About this Research Topic
Structural optimization, or the use of numerical optimization techniques to design material-efficient or cost-effective structures, has great potential for the construction industry. The construction industry is responsible for a large share of the worldwide consumption of natural resources, and structural optimization can help to reduce this, so improving the sustainability of the sector. In addition, structural optimization has the potential to reduce not only the construction cost, but also the engineering cost, by automating the repetitive task of sizing structural members. Finally, structural optimization can lead to innovative design solutions for specific structural components or materials.
Usually, a distinction is made between three structural optimization strategies: (1) size optimization, where the aim is to find the optimal dimensions of the structural components, (2) shape optimization, where the shape of the structure is parameterized and these parameters are optimized, and (3) topology optimization, where the optimal spatial distribution of structural material or structural components is determined.
The size of the design space generally increases from size optimization over shape optimization to topology optimization. A larger design space implies more freedom for the optimizer. This usually leads to a better performing solution, but also a more complex geometry, which may be difficult to build. Design optimization techniques that account for buildability are emerging rapidly, but there is still a long way to go, as each individual production or construction technique has its own technological limitations, and it is usually far from trivial to express them in a tractable mathematical form.
Digital fabrication techniques such as additive manufacturing and CNC milling promise a high degree of design freedom, allowing for mass customization, or the production of custom-designed complex shapes at a relatively low cost. As a consequence, the combination of structural optimization (topology optimization in particular) and digital fabrication seems like a perfect match. Nevertheless, digital fabrication techniques are not completely free from technological limitations either. The integration of these limitations in the optimization process is currently a very active research area.
Real-world structural engineering problems are usually characterized by a number of uncertainties. The actual loads, material properties, geometry, etc., may deviate from the values assumed in the design phase. Such uncertainties can have a detrimental impact on performance. As a consequence, in structural optimization, it is important not only to optimize the performance of the blueprint design, but also to ensure that its sensitivity with respect to uncertainties remains limited. Robust and reliability-based design optimization techniques allow for the incorporation of uncertainties in the optimization process.
This Research Topic encompasses (but is not limited to) size, shape, and topology optimization in the context of structural engineering, applied to the design of load-bearing structures, structural components, or structural materials under static or dynamic loading, in the absence or presence of uncertainties. The focus can be on the forward material or structural model and its sensitivities, the optimization technique, buildability, robustness, reliability, etc.
Usually, a distinction is made between three structural optimization strategies: (1) size optimization, where the aim is to find the optimal dimensions of the structural components, (2) shape optimization, where the shape of the structure is parameterized and these parameters are optimized, and (3) topology optimization, where the optimal spatial distribution of structural material or structural components is determined.
The size of the design space generally increases from size optimization over shape optimization to topology optimization. A larger design space implies more freedom for the optimizer. This usually leads to a better performing solution, but also a more complex geometry, which may be difficult to build. Design optimization techniques that account for buildability are emerging rapidly, but there is still a long way to go, as each individual production or construction technique has its own technological limitations, and it is usually far from trivial to express them in a tractable mathematical form.
Digital fabrication techniques such as additive manufacturing and CNC milling promise a high degree of design freedom, allowing for mass customization, or the production of custom-designed complex shapes at a relatively low cost. As a consequence, the combination of structural optimization (topology optimization in particular) and digital fabrication seems like a perfect match. Nevertheless, digital fabrication techniques are not completely free from technological limitations either. The integration of these limitations in the optimization process is currently a very active research area.
Real-world structural engineering problems are usually characterized by a number of uncertainties. The actual loads, material properties, geometry, etc., may deviate from the values assumed in the design phase. Such uncertainties can have a detrimental impact on performance. As a consequence, in structural optimization, it is important not only to optimize the performance of the blueprint design, but also to ensure that its sensitivity with respect to uncertainties remains limited. Robust and reliability-based design optimization techniques allow for the incorporation of uncertainties in the optimization process.
This Research Topic encompasses (but is not limited to) size, shape, and topology optimization in the context of structural engineering, applied to the design of load-bearing structures, structural components, or structural materials under static or dynamic loading, in the absence or presence of uncertainties. The focus can be on the forward material or structural model and its sensitivities, the optimization technique, buildability, robustness, reliability, etc.
Keywords: Size Optimization, Shape Optimization, Topology Optimization, Buildability, Robust and Reliability-Based Design Optimization
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