The effect of nano-CuO on mechanical, microstructural, and self-healing properties of clayey sandy soils

Choobbasti, Asskar Janalizadeh; Valizadeh, Mohammad

doi:10.1007/s12517-022-10621-5

Arab J Geosci

2022

DOI: 10.1007/s12517-022-10621-5

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The effect of nano-CuO on mechanical, microstructural, and self-healing properties of clayey sandy soils

Asskar Janalizadeh Choobbasti

Mohammad Valizadeh

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Cited by 2 publications

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Comparative Study on the Effects of Five Nano-Metallic Oxide Particles on the Long-Term Mechanical Property and Durability of Cement Mortar

Dong

Chen

et al. 2023

Buildings

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Nano-metallic oxide particles have been found to be potentially effective microstructural reinforcements for cement mortar and have become a research hotspot in recent years for nano-modification technology of building materials. However, different conclusions have been obtained due to various researchers used different research methods, which have resulted in a deficiency for the performance comparison between different nano-metallic oxide particles. In the present study, the effects of five kinds of nano-metallic oxide particles, namely nano-MgO, nano-Al2O3, nano-ZrO2, nano-CuO, and nano-ZnO, on the performance of cement mortar at 28 days and 730 days in terms of mechanical, durability, microstructure, and pore size distribution properties by performing different experiments were investigated. Test results show that the dosage of nano-MgO, nano-Al2O3, nano-ZrO2, nano-CuO, and nano-ZnO is 2%, 1%, 1%, 1%, and 2%, respectively, where they can significantly prove the compressive and flexural strengths, decrease the porosity, drying shrinkage, and permeability, and refine the pore size distribution of cement mortar. It can be seen through SEM analysis that nano-metallic oxide particles can promote cement hydration, and also refine the size and distribution of Ca(OH)2 crystal, but the specific principles are different. The analysis concluded that the five kinds of nano-metallic oxide particles can play a filling role in cementitious materials to improve the denseness and surface activity role to promote the hydration of cement particles, thus improving the mechanical properties, durability, and pore size distribution of cementitious materials, with the order of their modification effect on cement-based materials being nano-ZrO2 > nano-MgO > nano-Al2O3 > nano-ZnO > nano-CuO.

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Comparative Study on the Effects of Five Nano-Metallic Oxide Particles on the Long-Term Mechanical Property and Durability of Cement Mortar

Dong

Chen

et al. 2023

Buildings

View full text Add to dashboard Cite

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Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties

et al. 2022

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This study presents a novel method for predicting the undrained shear strength (cu) using artificial intelligence technology. The cu value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The group method of data handling (GMDH)-type neural network (NN) was utilized for the prediction of cu. The GMDH-type NN models were designed with various combinations of input parameters. In the prediction, the effective stress (σv’), standard penetration test result (NSPT), liquid limit (LL), plastic limit (PL), and plasticity index (PI) were used as input parameters in the design of the prediction models. In addition, the GMDH-type NN models were compared with the most commonly used method (i.e., linear regression) and other regression models such as random forest (RF) and support vector regression (SVR) models as comparative methods. In order to evaluate each model, the correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) were calculated for different input parameter combinations. The most effective model, the GMDH-type NN with input parameters (e.g., σv’, NSPT, LL, PL, PI), had a higher correlation coefficient (R2 = 0.83) and lower error rates (MAE = 14.64 and RMSE = 22.74) than other methods used in the prediction of cu value. Furthermore, the impact of input variables on the model output was investigated using the SHAP (SHApley Additive ExPlanations) technique based on the extreme gradient boosting (XGBoost) ensemble learning algorithm. The results demonstrated that using the GMDH-type NN is an efficient method in obtaining a new empirical mathematical model to provide a reliable prediction of the undrained shear strength of soils.

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The effect of nano-CuO on mechanical, microstructural, and self-healing properties of clayey sandy soils

Cited by 2 publications

References 37 publications

Comparative Study on the Effects of Five Nano-Metallic Oxide Particles on the Long-Term Mechanical Property and Durability of Cement Mortar

Comparative Study on the Effects of Five Nano-Metallic Oxide Particles on the Long-Term Mechanical Property and Durability of Cement Mortar

Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties

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