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      in Environment

      3 Questions: What a single car can say about traffic

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      Vehicle traffic has long defied description. Once measured roughly through visual inspection and traffic cameras, new smartphone crowdsourcing tools are now quantifying traffic far more precisely. This popular method, however, also presents a problem: Accurate measurements require a lot of data and users.

      Meshkat Botshekan, an MIT PhD student in civil and environmental engineering and research assistant at the MIT Concrete Sustainability Hub, has sought to expand on crowdsourcing methods by looking into the physics of traffic. During his time as a doctoral candidate, he has helped develop Carbin, a smartphone-based roadway crowdsourcing tool created by MIT CSHub and the University of Massachusetts Dartmouth, and used its data to offer more insight into the physics of traffic — from the formation of traffic jams to the inference of traffic phase and driving behavior. Here, he explains how recent findings can allow smartphones to infer traffic properties from the measurements of a single vehicle.  

      Q: Numerous navigation apps already measure traffic. Why do we need alternatives?

      A: Traffic characteristics have always been tough to measure. In the past, visual inspection and cameras were used to produce traffic metrics. So, there’s no denying that today’s navigation tools apps offer a superior alternative. Yet even these modern tools have gaps.

      Chief among them is their dependence on spatially distributed user counts: Essentially, these apps tally up their users on road segments to estimate the density of traffic. While this approach may seem adequate, it is both vulnerable to manipulation, as demonstrated in some viral videos, and requires immense quantities of data for reliable estimates. Processing these data is so time- and resource-intensive that, despite their availability, they can’t be used to quantify traffic effectively across a whole road network. As a result, this immense quantity of traffic data isn’t actually optimal for traffic management.

      Q: How could new technologies improve how we measure traffic?

      A: New alternatives have the potential to offer two improvements over existing methods: First, they can extrapolate far more about traffic with far fewer data. Second, they can cost a fraction of the price while offering a far simpler method of data collection. Just like Waze and Google Maps, they rely on crowdsourcing data from users. Yet, they are grounded in the incorporation of high-level statistical physics into data analysis.

      For instance, the Carbin app, which we are developing in collaboration with UMass Dartmouth, applies principles of statistical physics to existing traffic models to entirely forgo the need for user counts. Instead, it can infer traffic density and driver behavior using the input of a smartphone mounted in single vehicle.

      The method at the heart of the app, which was published last fall in Physical Review E, treats vehicles like particles in a many-body system. Just as the behavior of a closed many-body system can be understood through observing the behavior of an individual particle relying on the ergodic theorem of statistical physics, we can characterize traffic through the fluctuations in speed and position of a single vehicle across a road. As a result, we can infer the behavior and density of traffic on a segment of a road.

      As far less data is required, this method is more rapid and makes data management more manageable. But most importantly, it also has the potential to make traffic data less expensive and accessible to those that need it.

      Q: Who are some of the parties that would benefit from new technologies?

      A: More accessible and sophisticated traffic data would benefit more than just drivers seeking smoother, faster routes. It would also enable state and city departments of transportation (DOTs) to make local and collective interventions that advance the critical transportation objectives of equity, safety, and sustainability.

      As a safety solution, new data collection technologies could pinpoint dangerous driving conditions on a much finer scale to inform improved traffic calming measures. And since socially vulnerable communities experience traffic violence disproportionately, these interventions would have the added benefit of addressing pressing equity concerns. 

      There would also be an environmental benefit. DOTs could mitigate vehicle emissions by identifying minute deviations in traffic flow. This would present them with more opportunities to mitigate the idling and congestion that generate excess fuel consumption.  

      As we’ve seen, these three challenges have become increasingly acute, especially in urban areas. Yet, the data needed to address them exists already — and is being gathered by smartphones and telematics devices all over the world. So, to ensure a safer, more sustainable road network, it will be crucial to incorporate these data collection methods into our decision-making. More

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      in Environment

      Q&A: More-sustainable concrete with machine learning

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      As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

      Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

      Q: What applications does concrete have, and what properties make it a preferred building material?

      Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

      Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

      Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

      While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

      Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

      Q: Why is this problem important to address now, at this point in history?

      Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

      Q: How are you planning to address this problem to produce better concrete?

      Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

      Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

      Q: What kinds of AI and computational techniques are you employing for this?

      Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

      Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

      We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

      Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

      Q: How do you balance cost-benefit trade-offs?

      Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

      Q: What kinds of challenges do you face in this work?

      Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

      Q: What do you hope to achieve through this work?

      Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

      Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

      Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

      This research is supported by the MIT-IBM Watson AI Lab. More

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      in Environment

      Making roadway spending more sustainable

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      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

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      in Energy

      Predicting building emissions across the US

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      The United States is entering a building boom. Between 2017 and 2050, it will build the equivalent of New York City 20 times over. Yet, to meet climate targets, the nation must also significantly reduce the greenhouse gas (GHG) emissions of its buildings, which comprise 27 percent of the nation’s total emissions.

      A team of current and former MIT Concrete Sustainability Hub (CSHub) researchers is addressing these conflicting demands with the aim of giving policymakers the tools and information to act. They have detailed the results of their collaboration in a recent paper in the journal Applied Energy that projects emissions for all buildings across the United States under two GHG reduction scenarios.

      Their paper found that “embodied” emissions — those from materials production and construction — would represent around a quarter of emissions between 2016 and 2050 despite extensive construction.

      Further, many regions would have varying priorities for GHG reductions; some, like the West, would benefit most from reductions to embodied emissions, while others, like parts of the Midwest, would see the greatest payoff from interventions to emissions from energy consumption. If these regional priorities were addressed aggressively, building sector emissions could be reduced by around 30 percent between 2016 and 2050.

      Quantifying contradictions

      Modern buildings are far more complex — and efficient — than their predecessors. Due to new technologies and more stringent building codes, they can offer lower energy consumption and operational emissions. And yet, more-efficient materials and improved construction standards can also generate greater embodied emissions.

      Concrete, in many ways, epitomizes this tradeoff. Though its durability can minimize energy-intensive repairs over a building’s operational life, the scale of its production means that it contributes to a large proportion of the embodied impacts in the building sector.

      As such, the team centered GHG reductions for concrete in its analysis.

      “We took a bottom-up approach, developing reference designs based on a set of residential and commercial building models,” explains Ehsan Vahidi, an assistant professor at the University of Nevada at Reno and a former CSHub postdoc. “These designs were differentiated by roof and slab insulation, HVAC efficiency, and construction materials — chiefly concrete and wood.”

      After measuring the operational and embodied GHG emissions for each reference design, the team scaled up their results to the county level and then national level based on building stock forecasts. This allowed them to estimate the emissions of the entire building sector between 2016 and 2050.

      To understand how various interventions could cut GHG emissions, researchers ran two different scenarios — a “projected” and an “ambitious” scenario — through their framework.

      The projected scenario corresponded to current trends. It assumed grid decarbonization would follow Energy Information Administration predictions; the widespread adoption of new energy codes; efficiency improvement of lighting and appliances; and, for concrete, the implementation of 50 percent low-carbon cements and binders in all new concrete construction and the adoption of full carbon capture, storage, and utilization (CCUS) of all cement and concrete emissions.

      “Our ambitious scenario was intended to reflect a future where more aggressive actions are taken to reduce GHG emissions and achieve the targets,” says Vahidi. “Therefore, the ambitious scenario took these same strategies [of the projected scenario] but featured more aggressive targets for their implementation.”

      For instance, it assumed a 33 percent reduction in grid emissions by 2050 and moved the projected deadlines for lighting and appliances and thermal insulation forward by five and 10 years, respectively. Concrete decarbonization occurred far more quickly as well.

      Reductions and variations

      The extensive growth forecast for the U.S. building sector will inevitably generate a sizable number of emissions. But how much can this figure be minimized?

      Without the implementation of any GHG reduction strategies, the team found that the building sector would emit 62 gigatons CO2 equivalent between 2016 and 2050. That’s comparable to the emissions generated from 156 trillion passenger vehicle miles traveled.

      But both GHG reduction scenarios could cut the emissions from this unmitigated, business-as-usual scenario significantly.

