Prediction of compressive strength of nano-silica modified engineering cementitious composites exposed to high temperatures using hybrid deep learning models - Tanyildizi, Harun (Pergamon-Elsevier Science Ltd, 2024)
This study estimated the compressive strength of nano-silica-modified engineering cementitious composites subjected to high temperatures using innovative hybrid deep learning models. The innovative hybrid models in this study were designed using autoencoder (AE)-decision tree (DT) and autoencoder (AE)-extreme learning machine (ELM). Additionally, ELM, DT, and deep AE models in this study were designed to compare the results of innovative hybrid deep learning models. The sensitivity analysis was used for the statistical assessment of the experimental results. The input variables of the models were selected as the cement amount, fly ash amount, sand amount, water amount, high-range water reducer amount, PVA (polyvinyl alcohol) fiber amount, nano-silica amount, and the degree of exposure to high temperatures. The compressive strength was used as the output variable of the models. The mixture ratio in the experimental study was 583 kg/m3 cement, 467 kg/m3 sand, 700 kg/m3 fly ash, 187 kg/ m3 water, PVA fiber (0.5 %, 1 %, 1.5 % and 2 %) and nano silica (0 %, 1 %, 2 %, 3 % and 4 %) were used. The ELM, DT, and deep AE models estimated the compressive strength of nano-silicamodified engineering cementitious composites subjected to high temperatures with 93.86 %, 77.35 %, and 86.5 % accuracy, respectively. Also, the same compressive strength was estimated with 94.28 % and 98 % accuracy using the hybrid deep AE-DT and AE-ELM models. This study found that the innovative hybrid deep AEELM model predicted compressive strength with higher accuracy than the deep AE-DT, DT, ELM, and deep AE models. Additionally, the deep AE-DT model predicted compressive strength with higher accuracy than nonhybrid models. Thus, it can be stated that innovative hybrid deep models are more advantageous than other models in estimating the compressive strength of ECC. The sensitivity analysis obtained that the PVA fiber was the most significant variable affecting the compressive strength results of nano-silica-modified engineering cementitious composites subjected to high temperatures.
Keywords: Engineering cementitious composites; Nano-silica; High temperatures; Deep autoencoder; Decision tree; Extreme learning machine; Hybrid deep learning