Ju Zhou, Feiyi Li, Xinwu Wang, Heng Yin, Wenjing Zhang, Jiaoyang Du, Haibo Pu
Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll
Plants (Basel, Switzerland)
research study aims understand application artificial neural networks anns forecast selfcompacting recycled coarse aggregate concrete scrcac compressive strength different literature 602 available data sets scrcac mix designs collected data rearranged reconstructed trained tested ann model development models established using seven input variables mass cementitious content water natural coarse aggregate content natural fine aggregate content recycled coarse aggregate content chemical admixture mineral admixture used scrcac mix designs two normalization techniques used data normalization visualize data distribution normalization technique three transfer functions used modelling total six different types models run matlab used estimate 28th day scrcac compressive strength normalization technique 2 performs better 1 tansing best transfer function best kfold crossvalidation fold k 7 coefficient determination predicted actual compressive strength 078 training 086 testing impact number neurons layers model performed inputs standards used forecast 28th day compressive strength apart ann machine learning ml techniques like random forest extra trees extreme boosting light gradient boosting techniques adopted predict 28th day compressive strength scrcac compared ml ann prediction shows better results terms sensitive analysis study also extended determine 28th day compressive strength experimental work compared 28th day compressive strength ann best model standard ann mix designs similar fresh hardened properties average compressive strength ann model experimental results 39067 3836 mpa respectively correlation coefficient 1 appears ann validly predict compressive strength concrete