Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials

Tien-Thinh Le¹  Huong-LanThi Vu²  BinhThai Pham²  DongVan Dao²  Hai-Bang Ly² 

[1] Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;University of Transport Technology, Hanoi 100000, Vietnam;

关键词: compressive strength;    foamed concrete;    artificial neural network;    monte carlo simulations;   

DOI  :  10.3390/ma13051072

来源: DOAJ

【 摘 要 】

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R² value of 0.976 on the training part and an R² of 0.972 on the testing part, using the optimized C-ANN-[3−4−5−1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

【 授权许可】

Unknown