Sustainable Concrete via Bayesian Optimization
- URL: http://arxiv.org/abs/2310.18288v3
- Date: Mon, 20 Nov 2023 16:17:38 GMT
- Title: Sustainable Concrete via Bayesian Optimization
- Authors: Sebastian Ament, Andrew Witte, Nishant Garg, Julius Kusuma
- Abstract summary: Eight percent of global carbon dioxide emissions can be attributed to the production of cement.
The discovery of lower-carbon concrete formulae is therefore of high significance for sustainability.
- Score: 4.149122595744125
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Eight percent of global carbon dioxide emissions can be attributed to the
production of cement, the main component of concrete, which is also the
dominant source of CO2 emissions in the construction of data centers. The
discovery of lower-carbon concrete formulae is therefore of high significance
for sustainability. However, experimenting with new concrete formulae is time
consuming and labor intensive, as one usually has to wait to record the
concrete's 28-day compressive strength, a quantity whose measurement can by its
definition not be accelerated. This provides an opportunity for experimental
design methodology like Bayesian Optimization (BO) to accelerate the search for
strong and sustainable concrete formulae. Herein, we 1) propose modeling steps
that make concrete strength amenable to be predicted accurately by a Gaussian
process model with relatively few measurements, 2) formulate the search for
sustainable concrete as a multi-objective optimization problem, and 3) leverage
the proposed model to carry out multi-objective BO with real-world strength
measurements of the algorithmically proposed mixes. Our experimental results
show improved trade-offs between the mixtures' global warming potential (GWP)
and their associated compressive strengths, compared to mixes based on current
industry practices. Our methods are open-sourced at
github.com/facebookresearch/SustainableConcrete.
Related papers
- Industrial-scale Prediction of Cement Clinker Phases using Machine Learning [3.600969417368042]
Cement production exceeding 4.1 billion tonnes and contributing 2.4 tonnes of CO2 annually.
Traditional process models for cement manufacturing are confined to steady-state conditions with limited predictive capability for mineralogical phases.
Here, exploiting a comprehensive two-year operational dataset from an industrial cement plant, we present a machine learning framework that accurately predicts clinker mineralogy from process data.
arXiv Detail & Related papers (2024-12-16T17:03:04Z) - Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks [0.0]
This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks.
A detailed database of 708 CFRP confined concrete cylinders is developed with information on 8 parameters including the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete and the ultimate compressive strength of confined concrete fcc'
The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO predicted the results with
arXiv Detail & Related papers (2023-12-22T17:27:50Z) - Learning Energy-Based Prior Model with Diffusion-Amortized MCMC [89.95629196907082]
Common practice of learning latent space EBMs with non-convergent short-run MCMC for prior and posterior sampling is hindering the model from further progress.
We introduce a simple but effective diffusion-based amortization method for long-run MCMC sampling and develop a novel learning algorithm for the latent space EBM based on it.
arXiv Detail & Related papers (2023-10-05T00:23:34Z) - Probabilistic selection and design of concrete using machine learning [0.0]
Making reliable property predictions with machine learning can facilitate performance-based specification of concrete.
We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact.
Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering.
arXiv Detail & Related papers (2023-04-21T19:20:40Z) - Machine Guided Discovery of Novel Carbon Capture Solvents [48.7576911714538]
Machine learning offers a promising method for reducing the time and resource burdens of materials development.
We have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing carbon capture.
The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set.
arXiv Detail & Related papers (2023-03-24T18:32:38Z) - Robustness in Fatigue Strength Estimation [61.85933973929947]
In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation.
Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation.
We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
arXiv Detail & Related papers (2022-12-02T12:30:29Z) - Real-time high-resolution CO$_2$ geological storage prediction using
nested Fourier neural operators [58.728312684306545]
Carbon capture and storage (CCS) plays an essential role in global decarbonization.
Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration.
We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale.
arXiv Detail & Related papers (2022-10-31T04:04:03Z) - Posterior Coreset Construction with Kernelized Stein Discrepancy for
Model-Based Reinforcement Learning [78.30395044401321]
We develop a novel model-based approach to reinforcement learning (MBRL)
It relaxes the assumptions on the target transition model to belong to a generic family of mixture models.
It can achieve up-to 50 percent reduction in wall clock time in some continuous control environments.
arXiv Detail & Related papers (2022-06-02T17:27:49Z) - Accelerated Design and Deployment of Low-Carbon Concrete for Data
Centers [13.376376442187922]
We use conditional variational autoencoders (CVAEs) to discover concrete formulas with desired properties.
Our model is trained just using a small open dataset from the UCI Machine Learning Repository.
We report on how these formulations were used in the construction of buildings and structures in a Meta data center in DeKalb, IL, USA.
arXiv Detail & Related papers (2022-04-11T20:40:13Z) - Learning to Sieve: Prediction of Grading Curves from Images of Concrete
Aggregate [1.6249267147413522]
This paper proposes a deep learning based method for the determination of concrete aggregate grading curves.
In this context, we propose a network architecture applying multi-scale feature extraction modules.
arXiv Detail & Related papers (2022-04-07T10:04:05Z) - Density Ratio Estimation via Infinitesimal Classification [85.08255198145304]
We propose DRE-infty, a divide-and-conquer approach to reduce Density ratio estimation (DRE) to a series of easier subproblems.
Inspired by Monte Carlo methods, we smoothly interpolate between the two distributions via an infinite continuum of intermediate bridge distributions.
We show that our approach performs well on downstream tasks such as mutual information estimation and energy-based modeling on complex, high-dimensional datasets.
arXiv Detail & Related papers (2021-11-22T06:26:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.