Topology optimization of buildings-scale structures with material and fabrication constraints

The construction industry releases about 10% of anthropogenic Carbon Dioxide every year, primarily due to the manufacturing of construction materials. Structural optimization has been proposed as means of improving material efficiency in buildings, and thus reducing material demand for construction projects. Topology optimization has great potential for materially-efficient design because it is a free-form optimization method, allowing for performant geometries to be computationally derived with minimal input from the user. However, topology optimization algorithms must be modified to account for the specific fabrication and material constraints that are inherent in construction practices. This thesis shares a collection of research projects related to the use of topology optimization for large-scale structures relevant to the construction industry. First, a novel algorithm is proposed for large-scale 3D printed structures. The work focuses on the limitations presented by the printing nozzle, and the anisotropies that arise in 3D printed systems. Second, topology optimization is modified for design of structural glass. Several algorithms are developed, which are then used to design, fabricate, and test physical specimen to evaluate their real-world performance. Third, a framework is presented to design low-weight reinforced concrete structures. This system is used to design, build, and test reinforced concrete beams, so their performance can be compared to conventionally designed specimen. This thesis considers the diverse ways that topology optimization could be applied to design large-scale structures of various construction materials. The results demonstrate the types of computational techniques that can be used for generative design in the built environment.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Jewett, Jackson L.

Advisor dc:contributor.advisor

• Carstensen, Josephine V.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162452

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162452