametricism.co.uk | AKT II, lorenzogreco@gmail.com
- 4. Most amazing city- many Lorenzo Greco, parametricism.co.uk | AKT II, lorenzogreco@gmail.com
- 5. Sample model -Revit Lorenzo Greco, parametricism.co.uk | AKT II, lorenzogreco@gmail.com
- 6. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 6 End plate joint
- 7. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 7 Thickness > Bolt Strength > Phi > Rows Plate thickness [5..5..40] [mm] Rows count (subject to geometrical limits) [2..1..10] Bolt phi [16..2..30] [mm] Bolt strength [8.8, 9.8, 10.9] N, Compression force [0..4..200] [kN] Vy, Vertical shear [0..10..500] [kN] Mx, Bending moment around the major axis [0..6..300] [kNm] Database Generation
- 8. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 8 # N [kN] Vy [kN] Mx [kNm] Rows Φ t [mm] UF 0 0 0 0 2 16 5 0.00 1 10 0 0 2 16 5 0.02 ... ... ... ... ... ... ... ... 1834 200 500 300 4 22 10 0.89 Without considering multiple connections and varying angles, it was 60+ parameters. Reduced to 6 Database Generation
- 9. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 9 Data Analysis
- 10. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 10 ● Ridge ● RidgeCV ● KNeighbors (Regressor) ● Linear Regression ● Elastic Net ● Partial Least Squares regression ● Multi-layer Perceptron Benchmark
- 11. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 11 White City Place
- 12. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 12 Optimized dataset Accuracy benchmark Robot's analysis Real case Real case with optimized joints
- 13. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 13 Breakdown of utilization factor. Blue: White City Place Red: ML + Optimization Comparison: ML vs ground truth
- 14. Lorenzo Greco, parametricism.co.uk| AKT II, lorenzogreco@gmail.com 14 ● Automize joint design and steel design embedding engineers’ decisions ● Gain insights from patterns emerging from real projects and incorporate them in code and guidelines ● Automate checks on projects by proof engineers and city council ● Retrofitting ● Simplify explorations for complex structures ● Expand to MEP layout, architectural finishing, detailing, etc... Conclusion and future progress
Editor's Notes
- #10 Data distribution and correlation matrix
- #12 White City Place, a 7 storeys building in London designed by AKT II with Allies and Morrison architects with Lend Lease and commissioned by Stanhope. The building is situated north of White City underground station and is part of the redevelopment of the BBC Media Village.
- #13 We want to maximize the area under the graph. This shows the accuracy against recall(UF ratio) (a) Accuracy as function of absolute error in predicting the correct utilization factor (UF), for a K-Neighbour Regressor trained on an optimized dataset (b) Accuracy as function of absolute error in predicting the correct utilization factor (UF), for a K-Neighbour Regressor trained on an optimized data set, derived from Robot's analysis (c) Accuracy as function of absolute error in predicting the correct utilization factor (UF). The algorithm is tested against a real case. (d) Accuracy as function of absolute error in predicting the correct utilization factor (UF). The algorithm is tested against a real case, but with the goal of hitting the optimized algorithm that would fit rather than the actual ones used.