      Under the projected scenario, emissions would fall to 45 gigatons CO2 equivalent — a 27 percent decrease over the analysis period. The ambitious scenario would offer a further 6 percent reduction over the projected scenario, reaching 40 gigatons CO2 equivalent — like removing around 55 trillion passenger vehicle miles from the road over the period.

      “In both scenarios, the largest contributor to reductions was the greening of the energy grid,” notes Vahidi. “Other notable opportunities for reductions were from increasing the efficiency of lighting, HVAC, and appliances. Combined, these four attributes contributed to 85 percent of the emissions over the analysis period. Improvements to them offered the greatest potential emissions reductions.”

      The remaining attributes, such as thermal insulation and low-carbon concrete, had a smaller impact on emissions and, consequently, offered smaller reduction opportunities. That’s because these two attributes were only applied to new construction in the analysis, which was outnumbered by existing structures throughout the period.

      The disparities in impact between strategies aimed at new and existing structures underscore a broader finding: Despite extensive construction over the period, embodied emissions would comprise just 23 percent of cumulative emissions between 2016 and 2050, with the remainder coming primarily from operation.  

      “This is a consequence of existing structures far outnumbering new structures,” explains Jasmina Burek, a CSHub postdoc and an incoming assistant professor at the University of Massachusetts Lowell. “The operational emissions generated by all new and existing structures between 2016 and 2050 will always greatly exceed the embodied emissions of new structures at any given time, even as buildings become more efficient and the grid gets greener.”

      Yet the emissions reductions from both scenarios were not distributed evenly across the entire country. The team identified several regional variations that could have implications for how policymakers must act to reduce building sector emissions.

      “We found that western regions in the United States would see the greatest reduction opportunities from interventions to residential emissions, which would constitute 90 percent of the region’s total emissions over the analysis period,” says Vahidi.

      The predominance of residential emissions stems from the region’s ongoing population surge and its subsequent growth in housing stock. Proposed solutions would include CCUS and low-carbon binders for concrete production, and improvements to energy codes aimed at residential buildings.

      As with the West, ideal solutions for the Southeast would include CCUS, low-carbon binders, and improved energy codes.

      “In the case of Southeastern regions, interventions should equally target commercial and residential buildings, which we found were split more evenly among the building stock,” explains Burek. “Due to the stringent energy codes in both regions, interventions to operational emissions were less impactful than those to embodied emissions.”

      Much of the Midwest saw the inverse outcome. Its energy mix remains one of the most carbon-intensive in the nation and improvements to energy efficiency and the grid would have a large payoff — particularly in Missouri, Kansas, and Colorado.

      New England and California would see the smallest reductions. As their already-strict energy codes would limit further operational reductions, opportunities to reduce embodied emissions would be the most impactful.

      This tremendous regional variation uncovered by the MIT team is in many ways a reflection of the great demographic and geographic diversity of the nation as a whole. And there are still further variables to consider.

      In addition to GHG emissions, future research could consider other environmental impacts, like water consumption and air quality. Other mitigation strategies to consider include longer building lifespans, retrofitting, rooftop solar, and recycling and reuse.

      In this sense, their findings represent the lower bounds of what is possible in the building sector. And even if further improvements are ultimately possible, they’ve shown that regional variation will invariably inform those environmental impact reductions.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Environment

      Concrete’s role in reducing building and pavement emissions

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      Encountering concrete is a common, even routine, occurrence. And that’s exactly what makes concrete exceptional.

      As the most consumed material after water, concrete is indispensable to the many essential systems — from roads to buildings — in which it is used.

      But due to its extensive use, concrete production also contributes to around 1 percent of emissions in the United States and remains one of several carbon-intensive industries globally. Tackling climate change, then, will mean reducing the environmental impacts of concrete, even as its use continues to increase.

      In a new paper in the Proceedings of the National Academy of Sciences, a team of current and former researchers at the MIT Concrete Sustainability Hub (CSHub) outlines how this can be achieved.

      They present an extensive life-cycle assessment of the building and pavements sectors that estimates how greenhouse gas (GHG) reduction strategies — including those for concrete and cement — could minimize the cumulative emissions of each sector and how those reductions would compare to national GHG reduction targets. 

      The team found that, if reduction strategies were implemented, the emissions for pavements and buildings between 2016 and 2050 could fall by up to 65 percent and 57 percent, respectively, even if concrete use accelerated greatly over that period. These are close to U.S. reduction targets set as part of the Paris Climate Accords. The solutions considered would also enable concrete production for both sectors to attain carbon neutrality by 2050.

      Despite continued grid decarbonization and increases in fuel efficiency, they found that the vast majority of the GHG emissions from new buildings and pavements during this period would derive from operational energy consumption rather than so-called embodied emissions — emissions from materials production and construction.

      Sources and solutions

      The consumption of concrete, due to its versatility, durability, constructability, and role in economic development, has been projected to increase around the world.

      While it is essential to consider the embodied impacts of ongoing concrete production, it is equally essential to place these initial impacts in the context of the material’s life cycle.

      Due to concrete’s unique attributes, it can influence the long-term sustainability performance of the systems in which it is used. Concrete pavements, for instance, can reduce vehicle fuel consumption, while concrete structures can endure hazards without needing energy- and materials-intensive repairs.

      Concrete’s impacts, then, are as complex as the material itself — a carefully proportioned mixture of cement powder, water, sand, and aggregates. Untangling concrete’s contribution to the operational and embodied impacts of buildings and pavements is essential for planning GHG reductions in both sectors.

      Set of scenarios

      In their paper, CSHub researchers forecast the potential greenhouse gas emissions from the building and pavements sectors as numerous emissions reduction strategies were introduced between 2016 and 2050.

      Since both of these sectors are immense and rapidly evolving, modeling them required an intricate framework.

      “We don’t have details on every building and pavement in the United States,” explains Randolph Kirchain, a research scientist at the Materials Research Laboratory and co-director of CSHub.

      “As such, we began by developing reference designs, which are intended to be representative of current and future buildings and pavements. These were adapted to be appropriate for 14 different climate zones in the United States and then distributed across the U.S. based on data from the U.S. Census and the Federal Highway Administration”

      To reflect the complexity of these systems, their models had to have the highest resolutions possible.

      “In the pavements sector, we collected the current stock of the U.S. network based on high-precision 10-mile segments, along with the surface conditions, traffic, thickness, lane width, and number of lanes for each segment,” says Hessam AzariJafari, a postdoc at CSHub and a co-author on the paper.

      “To model future paving actions over the analysis period, we assumed four climate conditions; four road types; asphalt, concrete, and composite pavement structures; as well as major, minor, and reconstruction paving actions specified for each climate condition.”

      Using this framework, they analyzed a “projected” and an “ambitious” scenario of reduction strategies and system attributes for buildings and pavements over the 34-year analysis period. The scenarios were defined by the timing and intensity of GHG reduction strategies.

      As its name might suggest, the projected scenario reflected current trends. For the building sector, solutions encompassed expected grid decarbonization and improvements to building codes and energy efficiency that are currently being implemented across the country. For pavements, the sole projected solution was improvements to vehicle fuel economy. That’s because as vehicle efficiency continues to increase, excess vehicle emissions due to poor road quality will also decrease.

      Both the projected scenarios for buildings and pavements featured the gradual introduction of low-carbon concrete strategies, such as recycled content, carbon capture in cement production, and the use of captured carbon to produce aggregates and cure concrete.

      “In the ambitious scenario,” explains Kirchain, “we went beyond projected trends and explored reasonable changes that exceed current policies and [industry] commitments.”

      Here, the building sector strategies were the same, but implemented more aggressively. The pavements sector also abided by more aggressive targets and incorporated several novel strategies, including investing more to yield smoother roads, selectively applying concrete overlays to produce stiffer pavements, and introducing more reflective pavements — which can change the Earth’s energy balance by sending more energy out of the atmosphere.

      Results

      As the grid becomes greener and new homes and buildings become more efficient, many experts have predicted the operational impacts of new construction projects to shrink in comparison to their embodied emissions.

      “What our life-cycle assessment found,” says Jeremy Gregory, the executive director of the MIT Climate Consortium and the lead author on the paper, “is that [this prediction] isn’t necessarily the case.”

