Machine learning prediction of 28-day compressive strength of CNT/cement composites with considering size effects

Yang, Jinlong; Fan, Yucheng; Zhu, Fan; Ni, Zhengwei; Wan, Xili; Feng, Chun-Bo; Yang, Jie

doi:10.1016/j.compstruct.2023.116713

Cited by 11 publications

(1 citation statement)

References 61 publications

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“…Machine learning techniques applied to predict concrete compressive strength based on cellulose nanofibers yield R 2 >0.72, MAPE ≤ 0.1, and MAE ≤ 5, aligning with the standard of R 2 values exceeding 0.60 [42]. Despite proving superior performance, ANN necessitates substantial experience and computational resources [43]. The results of this study indicate that geopolymers utilizing nanosilica and CNCs, modeled using ANN methodology, exhibit the influence of nanosilica and CNCs on geopolymer compressive strength when applied at concentrations of 2%-4% and 1%-3%, respectively.…”

Section: Table 1 the Comparison Of Modelling Results Based On Ann Par...mentioning

confidence: 70%

Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements

Rahmawati,

Aisyah,

Sanusi

et al. 2024

Civ Eng J

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The utilization of nanosilica and cellulose nanocrystals (CNCs) in cement geopolymers remains challenged by intricacies and uncertainties regarding their concentration, posing difficulties in the formulation of systematic geopolymer mix designs. This study aims to formulate models based on Artificial Neural Networks (ANN) capable of forecasting the compressive strength of geopolymers through the utilization of experimentally acquired data. Nanosilica was applied at concentrations of 2%–4% and CNCs at 1%–3%. ANN was modeled using MATLAB to predict the compressive strength of the geopolymer. The results indicated an effect of nanosilica and CNCs on the compressive strength of geopolymer at 2%–4% concentration and 1%–3% CNCs. The best ANN was the GDX training function, purelin activation function, LGD and LGDM learning functions, Lr 0.1 and 0.01 at the number of epochs 3812 out of 25000 and 1774 out of 25000, resulting in the best correlation values of 0.994 and 0.959; the lowest RMSE values are 0.022 and 0.110. The results of the ANN model built based on actual data prove that the model is helpful for accurate simulation to predict the compressive strength of geopolymer cement. This study contributes novelty by optimizing the design model for Geopolymer Cements incorporating nanosilica and CNCs. Doi: 10.28991/CEJ-SP2024-010-03 Full Text: PDF

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Section: Table 1 the Comparison Of Modelling Results Based On Ann Par...mentioning

confidence: 70%

Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements

Rahmawati,

Aisyah,

Sanusi

et al. 2024

Civ Eng J

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Using artificial neural network and parametric regression to predict the effect of mechanical vibrations on heat transfer coefficient of a counter flow heat exchanger containing MWCNTs-water nanofluid

Meghdadi Isfahani,

Hosseinian,

Bagherzadeh

2024

J Therm Anal Calorim

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Machine learning prediction of 28-day compressive strength of CNT/cement composites with considering size effects

Cited by 11 publications

References 61 publications

Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements

Artificial Intelligence Models for Predicting the Compressive Strength of Geopolymer Cements

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Using artificial neural network and parametric regression to predict the effect of mechanical vibrations on heat transfer coefficient of a counter flow heat exchanger containing MWCNTs-water nanofluid

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