Correlations among physical properties of pervious concrete with different aggregate sizes and mix proportions
Authors:
Qifeng Lyu,
Pengfei Dai,
Anguo Chen
Abstract:
Permeable pavement material can benefit urban environment. Here in this work, different aggregate sizes and mix proportions were used to manufacture pervious pavement concrete and investigate correlations among its properties. The porosity, permeability, compressive strength, inner structure, thermal conductivity, and abrasion resistance of the specimens were obtained. Results showed lower aggrega…
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Permeable pavement material can benefit urban environment. Here in this work, different aggregate sizes and mix proportions were used to manufacture pervious pavement concrete and investigate correlations among its properties. The porosity, permeability, compressive strength, inner structure, thermal conductivity, and abrasion resistance of the specimens were obtained. Results showed lower aggregate-to-cement ratios and higher water-to-cement ratios led to porosity reduction, which decreased the permeability coefficient but increased the compressive strength, thermal conductivity, and abrasion resistance of the pervious concrete. Compared to the mixes, the aggregate sizes affected the physical properties of pervious concrete less. However, the sizes of pores and cement in the pervious concrete were more affected by aggregate sizes than by mixes. Moreover, the porosity, permeability coefficient, and compressive strength of the pervious concrete can be correlated by the power law, whereas the correlation between the porosity and abrasion resistance index can be fitted by a linear law.
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Submitted 2 June, 2024;
originally announced June 2024.
Local environment-based machine learning for molecular adsorption energy prediction
Authors:
Yifan Li,
Yihan Wu,
Yuhang Han,
Qujie Lyu,
Hao Wu,
Xiuying Zhang,
Lei Shen
Abstract:
Most machine learning (ML) models in Materials Science are developed by global geometric features, often falling short in describing localized characteristics, like molecular adsorption on materials. In this study, we introduce a local environment framework that extracts local features from crystal structures to portray the environment surrounding specific adsorption sites. Upon OC20 database (~20…
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Most machine learning (ML) models in Materials Science are developed by global geometric features, often falling short in describing localized characteristics, like molecular adsorption on materials. In this study, we introduce a local environment framework that extracts local features from crystal structures to portray the environment surrounding specific adsorption sites. Upon OC20 database (~20,000 3D entries), we apply our local environment framework on several ML models, such as random forest, convolutional neural network, and graph neural network. It is found that our framework achieves remarkable prediction accuracy in predicting molecular adsorption energy, significantly outperforming other examined global-environment-based models. Moreover, the employment of this framework reduces data requirements and augments computational speed, specifically for deep learning algorithms. Finally, we directly apply our Local Environment ResNet (LERN) on a small 2DMatPedia database (~2,000 2D entries), which also achieves highly accurate prediction, demonstrating the model transferability and remarkable data efficiency. Overall, the prediction accuracy, data-utilization efficiency, and transferability of our local-environment-based ML framework hold a promising high applicability across a broad molecular adsorption field, such as catalysis and sensor technologies.
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Submitted 19 November, 2023;
originally announced November 2023.