      “Instead, we found that more than 80 percent of the total emissions from new buildings and pavements between 2016 and 2050 would derive from their operation.”

      In fact, the study found that operations will create the majority of emissions through 2050 unless all energy sources — electrical and thermal — are carbon-neutral by 2040. This suggests that ambitious interventions to the electricity grid and other sources of operational emissions can have the greatest impact.

      Their predictions for emissions reductions generated additional insights.  

      For the building sector, they found that the projected scenario would lead to a reduction of 49 percent compared to 2016 levels, and that the ambitious scenario provided a 57 percent reduction.

      As most buildings during the analysis period were existing rather than new, energy consumption dominated emissions in both scenarios. Consequently, decarbonizing the electricity grid and improving the efficiency of appliances and lighting led to the greatest improvements for buildings, they found.

      In contrast to the building sector, the pavements scenarios had a sizeable gulf between outcomes: the projected scenario led to only a 14 percent reduction while the ambitious scenario had a 65 percent reduction — enough to meet U.S. Paris Accord targets for that sector. This gulf derives from the lack of GHG reduction strategies being pursued under current projections.

      “The gap between the pavement scenarios shows that we need to be more proactive in managing the GHG impacts from pavements,” explains Kirchain. “There is tremendous potential, but seeing those gains requires action now.”

      These gains from both ambitious scenarios could occur even as concrete use tripled over the analysis period in comparison to the projected scenarios — a reflection of not only concrete’s growing demand but its potential role in decarbonizing both sectors.

      Though only one of their reduction scenarios (the ambitious pavement scenario) met the Paris Accord targets, that doesn’t preclude the achievement of those targets: many other opportunities exist.

      “In this study, we focused on mainly embodied reductions for concrete,” explains Gregory. “But other construction materials could receive similar treatment.

      “Further reductions could also come from retrofitting existing buildings and by designing structures with durability, hazard resilience, and adaptability in mind in order to minimize the need for reconstruction.”

      This study answers a paradox in the field of sustainability. For the world to become more equitable, more development is necessary. And yet, that very same development may portend greater emissions.

      The MIT team found that isn’t necessarily the case. Even as America continues to use more concrete, the benefits of the material itself and the interventions made to it can make climate targets more achievable.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Environment

      Mitigating hazards with vulnerability in mind

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      From tropical storms to landslides, the form and frequency of natural hazards vary widely. But the feelings of vulnerability they can provoke are universal.

      Growing up in hazard-prone cities, Ipek Bensu Manav, a civil and environmental engineering PhD candidate with the MIT Concrete Sustainability Hub (CSHub), noticed that this vulnerability was always at the periphery. Today, she’s studying vulnerability, in both its engineering and social dimensions, with the aim of promoting more hazard-resilient communities.

      Her research at CSHub has taken her across the country to attend impactful conferences and allowed her to engage with prominent experts and decision-makers in the realm of resilience. But more fundamentally, it has also taken her beyond the conventional bounds of engineering, reshaping her understanding of the practice.

      From her time in Miami, Florida, and Istanbul, Turkey, Manav is no stranger to natural hazards. Istanbul, which suffered a devastating earthquake in 1999, is predicted to experience an equally violent tremor in the near future, while Miami ranks among the top cities in the U.S. in terms of natural disaster risk due to its vulnerability to hurricanes.

      “Growing up in Miami, I’d always hear about hurricane season on the news,” recounts Manav, “While in Istanbul there was a constant fear about the next big earthquake. Losing people and [witnessing] those kinds of events instilled in me a desire to tame nature.”

      It was this desire to “push the bounds of what is possible” — and to protect lives in the process — that motivated Manav to study civil engineering at Boğaziçi University. Her studies there affirmed her belief in the formidable power of engineering to “outsmart nature.”

      This, in part, led her to continue her studies at MIT CSHub — a team of interdisciplinary researchers who study how to achieve resilient and sustainable infrastructure. Her role at CSHub has given her the opportunity to study resilience in depth. It has also challenged her understanding of natural disasters — and whether they are “natural” at all.

