Algorithm selection in structural optimization

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 153-162).Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit optimization's application. Among them is the requirement for engineers-who generally lack the requisite expertise-to choose an optimization algorithm for a given problem. A suitable choice of algorithm improves the resulting design and reduces computational cost, yet the field of optimization does little to guide engineers in selecting from an overwhelming number of options. The goal of this dissertation is to aid, and ultimately to automate, algorithm selection, thus enhancing optimization's applicability in real-world design. The initial chapters examine the extent of the problem by reviewing relevant literature and by performing a short, empirical study of algorithm performance variation. We then specify hundreds of bridge design problems by methodically varying problem characteristics, and solve each of them with eight commonly-used nonlinear optimization algorithms. The resulting, extensive data set is used to address the algorithm selection problem. The results are first interpreted from an engineering perspective to ensure their validity as solutions to realistic problems. Algorithm performance trends are then analyzed, showing that no single algorithm outperforms the others on every problem. Those that achieve the best solutions are often computationally expensive, and those that converge quickly often arrive at poor solutions. Some problem features, such as the numbers of design variables and constraints, the structural type, and the nature of the objective function, correlate with algorithm performance. This knowledge and the generated data set are then used to develop techniques for automatic selection of optimization algorithms, based on a range supervised learning methods. Compared to a set of current, manual selection strategies, these techniques select the best algorithm almost twice as often, lead to better-quality solutions and reduced computational cost, and-on a randomly-chosen set of mass minimization problems-reduce average material use by 9.4%. The dissertation concludes by outlining future research on algorithm selection, on integrating these techniques in design software, and on adapting structural optimization to the realities of design. Keywords: Algorithm selection, structural optimization, structural design, machine learningby Rory Clune.Ph.D

Explorative structural design

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 80-83).The thesis proposes a new way of thinking about structural design software. The current state of computational structural design in practice is assessed, and a review of relevant literature and existing software identifies both the strengths of existing approaches and areas in which contributions can be made. A new approach is proposed which combines the strengths of architectural modeling software with structural analysis software, and an original object-oriented framework for the development of next-generation structural design tools is presented. The thesis shows that the field of structural optimization, long maligned by engineers for its impracticalities for engineering practice, can be made relevant and beneficial in providing techniques to explore the design space in an optimally-directed way, leading to the discovery of unexpected and novel structural designs which are easier to build, use less material, and cost less than structures designed by conventional software. The software framework is extended to include these optimization components and to facilitate the future inclusion of new algorithms by users. A fully functional design environment is developed and presented as an implementation of the work of the thesis.by Rory P. Clune.S.M

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries