{"id":38327,"date":"2026-06-29T16:12:13","date_gmt":"2026-06-29T10:27:13","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=38327"},"modified":"2026-06-29T16:12:16","modified_gmt":"2026-06-29T10:27:16","slug":"computer-model-could-enable-bridges-and-buildings-that-use-less-material","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/computer-model-could-enable-bridges-and-buildings-that-use-less-material\/","title":{"rendered":"Computer model could enable bridges and buildings that use less material\u00a0"},"content":{"rendered":"\n

MIT researchers developed an approach for generating more buildable structures, bridging the gap between optimized design and real-world construction.<\/strong><\/em><\/p>\n\n\n

Zach Winn<\/div>\n\n\n
\"\"<\/figure>\n\n\n\n

Cambridge, Mass.<\/strong> — In 2022, global production of construction materials accounted for\u00a0more than 7 percent<\/a>\u00a0of total carbon emissions. But how many of those materials were truly necessary to build houses, buildings, and bridges?<\/p>\n\n\n\n

A technique called topology optimization can design structures that reduce the amount of material used, in some cases by as much as 90 percent, which would represent a multi-gigaton reduction in building emissions. Unfortunately, topology optimization is mostly used by researchers for applications like 3D printing rather than by engineers designing at the scale of buildings and bridges.<\/p>\n\n\n\n

That\u2019s because topology optimization doesn\u2019t create structures that can easily be built on time and budget, which are the things builders really care about.<\/p>\n\n\n\n

Now MIT researchers have created a way to make topology optimization designs more buildable. Their framework, described in a new paper in Automation in Construction<\/em><\/a> today, allows users to apply constraints to algorithmically generated structures to limit their complexity. For instance, the approach allows users to limit how many components meet at each point of their design and how small they want their smallest parts. It also builds on previous work by designing structures with multiple materials and taking into account materials\u2019 properties to distribute load and specify part connections.<\/p>\n\n\n\n

\u201cThere\u2019s an interplay between the materials you\u2019re using, the constructability of designs, and the optimization of the structure,\u201d says senior author Josephine Carstensen, MIT\u2019s Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering. \u201cYou need to be able to address all three at the same time. That\u2019s what we tried to do here.\u201d<\/p>\n\n\n\n

The researchers used their approach to design steel, wood, and multimaterial truss structures that support loads in buildings and bridges, showing the carbon emissions associated with materials changed significantly when different constraints were applied. They hope their framework will bring topology optimization closer to real-world use in construction.<\/p>\n\n\n\n

\u201cIn the literature, there\u2019s sometimes been a disconnect between the carbon savings you can achieve on a computer and the realistic carbon savings you can achieve for built structures \u2014 especially when it comes to design technologies like topology optimization,\u201d Carstensen says. \u201cThe problem lies in the lack of constructability of designs. These designs have been perceived as too difficult to make with conventional methods, so they are never even attempted. That\u2019s what is exciting about our approach: We can add constraints so that you will never be in a situation where the design that comes out is too hard to make.\u201d<\/p>\n\n\n\n

Joining Carstensen on the paper is first author and civil and environmental engineering PhD student Zane Schemmer.<\/p>\n\n\n\n

More buildable designs<\/strong><\/p>\n\n\n\n

Computer-based topology optimization has been around for decades. It uses computer programs to optimally distribute material in a given space, for instance creating the strongest possible structures at the lowest weight. The resulting designs are often complex, spider web-like structures that would be a challenge for even the most capable engineers to build.<\/p>\n\n\n\n

\u201cA big question Josephine and I were asking is why isn\u2019t industry using it?\u201d Schemmer recalls. \u201cWhat are the obstacles that prevent industry from designing things more efficiently, and how can we fill the gaps between research and real life?\u201d<\/p>\n\n\n\n

In recent years, several researchers have developed ways to make topology optimization easier to use. For their study, Schemmer and Carstensen wanted to bring those approaches together and add new capabilities, like creating designs that use multiple materials, which has been another challenge in the field.<\/p>\n\n\n\n

\u201cA big aspect of sustainability going forward will be not only using less material, but also implementing materials efficiently based on considerations like where you are in the world, your access to materials, and each of their associated carbon costs,\u201d Schemmer says.<\/p>\n\n\n\n

To build their framework, they used a class of equations called mixed integer algorithms that help make binary decisions about things like materials and connections.<\/p>\n\n\n\n

\u201cYou can\u2019t have a part that\u2019s 72 percent timber and 28 percent steel,\u201d Schemmer says. \u201cInstead, it says, \u2018This truss or cable is going to be made out of this,\u2019 and then based on that decision, how do we make sure all of these connections meet their strength standards?\u201d<\/p>\n\n\n\n