      “Over the past few decades, some policy choices have increased the risk of experiencing disasters,” explains Manav. “An increasingly popular sentiment among resilience researchers is that natural disasters are not ‘natural,’ but are actually man-made. At CSHub we believe there is an opportunity to do better with the growing knowledge and engineering and policy research.”

      As a part of the CSHub portfolio, Manav’s research looks not just at resilient engineering, but the engineering of resilient communities.

      Her work draws on a metric developed at CSHub known as city texture, which is a measurement of the rectilinearity of a city’s layout. City texture, Manav and her colleagues have found, is a versatile and informative measurement. By capturing a city’s order or disorder, it can predict variations in wind flow — variations currently too computationally intensive for most cities to easily render.  

      Manav has derived this metric for her native South Florida. A city texture analysis she conducted there found that numerous census tracts could experience wind speeds 50 percent greater than currently predicted. Mitigating these wind variations could lead to some $697 million in savings annually.

      Such enormous hazard losses and the growing threat of climate change have presented her with a new understanding of engineering.

      “With resilience and climate change at the forefront of engineering, the focus has shifted,” she explains, “from defying limits and building impressive structures to making structures that adapt to the changing environment around us.”

      Witnessing this shift has reoriented her relationship with engineering. Rather than viewing it as a distinct science, she has begun to place it in its broader social and political context — and to recognize how those social and political dynamics often determine engineering outcomes.

      “When I started grad school, I often felt ‘Oh this is an engineering problem. I can engineer a solution’,” recounts Manav. “But as I’ve read more about resilience, I’ve realized that it’s just as much a concern of politics and policy as it is of engineering.”

      She attributes her awareness of policy to MIT CSHub’s collaboration with the Portland Cement Association and the Ready Mixed Concrete Research & Education Foundation. The commitment of the concrete and cement industries to resilient construction has exposed her to the myriad policies that dictate the resilience of communities.

      “Spending time with our partners made me realize how much of a policy issue [resilience] is,” she explains. “And working with them has provided me with a seat at the table with the people engaged in resilience.”

      Opportunities for engagement have been plentiful. She has attended numerous conferences and met with leaders in the realm of sustainability and resilience, including the International Code Council (ICC), Smart Home America, and Strengthen Alabama Homes.

      Some opportunities have proven particularly fortuitous. When attending a presentation hosted by the ICC and the National Association for the Advancement of Colored People (NAACP) that highlighted people of color working on building codes, Manav felt inspired to reach out to the presenters. Soon after, she found herself collaborating with them on a policy report on resilience in communities of color.

      “For me, it was a shifting point, going from prophesizing about what we could be doing, to observing what is being done. It was a very humbling experience,” she says. “Having worked in this lab made me feel more comfortable stepping outside of my comfort zone and reaching out.”

      Manav credits this growing confidence to her mentorship at CSHub. More than just providing support, CSHub Co-director Randy Kirchain has routinely challenged her and inspired further growth.

      “There have been countless times that I’ve reached out to him because I was feeling unsure of myself or my ideas,” says Manav. “And he’s offered clarity and assurance.”

      Before her first conference, she recalls Kirchain staying in the office well into the evening to help her practice and hone her presentation. He’s also advocated for her on research projects to ensure that her insight is included and that she receives the credit she deserves. But most of all, he’s been a great person to work with.

      “Randy is a lighthearted, funny, and honest person to be around,” recounts Manav. “He builds in me the confidence to dive straight into whatever task I’m tackling.”

      That current task is related to equity. Inspired by her conversations with members of the NAACP, Manav has introduced a new dimension to her research — social vulnerability.

      In contrast to place vulnerability, which captures the geographical susceptibility to hazards, social vulnerability captures the extent to which residents have the resources to respond to and recover from hazard events. Household income could act as a proxy for these resources, and the spread of household income across geographies and demographics can help derive metrics of place and social vulnerability. And these metrics matter.

      “Selecting different metrics favors different people when distributing hazard mitigation and recovery funds,” explains Manav. “If we’re looking at just the dollar value of losses, then wealthy households with more valuable properties disproportionally benefit. But, conversely, if we look at losses as a percentage of income, we’re going to prioritize low-income households that might not necessarily have the resources to recover.”

