Optimization-driven conceptual design of truss structures in a parametric modelling environment

Linwei He; Qingpeng Li; Matthew Gilbert; Paul Shepherd; Catherine Rankine; T. Pritchard; Reale Vincenzo

doi:10.1016/j.istruc.2021.12.048

Optimization-driven conceptual design of truss structures in a parametric modelling environment

Linwei He, Qingpeng Li, Matthew Gilbert, Paul Shepherd, Catherine Rankine, T. Pritchard, Reale Vincenzo

University of Sheffield

Research output: Contribution to journal › Article › peer-review

17 Citations (SciVal)

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Abstract

Structural optimization methods can be extremely powerful when used at the initial, conceptual, design stage of a building or bridge structure, potentially identifying materially efficient forms that are beyond the imagination of a human designer. This is particularly important at present, given the pressing need to reduce the carbon footprint associated with the built environment in the face of the current climate emergency. In this contribution, a computationally efficient global-local optimization framework is proposed, in which a linear programming-based truss layout optimization step is employed to generate initial (near-)optimal designs, with a non-linear optimization step then used to generate designs that take account of real-world complexity. To facilitate rapid exploration of design concepts, the proposed global-local optimization framework has been made available in the Peregrine plug-in for the popular Rhino-Grasshopper parametric modelling environment. The efficacy of the approach is demonstrated through its application to a range of case study problems.

Original language	English
Pages (from-to)	469-482
Number of pages	14
Journal	Structures
Volume	37
Early online date	18 Jan 2022
DOIs	https://doi.org/10.1016/j.istruc.2021.12.048
Publication status	Published - 31 Mar 2022

Bibliographical note

Funding Information:
The financial support provided by the Engineering and Physical Research Council (EPSRC) for projects EP/N023471/1 and EP/N023269/1 and a follow-up Knowledge Exchange project is gratefully acknowledged. The development of the Peregrine Rhino / Grasshopper plugin has also been supported by the INTEGRADDE project, funded from the European Union's Horizon 2020 research and innovation programme under grant agreement No 820776.

Funding Information:
The financial support provided by the Engineering and Physical Research Council (EPSRC) for projects EP/N023471/1 and EP/N023269/1 and a follow-up Knowledge Exchange project is gratefully acknowledged. The development of the Peregrine Rhino / Grasshopper plugin has also been supported by the INTEGRADDE project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820776.

Keywords

Layout optimization
Parametric design
Structural design
Structural optimization
Topology optimization

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Building and Construction
Civil and Structural Engineering
Architecture

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.istruc.2021.12.048

AuthorAccepted.PDFAccepted author manuscript, 13 MBLicence: CC BY-NC-ND

Cite this

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title = "Optimization-driven conceptual design of truss structures in a parametric modelling environment",

abstract = "Structural optimization methods can be extremely powerful when used at the initial, conceptual, design stage of a building or bridge structure, potentially identifying materially efficient forms that are beyond the imagination of a human designer. This is particularly important at present, given the pressing need to reduce the carbon footprint associated with the built environment in the face of the current climate emergency. In this contribution, a computationally efficient global-local optimization framework is proposed, in which a linear programming-based truss layout optimization step is employed to generate initial (near-)optimal designs, with a non-linear optimization step then used to generate designs that take account of real-world complexity. To facilitate rapid exploration of design concepts, the proposed global-local optimization framework has been made available in the Peregrine plug-in for the popular Rhino-Grasshopper parametric modelling environment. The efficacy of the approach is demonstrated through its application to a range of case study problems.",

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AU - Vincenzo, Reale

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N2 - Structural optimization methods can be extremely powerful when used at the initial, conceptual, design stage of a building or bridge structure, potentially identifying materially efficient forms that are beyond the imagination of a human designer. This is particularly important at present, given the pressing need to reduce the carbon footprint associated with the built environment in the face of the current climate emergency. In this contribution, a computationally efficient global-local optimization framework is proposed, in which a linear programming-based truss layout optimization step is employed to generate initial (near-)optimal designs, with a non-linear optimization step then used to generate designs that take account of real-world complexity. To facilitate rapid exploration of design concepts, the proposed global-local optimization framework has been made available in the Peregrine plug-in for the popular Rhino-Grasshopper parametric modelling environment. The efficacy of the approach is demonstrated through its application to a range of case study problems.

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Optimization-driven conceptual design of truss structures in a parametric modelling environment

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Computational Design Optimization of Large-Scale Building Structures

Cite this

Optimization-driven conceptual design of truss structures in a parametric modelling environment

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Projects

Computational Design Optimization of Large-Scale Building Structures

Cite this