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    Design’s new frontier

    In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

    “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

    Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

    In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

    Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

    Computational design

    Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

    “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

    For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

    To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

    Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

    Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

    “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

    Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

    Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

    “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

    Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

    Personal fabrication

    Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

    “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

    Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

    First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

    To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

    “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

    Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

    Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

    “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

    Computational fluid dynamics and participatory design

    Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

    As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

    “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

    After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

    The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

    Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

    “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

    These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

    Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

    Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

    The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

    Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

    “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

    While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

    Designing intelligent systems

    When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

    Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

    “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

    For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

    To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

    “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

    Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

    “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

    To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

    In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

    “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

    Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

    Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

    “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

    Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

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    Saving seaweed with machine learning

    Last year, Charlene Xia ’17, SM ’20 found herself at a crossroads. She was finishing up her master’s degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program.

    “I had two thoughts: I’m either going to get a PhD to work on a project that protects our planet, or I’m going to start a restaurant,” recalls Xia.

    Xia poured over her extensive cookbook collection, researching international cuisines as she anxiously awaited word about her graduate school applications. She even looked into the cost of a food truck permit in the Boston area. Just as she started hatching plans to open a plant-based skewer restaurant, Xia received word that she had been accepted into the mechanical engineering graduate program at MIT.

    Shortly after starting her doctoral studies, Xia’s advisor, Professor David Wallace, approached her with an interesting opportunity. MathWorks, a software company known for developing the MATLAB computing platform, had announced a new seed funding program in MIT’s Department of Mechanical Engineering. The program encouraged collaborative research projects focused on the health of the planet.

    “I saw this as a super-fun opportunity to combine my passion for food, my technical expertise in ocean engineering, and my interest in sustainably helping our planet,” says Xia.

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    From MIT Mechanical Engineering: “Saving Seaweed with Machine Learning”

    Wallace knew Xia would be up to the task of taking an interdisciplinary approach to solve an issue related to the health of the planet. “Charlene is a remarkable student with extraordinary talent and deep thoughtfulness. She is pretty much fearless, embracing challenges in almost any domain with the well-founded belief that, with effort, she will become a master,” says Wallace.

    Alongside Wallace and Associate Professor Stefanie Mueller, Xia proposed a project to predict and prevent the spread of diseases in aquaculture. The team focused on seaweed farms in particular.

    Already popular in East Asian cuisines, seaweed holds tremendous potential as a sustainable food source for the world’s ever-growing population. In addition to its nutritive value, seaweed combats various environmental threats. It helps fight climate change by absorbing excess carbon dioxide in the atmosphere, and can also absorb fertilizer run-off, keeping coasts cleaner.

    As with so much of marine life, seaweed is threatened by the very thing it helps mitigate against: climate change. Climate stressors like warm temperatures or minimal sunlight encourage the growth of harmful bacteria such as ice-ice disease. Within days, entire seaweed farms are decimated by unchecked bacterial growth.

    To solve this problem, Xia turned to the microbiota present in these seaweed farms as a predictive indicator of any threat to the seaweed or livestock. “Our project is to develop a low-cost device that can detect and prevent diseases before they affect seaweed or livestock by monitoring the microbiome of the environment,” says Xia.

    The team pairs old technology with the latest in computing. Using a submersible digital holographic microscope, they take a 2D image. They then use a machine learning system known as a neural network to convert the 2D image into a representation of the microbiome present in the 3D environment.

    “Using a machine learning network, you can take a 2D image and reconstruct it almost in real time to get an idea of what the microbiome looks like in a 3D space,” says Xia.

    The software can be run in a small Raspberry Pi that could be attached to the holographic microscope. To figure out how to communicate these data back to the research team, Xia drew upon her master’s degree research.

    In that work, under the guidance of Professor Allan Adams and Professor Joseph Paradiso in the Media Lab, Xia focused on developing small underwater communication devices that can relay data about the ocean back to researchers. Rather than the usual $4,000, these devices were designed to cost less than $100, helping lower the cost barrier for those interested in uncovering the many mysteries of our oceans. The communication devices can be used to relay data about the ocean environment from the machine learning algorithms.

    By combining these low-cost communication devices along with microscopic images and machine learning, Xia hopes to design a low-cost, real-time monitoring system that can be scaled to cover entire seaweed farms.

    “It’s almost like having the ‘internet of things’ underwater,” adds Xia. “I’m developing this whole underwater camera system alongside the wireless communication I developed that can give me the data while I’m sitting on dry land.”

    Armed with these data about the microbiome, Xia and her team can detect whether or not a disease is about to strike and jeopardize seaweed or livestock before it is too late.

    While Xia still daydreams about opening a restaurant, she hopes the seaweed project will prompt people to rethink how they consider food production in general.