      Manav has incorporated metrics of social vulnerability into her city texture loss estimations. The resulting approach could predict unmitigated damage, estimate subsequent hazard losses, and measure the disparate impact of those losses on low-income and socially vulnerable communities.

      Her hope is that this streamlined approach could change how funds are disbursed and give communities the tools to solve the entwined challenges of climate change and equity.

      The city texture work Manav has adopted is quite different from the gravity-defying engineering that drew her to the field. But she’s found that it is often more pragmatic and impactful.

      Rather than mastering the elements, she’s learning how to adapt to them and help others do the same. Solutions to climate change, she’s discovered, demand the collaboration of numerous parties — as well as a willingness to confront one’s own vulnerabilities and make the decision to reach out.  More

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      in Environment

      Countering climate change with cool pavements

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      Pavements are an abundant urban surface, covering around 40 percent of American cities. But in addition to carrying traffic, they can also emit heat.

      Due to what’s called the urban heat island effect, densely built, impermeable surfaces like pavements can absorb solar radiation and warm up their surroundings by re-emitting that radiation as heat. This phenomenon poses a serious threat to cities. It increases air temperatures by up as much as 7 degrees Fahrenheit and contributes to health and environmental risks — risks that climate change will magnify.

      In response, researchers at the MIT Concrete Sustainability Hub (MIT CSHub) are studying how a surface that ordinarily heightens urban heat islands can instead lessen their intensity. Their research focuses on “cool pavements,” which reflect more solar radiation and emit less heat than conventional paving surfaces.

      A recent study by a team of current and former MIT CSHub researchers in the journal of Environmental Science and Technology outlines cool pavements and their implementation. The study found that they could lower air temperatures in Boston and Phoenix by up to 1.7 degrees Celsius (3 F) and 2.1 C (3.7 F), respectively. They would also reduce greenhouse gas emissions, cutting total emissions by up to 3 percent in Boston and 6 percent in Phoenix. Achieving these savings, however, requires that cool pavement strategies be selected according to the climate, traffic, and building configurations of each neighborhood.

      Cities like Los Angeles and Phoenix have already conducted sizeable experiments with cool pavements, but the technology is still not widely implemented. The CSHub team hopes their research can guide future cool paving projects to help cities cope with a changing climate.

      Scratching the surface

      It’s well known that darker surfaces get hotter in sunlight than lighter ones. Climate scientists use a metric called “albedo” to help describe this phenomenon.

      “Albedo is a measure of surface reflectivity,” explains Hessam AzariJafari, the paper’s lead author and a postdoc at the MIT CSHub. “Surfaces with low albedo absorb more light and tend to be darker, while high-albedo surfaces are brighter and reflect more light.”

      Albedo is central to cool pavements. Typical paving surfaces, like conventional asphalt, possess a low albedo and absorb more radiation and emit more heat. Cool pavements, however, have brighter materials that reflect more than three times as much radiation and, consequently, re-emit far less heat.

      “We can build cool pavements in many different ways,” says Randolph Kirchain, a researcher in the Materials Science Laboratory and co-director of the Concrete Sustainability Hub. “Brighter materials like concrete and lighter-colored aggregates offer higher albedo, while existing asphalt pavements can be made ‘cool’ through reflective coatings.”

      CSHub researchers considered these several options in a study of Boston and Phoenix. Their analysis considered different outcomes when concrete, reflective asphalt, and reflective concrete replaced conventional asphalt pavements — which make up more than 95 percent of pavements worldwide.

      Situational awareness

      For a comprehensive understanding of the environmental benefits of cool pavements in Boston and Phoenix, researchers had to look beyond just paving materials. That’s because in addition to lowering air temperatures, cool pavements exert direct and indirect impacts on climate change.  

      “The one direct impact is radiative forcing,” notes AzariJafari. “By reflecting radiation back into the atmosphere, cool pavements exert a radiative forcing, meaning that they change the Earth’s energy balance by sending more energy out of the atmosphere — similar to the polar ice caps.”

      Cool pavements also exert complex, indirect climate change impacts by altering energy use in adjacent buildings.