The system\u2019s decisions also take into account material properties. For instance, steel struts can withstand compressive loads, but steel cables cannot. The model also has more realistic modeling of how parts connect than previous approaches.<\/p>\n\n\n\n

\u201cIn 3D printing, the way things come together is easy,\u201d Carstensen says. \u201cIn construction, that\u2019s not the case. If you\u2019re building with timber, there\u2019s a certain rule set, versus steel has a different rule set.\u201d<\/p>\n\n\n\n

Users can also decide how complex they want their design to be by specifying the maximum number of connections at each joint and the minimum angle between connected components. The model also creates minimum size limits for parts, further improving its constructability.<\/p>\n\n\n\n

\u201cIt\u2019s tough to give a contractor these complex, intricate designs because it\u2019s going to be super difficult to build,\u201d Schemmer says. \u201cA lot of times contractors won\u2019t pick up a project like that to begin with.\u201d<\/p>\n\n\n\n

The researchers compared structures designed with their approach to structures designed with conventional topology optimization, showing dramatic differences in final designs that transformed how the structures would be built. Using the Lockport \u201cUpside-Down Bridge\u201d near Buffalo, New York, as an example, they applied individual constraints, like a minimum angle on part connections or minimum part sizes, to the bridge\u2019s truss design to better understand how each constraint impacted final designs.<\/p>\n\n\n\n

Finally, they made truss designs that used wood only, steel only, and combined wood and steel, showing how different projects offered tradeoffs with respect to environmental impact and constructability.<\/p>\n\n\n\n

\u201cWe saw how the system knew that you could design a bridge of pure steel, but that might not be best from a carbon standpoint,\u201d Schemmer says. \u201cOr you could design a bridge out of purely timber, but that might not be the strongest. But these materials can work together, so you use timber for the carbon savings and steel where you need extra strength, and there\u2019s a balance you can find in these structures.\u201d<\/p>\n\n\n\n

From research to industry<\/strong><\/p>\n\n\n\n

The researchers say their approach is more computationally intensive than some others, but they were able to use a MacBook Pro to run the programs in their experiment, and they believe it\u2019s practical for most civil engineering firms.<\/p>\n\n\n\n

\u201cIt\u2019s computationally a little tougher to solve, but there\u2019s a lot of tools coming out nowadays that make these problems a lot more feasible,\u201d Schemmer says. \u201cThis approach has been avoided by industry in the past, but now we think it\u2019s a practical way to solve problems dealing with variable constraints.\u201d<\/p>\n\n\n\n

If users have more computational resources, the researchers say their approach could work with a long list of materials and far bigger structures than homes, small buildings, and bridges.<\/p>\n\n\n\n

Moving forward, Carstensen says the team plans to build scaled-down structures designed by the model to further validate its predictions. They also want to add constraints to their model to make it even more seamless for civil engineers to use when designing the world\u2019s infrastructure.<\/p>\n\n\n\n

\u201cAs a structural engineer by training, I was never taught how to design for low-carbon,\u201d Schemmer says. \u201cTo tackle a problem as big as climate change, addressing the built environment is a great place to start. One of the most tangible things we can do is work at the layer of construction, at the design stage, because that\u2019s a fundamental step that we can control. There are a lot of decisions we make early on that lead us to use extra material we don\u2019t need.\u201d<\/p>\n\n\n\n

The work was funded by the MIT Morningside Academy for Design.<\/p>\n","protected":false},"excerpt":{"rendered":"

Cambridge, Mass. — In 2022, global production of construction materials accounted for\u00a0more than 7 percent\u00a0of total carbon emissions. But how many of those materials were truly necessary to build houses, buildings, and bridges?<\/p>\n","protected":false},"author":2,"featured_media":38328,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[43],"tags":[],"class_list":["post-38327","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-computer-science"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0.webp",900,600,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-200x200.webp",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-675x450.webp",675,450,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-768x512.webp",750,500,true],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0.webp",750,500,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0.webp",900,600,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0.webp",900,600,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0.webp",900,600,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-870x570.webp",870,570,true],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-600x600.webp",600,600,true],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-600x600.webp",600,600,true],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-760x490.webp",760,490,true],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-550x360.webp",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-95x65.webp",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-640x600.webp",640,600,true],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-96x96.webp",96,96,true],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/06\/MIT-Efficient-Trusses-01-press_0-150x100.webp",150,100,true]},"author_info":{"info":["Zach Winn"]},"category_info":"Computer Science<\/a>","tag_info":"Computer Science","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/38327","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=38327"}],"version-history":[{"count":1,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/38327\/revisions"}],"predecessor-version":[{"id":38329,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/38327\/revisions\/38329"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/38328"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=38327"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=38327"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=38327"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}