    “We should think about farming and food production in terms of the entire ecosystem,” she says. “My meta-goal for this project would be to get people to think about food production in a more holistic and natural way.” More

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    A robot that finds lost items

    A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.

    Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.

    The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.

    Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.

    While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.

    “This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab.

    Co-authors include research assistant Tara Boroushaki, the lead author; electrical engineering and computer science graduate student Isaac Perper; research associate Mergen Nachin; and Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering. The research will be presented at the Association for Computing Machinery Conference on Embedded Networked Senor Systems next month.

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    Sending signals

    RFusion begins searching for an object using its antenna, which bounces signals off the RFID tag (like sunlight being reflected off a mirror) to identify a spherical area in which the tag is located. It combines that sphere with the camera input, which narrows down the object’s location. For instance, the item can’t be located on an area of a table that is empty.

    But once the robot has a general idea of where the item is, it would need to swing its arm widely around the room taking additional measurements to come up with the exact location, which is slow and inefficient.

    The researchers used reinforcement learning to train a neural network that can optimize the robot’s trajectory to the object. In reinforcement learning, the algorithm is trained through trial and error with a reward system.

    “This is also how our brain learns. We get rewarded from our teachers, from our parents, from a computer game, etc. The same thing happens in reinforcement learning. We let the agent make mistakes or do something right and then we punish or reward the network. This is how the network learns something that is really hard for it to model,” Boroushaki explains.

    In the case of RFusion, the optimization algorithm was rewarded when it limited the number of moves it had to make to localize the item and the distance it had to travel to pick it up.

    Once the system identifies the exact right spot, the neural network uses combined RF and visual information to predict how the robotic arm should grasp the object, including the angle of the hand and the width of the gripper, and whether it must remove other items first. It also scans the item’s tag one last time to make sure it picked up the right object.

    Cutting through clutter

    The researchers tested RFusion in several different environments. They buried a keychain in a box full of clutter and hid a remote control under a pile of items on a couch.

    But if they fed all the camera data and RF measurements to the reinforcement learning algorithm, it would have overwhelmed the system. So, drawing on the method a GPS uses to consolidate data from satellites, they summarized the RF measurements and limited the visual data to the area right in front of the robot.

    Their approach worked well — RFusion had a 96 percent success rate when retrieving objects that were fully hidden under a pile.

    “Sometimes, if you only rely on RF measurements, there is going to be an outlier, and if you rely only on vision, there is sometimes going to be a mistake from the camera. But if you combine them, they are going to correct each other. That is what made the system so robust,” Boroushaki says.

    In the future, the researchers hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements. This would enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting.

    Beyond its potential industrial uses, a system like this could even be incorporated into future smart homes to assist people with any number of household tasks, Boroushaki says.

    “Every year, billions of RFID tags are used to identify objects in today’s complex supply chains, including clothing and lots of other consumer goods. The RFusion approach points the way to autonomous robots that can dig through a pile of mixed items and sort them out using the data stored in the RFID tags, much more efficiently than having to inspect each item individually, especially when the items look similar to a computer vision system,” says Matthew S. Reynolds, CoMotion Presidential Innovation Fellow and associate professor of electrical and computer engineering at the University of Washington, who was not involved in the research. “The RFusion approach is a great step forward for robotics operating in complex supply chains where identifying and ‘picking’ the right item quickly and accurately is the key to getting orders fulfilled on time and keeping demanding customers happy.”

    The research is sponsored by the National Science Foundation, a Sloan Research Fellowship, NTT DATA, Toppan, Toppan Forms, and the Abdul Latif Jameel Water and Food Systems Lab. More

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    Making roadway spending more sustainable

    The share of federal spending on infrastructure has reached an all-time low, falling from 30 percent in 1960 to just 12 percent in 2018.

    While the nation’s ailing infrastructure will require more funding to reach its full potential, recent MIT research finds that more sustainable and higher performing roads are still possible even with today’s limited budgets.

    The research, conducted by a team of current and former MIT Concrete Sustainability Hub (MIT CSHub) scientists and published in Transportation Research D, finds that a set of innovative planning strategies could improve pavement network environmental and performance outcomes even if budgets don’t increase.

    The paper presents a novel budget allocation tool and pairs it with three innovative strategies for managing pavement networks: a mix of paving materials, a mix of short- and long-term paving actions, and a long evaluation period for those actions.

    This novel approach offers numerous benefits. When applied to a 30-year case study of the Iowa U.S. Route network, the MIT CSHub model and management strategies cut emissions by 20 percent while sustaining current levels of road quality. Achieving this with a conventional planning approach would require the state to spend 32 percent more than it does today. The key to its success is the consideration of a fundamental — but fraught — aspect of pavement asset management: uncertainty.

    Predicting unpredictability

    The average road must last many years and support the traffic of thousands — if not millions — of vehicles. Over that time, a lot can change. Material prices may fluctuate, budgets may tighten, and traffic levels may intensify. Climate (and climate change), too, can hasten unexpected repairs.