      “On the one hand, by lowering temperatures, cool pavements can reduce some need for AC [air conditioning] in the summer while increasing heating demand in the winter,” says AzariJafari. “Conversely, by reflecting light — called incident radiation — onto nearby buildings, cool pavements can warm structures up, which can increase AC usage in the summer and lower heating demand in the winter.”

      What’s more, albedo effects are only a portion of the overall life cycle impacts of a cool pavement. In fact, impacts from construction and materials extraction (referred to together as embodied impacts) and the use of the pavement both dominate the life cycle. The primary use phase impact of a pavement — apart from albedo effects  — is excess fuel consumption: Pavements with smooth surfaces and stiff structures cause less excess fuel consumption in the vehicles that drive on them.

      Assessing the climate-change impacts of cool pavements, then, is an intricate process — one involving many trade-offs. In their study, the researchers sought to analyze and measure them.

      A full reflection

      To determine the ideal implementation of cool pavements in Boston and Phoenix, researchers investigated the life cycle impacts of shifting from conventional asphalt pavements to three cool pavement options: reflective asphalt, concrete, and reflective concrete.

      To do this, they used coupled physical simulations to model buildings in thousands of hypothetical neighborhoods. Using this data, they then trained a neural network model to predict impacts based on building and neighborhood characteristics. With this tool in place, it was possible to estimate the impact of cool pavements for each of the thousands of roads and hundreds of thousands of buildings in Boston and Phoenix.

      In addition to albedo effects, they also looked at the embodied impacts for all pavement types and the effect of pavement type on vehicle excess fuel consumption due to surface qualities, stiffness, and deterioration rate.

      After assessing the life cycle impacts of each cool pavement type, the researchers calculated which material — conventional asphalt, reflective asphalt, concrete, and reflective concrete — benefited each neighborhood most. They found that while cool pavements were advantageous in Boston and Phoenix overall, the ideal materials varied greatly within and between both cities.

      “One benefit that was universal across neighborhood type and paving material, was the impact of radiative forcing,” notes AzariJafari. “This was particularly the case in areas with shorter, less-dense buildings, where the effect was most pronounced.”

      Unlike radiative forcing, however, changes to building energy demand differed by location. In Boston, cool pavements reduced energy demand as often as they increased it across all neighborhoods. In Phoenix, cool pavements had a negative impact on energy demand in most census tracts due to incident radiation. When factoring in radiative forcing, though, cool pavements ultimately had a net benefit.

      Only after considering embodied emissions and impacts on fuel consumption did the ideal pavement type manifest for each neighborhood. Once factoring in uncertainty over the life cycle, researchers found that reflective concrete pavements had the best results, proving optimal in 53 percent and 73 percent of the neighborhoods in Boston and Phoenix, respectively.

      Once again, uncertainties and variations were identified. In Boston, replacing conventional asphalt pavements with a cool option was always preferred, while in Phoenix concrete pavements — reflective or not — had better outcomes due to rigidity at high temperatures that minimized vehicle fuel consumption. And despite the dominance of concrete in Phoenix, in 17 percent of its neighborhoods all reflective paving options proved more or less as effective, while in 1 percent of cases, conventional pavements were actually superior.

      “Though the climate change impacts we studied have proven numerous and often at odds with each other, our conclusions are unambiguous: Cool pavements could offer immense climate change mitigation benefits for both cities,” says Kirchain.

      The improvements to air temperatures would be noticeable: the team found that cool pavements would lower peak summer air temperatures in Boston by 1.7 C (3 F) and in Phoenix by 2.1 C (3.7 F). The carbon dioxide emissions reductions would likewise be impressive. Boston would decrease its carbon dioxide emissions by as much as 3 percent over 50 years while reductions in Phoenix would reach 6 percent over the same period.

      This analysis is one of the most comprehensive studies of cool pavements to date — but there’s more to investigate. Just as with pavements, it’s also possible to adjust building albedo, which may result in changes to building energy demand. Intensive grid decarbonization and the introduction of low-carbon concrete mixtures may also alter the emissions generated by cool pavements.

      There’s still lots of ground to cover for the CSHub team. But by studying cool pavements, they’ve elevated a brilliant climate change solution and opened avenues for further research and future mitigation.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More