foundry-ml/dataset_concrete_compressive_strength

Machine learning in concrete science: applications, challenges, and best practices

Dataset containing concrete compressive strength for 1030 materials

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/8k1f-mx77
• Year: 2022
• Authors: Li, Zhanzhao, Yoon, Jinyoung, Zhang, Rui, Rajabipour, Farshad, Srubar III, Wil V., Dabo, Ismaila, Radlińska, Aleksandra
• Data Type: tabular

Fields

Field	Role	Description	Units
Cement (component 1)(kg in a m^3 mixture)	input	Amount of cement	kg/m^3
Blast Furnace Slag (component 2)(kg in a m^3 mixture)	input	Amount of blast furnace slag	kg/m^3
Fly Ash (component 3)(kg in a m^3 mixture)	input	Amount of fly ash	kg/m^3
Water (component 4)(kg in a m^3 mixture)	input	Amount of water	kg/m^3
Superplasticizer (component 5)(kg in a m^3 mixture)	input	Amount of superplasticizer	kg/m^3
Coarse Aggregate (component 6)(kg in a m^3 mixture)	input	Amount of coarse aggregate	kg/m^3
Age (day)	input	Age of concrete	days
Concrete compressive strength(MPa, megapascals)	target	Concrete compressive strength	MPa

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/8k1f-mx77")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_concrete_compressive_strength")

Citation

@misc{https://doi.org/10.18126/8k1f-mx77
doi = {10.18126/8k1f-mx77}
url = {https://doi.org/10.18126/8k1f-mx77}
author = {Li, Zhanzhao and Yoon, Jinyoung and Zhang, Rui and Rajabipour, Farshad and Srubar III, Wil V. and Dabo, Ismaila and Radlińska, Aleksandra}
title = {Machine learning in concrete science: applications, challenges, and best practices}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}

License

other

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This dataset was exported from Foundry-ML, a platform for materials science datasets.