Decarbonizing Concrete with Artificial Intelligence | 9am March 12, 2024

[Grigory Bronevetsky]

Modeling Talks

Decarbonizing Concrete with Artificial Intelligence Mathieu Bauchy, UCLA Tuesday, Mar 12 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/decarbonizing-concrete-with-artificial-intelligence Abstract: With an annual production of 4 tons per capita, concrete is the second most used material in the world after water. Although concrete has largely defined modern society, it comes with a hidden cost: it is a climate killer. Concrete contributes to 8% of global CO 2 emissions, which is quadruple the emissions of the entire aviation industry. In this presentation, I will discuss how artificial intelligence can be used to reduce the carbon footprint of concrete. Based on a dataset of more than 1 million concrete mixtures, we trained a series of machine learning models that accurately predict the performance of a concrete formulation based on its mixture proportions. Based on these models, we introduced an inverse design engine that generates optimal concrete formulations featuring minimum carbon footprint while meeting all required performance targets and constraints. This approach results in an average reduction in concrete’s global warming potential (GWP) of 30%—with no changes in the raw materials, no modification of the production process, and no cost premium.   Bio:  Mathieu Bauchy is an Associate Professor in the Civil & Environmental Engineering Department at the University of California, Los Angeles (UCLA). His research focuses on decoding the physics governing the behavior of construction materials using simulations and artificial intelligence—with a focus on decarbonizing the construction industry. He is also co-founder of the cleantech startup Concrete.ai, which uses generative AI to prescribe new concrete formulations that are both less carbon-intensive and more economical. More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home

[Grigory Bronevetsky]

Video Recording
Slides

Abstract:

• Focus: decarbonizing concrete

• Concrete:

  • Critical material for most construction: buildings, roads

  • 4 Tons per Capital Annual Production: most used material after water

  • Significant CO2 emissions (driven by global scale, rather than per-Ton carbon intensity)

  • Concrete = water+sand+cement+gravel+additives

• Concrete Carbon Footprint

  • 8% of global CO2 emissions (e.g. aviation is 2%)

  • Hard to decarbinize

  • Stages:

    • Production

    • Construction

    • Use

    • End of Life

  • Focus: production stage / cradle-to-gate

    • Extraction and upstream production, 

    • Transportation to factory, 

    • Manufacturing

    • Most challenging right now; more progress has been made on later stages

    • 50%+ of overall emissions: https://www.istructe.org/IStructE/media/Public/TSE-Archive/2020/A-brief-guide-to-calculating-embodied-carbon.pdf

  • Goal: attribute emissions to different stages of production

• Cement in concrete

  • 15% by mass

  • 50% by material cost

  • 95% by carbon footprint

• Opportunity: reduce use of cement in concrete without compromising quality / structural integrity

  • Challenge: solution must work at the huge global scale (e.g. alternative materials must be available in a large volume)

  • Approach: use AI to optimize concrete design

  • Balance:

    • Product attributes: strength, durability, ec.

    • Manufacturing attributes: slump (how liquid it is), setting time at construction site, pumpability, etc. (affect cost of production and use of concrete)

  • Optimization

    • Cost function

    • Degrees of freedom

    • Constraints

  • Primary degrees of freedom: ratios of the various inputs

    • Fine aggregates

    • Coarse aggregates

    • Water

    • Air

    • Cement

      • May be replaced by alternative materials: fly ash, slag, silica fume

      • .8 Ton of CO2 released per ton, capturing this CO2 will increase cost of concrete by 2x/3x

    • Admixtures

    • Input availability varies by location

  • Challenge of optimization

    • 1.34E10 possible solutions in design space

    • 800 average number of concrete formulations per plant for different use-cases

    • No good way to simulate concrete due to its internal heterogeneity, multi-scale dynamics

• Optimization via machine learning

  • Datasets: 

    • Very little ~1K data points

    • High variability in measurement, many outliers:

(measurement error, human error, error in concrete batchng/machinery, data processing/recording error, intrinsic variability of concrete, unexpected but true behavior)

• Need to extrapolate from datasets to different concrete designs that don’t exist yet

• Need to explicitly capture uncertainty (e.g. in strength estimates)

• Concrete database:

  • >1m data points

  • Can understand tradeoffs between water/cement ratio and strength, cement solid fraction and strength

• Cement database

  • >2k datapoints

  • Different chemical compositions

• Fly ash database

  • >20k datapoints

  • Different chemical compositions

• Managing outliers via an ensemble-based detector

• Extrapolation from available data vie leave-one-cluster-out cross validation

  • Cluster data

  • Train on all-but-1 cluster, predict on remaining cluster

  • Adjust hyperparameters to minimize validation error to avoid in-sample overfitting

• Ensembled neural networks to predict compressive strength, shrinkage, slump

• Human vs AI competition

  • Goal: densign concrete mix with lowest embodied carbon showing 5000psi strength

  • AI succeed in

    • Meeting strength target and 

    • Meeting slump goal

    • Lower embodied CO2

    • Material cost decreased

    • Can estimate uncertainty in its predictions

    • Has less bias than human, can use material combinations that human expert did not anticipate

  • Human barely missed on strength target, design had more CO2

  • SHAP analysis to understand reasons for AI’s design

• From laboratory to field

  • Challenge: regionality of materials

  • Material variations over time

  • Large number of mix designs per plant for different use-cases

  • Manufacturing uncertainties

  • Hard to quantify properties with no standard test (finishability, pumpability)

  • Data availability is limited

• Concrete.ai: software solution to use generative AI to reduce concrete’s cost and carbon footprint

  • https://www.concrete.ai/

  • Partners across North America

  • Native software integration to directly connect to manufacturer software to get data

  • High-throughput optimization of all concretes at a given plant at once

  • Active learning that dynamically adapts models

  • 2 million cubic yards of concrete optimized

  • 30% carbon footprint reduction (avg)

  • 5% materials cost savings

  • Estimate: if everyone used AI to optimize concrete this would mitigate 500m Tons of CO2 emissions