<div>Portland cement concrete (PCC) is a major contributor to human-made CO2 emissions. To address this environmental impact, fly ash geopolymer concrete (FAGC) has emerged as a promising low-carbon alternative. This study establishes a robust compressive strength prediction model for FAGC and develops an optimal mixture design method to achieve target compressive strength with minimal CO2 emissions. To develop robust prediction models, comprehensive factors, including fly ash characteristics, mixture proportions, curing parameters, and specimen types, are considered, a large dataset comprising 1136 observations is created, and polynomial regression, genetic programming, and ensemble learning are employed. The ensemble learning model shows superior accuracy and generalization ability with an RMSE value of 1.81 MPa and an R2 value of 0.93 in the experimental validation set. Then, the study integrates the developed strength model with a life cycle assessment-based CO2 emissions model, formulating an optimal FAGC mixture design program. A case study validates the effectiveness of this program, demonstrating a 16.7% reduction in CO2 emissions for FAGC with a compressive strength of 50 MPa compared to traditional trial-and-error design. Moreover, compared to PCC, the developed FAGC achieves a substantial 60.3% reduction in CO2 emissions. This work provides engineers with tools for compressive strength prediction and low carbon optimization of FAGC, enabling rapid and highly accurate design of concrete with lower CO2 emissions and greater sustainability.</div>

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Training Set

Alex Keeler · Figshare · 2015

Training Set

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Training Set

Zink, Samantha, Qu, Songrong, Holton, Tom et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

Dataset (training set) used for training the neural network

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Training Set

Zink, Samantha, Qu, Songrong, Holton, Tom et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

Dataset (training set) used for training the neural network

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Training Set

Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning · 2010

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Training set

Francois Lang · OSF Preprints (OSF Preprints) · 2018

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Training set

Francois Lang · OSF Preprints (OSF Preprints) · 2018

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Training Set

Joshua Whittam · Figshare · 2015

This was the JMP training set for the project

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training set and test set

Belter Xin · Figshare · 2020

s3-1_x_training_set.csv- separator: tab - CID, mol_path (skeleton), fragment_cluster, fragment_cluster_smiles s3-2_x_train_mol_sentence.csv- separator: space- fragment_cluster_smiles s3-3_x_test_set.csv- separator: tab - CID, mol_path (skeleton), fragment_cluster, fragment_cluster_smiles s3-4_x_test_mol_sentence.csv- separator: space- fragment_cluster_smiles

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training set and test set

Belter Xin · Figshare · 2020

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Training set and test set.

Shichao Hou (18585070), Peng Zhao (128233), Peng Cui (33358) et al. · Figshare · 2024

<div>The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets</div>

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BTSbot training set

Rehemtulla, Nabeel · Arch · 2023

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