Sebastian Ament, Andrew Witte, Nishant Garg, Julius Kusuma
We lifted 11 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/SustainableConcrete | canonical | 5 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| additive_time_kernel | Ran | facebookresearch/SustainableConcrete/boxcrete/kernels.py code served (permissive licence) · get_code("ab34ce4558c7827f") |
| derive_source_from_mix_name | Ran | facebookresearch/SustainableConcrete/boxcrete/mix_naming.py code served (permissive licence) · get_code("e669f6a366c5fbff") |
| get_strength_gp_input_transform | Ran | facebookresearch/SustainableConcrete/boxcrete/strength_model_legacy.py code served (permissive licence) · get_code("e14dd0aaf23fe992") |
| max_scale_Y | Ran | facebookresearch/SustainableConcrete/boxcrete/features.py code served (permissive licence) · get_code("054a15188e62cf01") |
| plot_calibration | Ran | facebookresearch/SustainableConcrete/boxcrete/plotting.py code served (permissive licence) · get_code("c05dfdf744f4c46e") |
| block_loo_loss | Not yet run | facebookresearch/SustainableConcrete/boxcrete/block_loo.py code served (permissive licence) · get_code("e575da64657e1552") |
| canonical_name | Not yet run | facebookresearch/SustainableConcrete/boxcrete/mix_naming.py code served (permissive licence) · get_code("6690faa2afa56365") |
| compute_loo_cv | Not yet run | facebookresearch/SustainableConcrete/boxcrete/plotting.py code served (permissive licence) · get_code("f53a1315a130fec0") |
| legacy_name | Not yet run | facebookresearch/SustainableConcrete/boxcrete/mix_naming.py code served (permissive licence) · get_code("b5bf5da63e8d93f3") |
| train_block_loo | Not yet run | facebookresearch/SustainableConcrete/boxcrete/block_loo.py code served (permissive licence) · get_code("dee0d1ce6b2dc840") |
| within_group_prior | Not yet run | facebookresearch/SustainableConcrete/boxcrete/priors.py code served (permissive licence) · get_code("12752291505ed111") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 CO 2 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.18288")
get_code_for_paper("2310.18288")
have("2310.18288")
Connect an agent — have() is free.