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Multimodal machine learning approach for exploring the 28-day compressive strength of nanomaterials-reinforced cement composites

Yang Jinlong, Zeng Bowen, Hang Ziyan, Fan Yucheng, Ni Zhi, Feng Chuang, Liu Chuang, Yang Jie

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 3

art. no. e202, 2023

Accurately predicting the 28-day compressive strength (CS) of carbon nanotubes-reinforced cement composites (CNTRCCs) and graphene oxide-reinforced cement composites (GORCCs) is crucial for accelerating their potential application in civil engineering. However, traditional experimental and theoretical modeling methods suffer from problems, including time-consuming, costly, and inefficient. Moreover, it is also challenging to consider the effects of multiple coupling factors. In this work, a multimodal machine learning (ML) approach is proposed as the first attempt to explore the complex relationships between the CS of hybrid system containing both CNTRCCs and GORCCs. The proposed multimodal ML shows great potential in estimating the nanomaterials-reinforced cement composites with a coefficient of determination (R2) of 0.96, surpassing the single-modal ML approaches. The results demonstrate the effectiveness of the developed model in accurately predicting the 28-day CS of hybrid system containing both CNTRCCs and GORCCs. Shapley additive explanations (SHAP) analysis illustrates that the optimal concentration of CNT is approximately 0.5 wt%, and preferred length of CNT and sheet size of GO are within a range of 20–30 μm and below 10 μm, respectively. Additionally, the enhancement effect of a single-layer GO is better than its multilayer counterparts.

Primary nonlinear damped natural frequency of dielectric composite beam reinforced with graphene platelets (GPLs)

Qian Qiangfei, Wang Yu, Zhu Fan, Feng Chuang, Yang Jie, Wang Shuguang

Archives of Civil and Mechanical Engineering

2022

Vol. 22, no. 1

art. no. e53, 2022

The present study deals with the primary damped natural frequency of dielectric composite beam reinforced with graphene platelet (GPL). The beam is subjected to pre-stress in the longitudinal direction and external electrical loading throughout the beam thickness direction for tuning the frequency characteristics. The material properties of the composites required for structural analysis are determined by effective medium theory (EMT) and rule of mixture. Using Timoshenko beam theory and Hamilton’s principle, the governing equations for damped nonlinear free vibration of the beam are derived and solved numerically by differential quadrature (DQ) and direct iterative methods. The effects of the attributes of the electrical loading and the GPL fillers on the damped free vibration characteristics are investigated. The analysis shows that when the GPL concentration is greater than the percolation threshold, the voltage of the electrical loading and GPL aspect ratio start to play a vital role in the damped vibration. The nonlinear damped frequency of the hinged-hinged (H–H) beam decreases by 83.8% when the voltage increases from 0 to 30 V. It is found that there exist two critical AC (alternating current) frequencies, i.e., approximate 10−3 Hz and 102 Hz, around which the primary damped natural frequency has a sudden jump as AC frequency either slightly increases or decreases. The vibration characteristics presented demonstrate the potential of developing smart composite structures whose vibration characteristics can be actively tuned by changing the attributes of the applied electrical loading.