Frame design with a surrogate model
Size a six-storey steel frame for gravity, wind and an earthquake from a few dozen analyses: they train a Gaussian process, a genetic algorithm searches it, and the analysis checks the answer.
Three member sizes make the frame: the lower columns (12 HEB sections), the upper columns (12) and the beams (9 IPE). That is 12 × 12 × 9 = 1,296 designs, one square each below. The frame carries gravity, wind from either side and an earthquake; pick a load case under the 3D view. If one analysis took an hour, checking every design would take 54 days. How many do you expect to pass every check?
One-way slabs span 6 m onto the beams in y; the roof carries only; steel self-weight added. The earthquake uses the type 1 spectrum of EN 1998-1 with . Changing a load starts over with new FE runs.
One grid per upper-column size; in each, lower columns grow to the right and beams grow upward.
The current design
Your browser can’t show the 3D view; the numbers and the cross-section still work.
Wind on the 18 m face, 0.90 kN/m²: 417 kN in all.
Method
A Latin hypercube picks N frame designs; each is analysed with a linear 3D frame model: 12-degree-of-freedom beam elements, rigid floors, fixed bases, the EN 1990 combinations 6.10 with the recommended factors for wind on either face, sway imperfections (EN 1993-1-1, 5.3.2), Horne’s with sway amplification (5.2.2) and simplified member screens.
The earthquake follows the lateral force method of EN 1998-1 (4.3.3.2): the period from Rayleigh’s quotient with the floor masses of , the type 1 design spectrum with , the base shear spread over the floors in proportion to height and mass, an accidental torsion of 5 % of the plan size, and the combinations with or . Each direction’s effects are amplified by where (4.4.2.2). The earthquake check is the larger of the damage-limitation drift at the plan corners (4.4.3.2, ) and ; the members are screened under the seismic combinations too.
A Gaussian process per output (drift, utilization of three member groups, , the earthquake check; Matérn 5/2 with one length scale per variable) is fitted to these runs by maximizing the marginal likelihood. Its accuracy is measured by leave-one-out with the same hyperparameters.
The map shows what the surrogate has learned: for every design at one upper-column size, whether it predicts all limits met with its band inside them, unsure (the band crosses a limit) or not met, and the predicted limit where crosses it. Each FE run you add joins the training set and the surrogate is refitted (infill; Forrester, Sóbester and Keane 2008).
A genetic algorithm then minimizes steel mass on the surrogate, keeping standard deviations away from every limit (designs already analysed count with their FE result), and the frame model checks the answer; a failed check joins the training set and the search runs again (surrogate-assisted evolutionary design, Jin 2011). Reveal analyses all 1,296 catalog designs as the honest baseline and grades the surrogate against it.
This is the workflow I use in my research: surrogates trained on expensive analyses, searched by an optimizer and checked by the full model.
Assumptions and limits
A teaching model, not a design tool: wind on either face with a simplified net pressure coefficient of 1.3 and no wind torsion, rigid floors, fixed bases, no connection design, no checks of the floor beams’ lateral stability (the slab is assumed to hold them), S355 with .
The column check with a storey-height buckling length is a simplified screen, not the EN 1993-1-1 interaction formula. Beams are checked for bending and shear separately. comes from Horne’s storey method in both directions; amplification applies to the sway effects only.
The map is a slice at one upper-column size; FE runs at other sizes are drawn faint, and the GA moves in all three variables. The run budget and the hour per analysis are a thought experiment: here one analysis takes milliseconds.
The drift limits , and are illustrative screening limits. The bands are model-based intervals of the surrogate, not probabilities of meeting a code; is not a 98 % reliability. The exhaustive optimum is exact only within this catalog and this simplified model.
The earthquake design is ductility class low () with the member screens above and no capacity design. EN 1998-1 recommends that class only for low seismicity (6.1.2); at a real frame would normally be designed for ductility class medium with capacity design, so read this as a screening check. The lateral force method needs and a building regular in elevation; the page says when a design’s period is beyond that. above 0.2 counts as failing, as it would need a second-order analysis. for every storey in ; importance class II; ductile non-structural elements.
Loads are stated assumptions you can change. Hand check before publication: the default design against a commercial frame program.
Cite
@software{guven_surrogate_frame,
author = {Güven, Murat},
title = {Frame design with a surrogate model},
year = {2026},
url = {https://likemaestro.github.io/tools/surrogate-frame}
}References
- CEN (2002). EN 1990: Eurocode – Basis of structural design. European Committee for Standardization.
- CEN (2004). EN 1998-1: Eurocode 8: Design of structures for earthquake resistance – Part 1: General rules, seismic actions and rules for buildings. European Committee for Standardization.
- CEN (2005a). EN 1991-1-4: Eurocode 1: Actions on structures – Part 1-4: General actions – Wind actions. European Committee for Standardization.
- CEN (2005b). EN 1993-1-1: Eurocode 3: Design of steel structures – Part 1-1: General rules and rules for buildings. European Committee for Standardization.
- Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2008). Engineering Design via Surrogate Modelling: A Practical Guide. Wiley. doi:10.1002/9780470770801
- Jin, Y. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61–70. doi:10.1016/j.swevo.2011.05.001
- McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239–245. doi:10.1080/00401706.1979.10489755
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. doi:10.7551/mitpress/3206.001.0001