Consequence-guided clarification improves incomplete design specifications acceptance
Consequence-guided clarification: releasing minimum-compliance topology-optimized designs from incomplete specifications
Computational Engineering, Finance, and Science
Summary
Designing structures with missing details about loads or supports can lead to wrong or unreliable results. The authors propose a method called consequence-guided clarification (CGC) that tests each possible complete version of the missing details by measuring how much the design's performance changes. If the design performs similarly across all plausible versions, it is accepted; otherwise, CGC asks targeted questions to clarify the most important missing details. This approach reduces the number of questions and computations needed compared to asking about everything or assuming defaults.
What this means in practice
- •For structural engineers: Improve structural design processes by identifying which incomplete design details most affect performance and reducing unnecessary specification queries.
- •For product design teams: Use consequence-guided clarification to verify structural designs under uncertain conditions and focus refinement on impactful design assumptions.
Authors
Shaoliang Yang, Jun Wang
Abstract
A converged topology optimization can solve the wrong problem when loads, supports, or the domain are underspecified. Assuming defaults, or asking the problem's author about every detail, ignores how much each answer changes the design. We propose consequence-guided clarification (CGC), which bases acceptance on computed mechanical consequences. CGC computes a reference design for each of a finite list of complete specifications, called readings. A design is accepted only if its compliance is at most 5% above the reference at every reading consistent with the answers. Otherwise, CGC asks the question whose worst answer leaves the smallest worst-case excess, and withholds acceptance at its question limit. To save optimizations, the screening variant CGC-S postpones some references until a candidate must be checked. We developed and evaluated both on 36 arXiv-derived tasks, treating each of 134 readings in turn as intended and simulating correct answers. In 26 tasks, the default-reading design failed another reading. Apart from always-asked questions on load direction, sign, and material budget, asking everything took 188 questions; CGC and CGC-S each took 105. The acceptance test supplied most of this saving: applying it to the default design alone took 117. Relative to asking everything, CGC-S requested 4.6 extra reference designs per avoided question, each from two optimization starts, counting optimization time only. References are not certified optima: better ones pushed three accepted cases above tolerance, and finer meshes changed some decisions. Within these limits, the test shows which readings a design covers and which details block acceptance.