    Managing these uncertainties effectively means looking long into the future and anticipating possible changes.

    “Capturing the impacts of uncertainty is essential for making effective paving decisions,” explains Fengdi Guo, the paper’s lead author and a departing CSHub research assistant.

    “Yet, measuring and relating these uncertainties to outcomes is also computationally intensive and expensive. Consequently, many DOTs [departments of transportation] are forced to simplify their analysis to plan maintenance — often resulting in suboptimal spending and outcomes.”

    To give DOTs accessible tools to factor uncertainties into their planning, CSHub researchers have developed a streamlined planning approach. It offers greater specificity and is paired with several new pavement management strategies.

    The planning approach, known as Probabilistic Treatment Path Dependence (PTPD), is based on machine learning and was devised by Guo.

    “Our PTPD model is composed of four steps,” he explains. “These steps are, in order, pavement damage prediction; treatment cost prediction; budget allocation; and pavement network condition evaluation.”

    The model begins by investigating every segment in an entire pavement network and predicting future possibilities for pavement deterioration, cost, and traffic.

    “We [then] run thousands of simulations for each segment in the network to determine the likely cost and performance outcomes for each initial and subsequent sequence, or ‘path,’ of treatment actions,” says Guo. “The treatment paths with the best cost and performance outcomes are selected for each segment, and then across the network.”

    The PTPD model not only seeks to minimize costs to agencies but also to users — in this case, drivers. These user costs can come primarily in the form of excess fuel consumption due to poor road quality.

    “One improvement in our analysis is the incorporation of electric vehicle uptake into our cost and environmental impact predictions,” Randolph Kirchain, a principal research scientist at MIT CSHub and MIT Materials Research Laboratory (MRL) and one of the paper’s co-authors. “Since the vehicle fleet will change over the next several decades due to electric vehicle adoption, we made sure to consider how these changes might impact our predictions of excess energy consumption.”

    After developing the PTPD model, Guo wanted to see how the efficacy of various pavement management strategies might differ. To do this, he developed a sophisticated deterioration prediction model.

    A novel aspect of this deterioration model is its treatment of multiple deterioration metrics simultaneously. Using a multi-output neural network, a tool of artificial intelligence, the model can predict several forms of pavement deterioration simultaneously, thereby, accounting for their correlations among one another.

    The MIT team selected two key metrics to compare the effectiveness of various treatment paths: pavement quality and greenhouse gas emissions. These metrics were then calculated for all pavement segments in the Iowa network.

    Improvement through variation

     The MIT model can help DOTs make better decisions, but that decision-making is ultimately constrained by the potential options considered.

    Guo and his colleagues, therefore, sought to expand current decision-making paradigms by exploring a broad set of network management strategies and evaluating them with their PTPD approach. Based on that evaluation, the team discovered that networks had the best outcomes when the management strategy includes using a mix of paving materials, a variety of long- and short-term paving repair actions (treatments), and longer time periods on which to base paving decisions.

    They then compared this proposed approach with a baseline management approach that reflects current, widespread practices: the use of solely asphalt materials, short-term treatments, and a five-year period for evaluating the outcomes of paving actions.

    With these two approaches established, the team used them to plan 30 years of maintenance across the Iowa U.S. Route network. They then measured the subsequent road quality and emissions.

    Their case study found that the MIT approach offered substantial benefits. Pavement-related greenhouse gas emissions would fall by around 20 percent across the network over the whole period. Pavement performance improved as well. To achieve the same level of road quality as the MIT approach, the baseline approach would need a 32 percent greater budget.

    “It’s worth noting,” says Guo, “that since conventional practices employ less effective allocation tools, the difference between them and the CSHub approach should be even larger in practice.”

    Much of the improvement derived from the precision of the CSHub planning model. But the three treatment strategies also play a key role.

    “We’ve found that a mix of asphalt and concrete paving materials allows DOTs to not only find materials best-suited to certain projects, but also mitigates the risk of material price volatility over time,” says Kirchain.

    It’s a similar story with a mix of paving actions. Employing a mix of short- and long-term fixes gives DOTs the flexibility to choose the right action for the right project.

    The final strategy, a long-term evaluation period, enables DOTs to see the entire scope of their choices. If the ramifications of a decision are predicted over only five years, many long-term implications won’t be considered. Expanding the window for planning, then, can introduce beneficial, long-term options.

    It’s not surprising that paving decisions are daunting to make; their impacts on the environment, driver safety, and budget levels are long-lasting. But rather than simplify this fraught process, the CSHub method aims to reflect its complexity. The result is an approach that provides DOTs with the tools to do more with less.

    This research was supported through the MIT Concrete Sustainability Hub by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More