Celik, FatihYildiz, OguzhanColak, Andac BaturBozkir, Samet Mufit2024-11-072024-11-0720231555-256X1555-2578https://doi.org/10.32604/fdmp.2022.021014https://hdl.handle.net/11480/14103In this study, the workability of cement-based grouts containing n-TiO2 nanoparticles and fly ash has been investigated experimentally. Several characteristic quantities (including, but not limited to, the marsh cone flow time, the mini slump spreading diameter and the plate cohesion meter value) have been measured for different percentages of these additives. The use of fly ash as a mineral additive has been found to result in improvements in terms of workability behavior as expected. Moreover, if nano titanium oxide is also used, an improvement can be obtained regarding the bleeding values for the cement-based grout mixes. Using such experimental data, a multi-layer perceptron artificial neural network model has been developed (5 neurons in the hidden layer of the network model have been developed using a total of 42 experimental data). 70% of the data employed in this model have been used for training, 15% for validation and 15% for the test phase. The results demonstrate that the artificial neural network model can predict Marsh cone flow time, mini slump spreading diameter and plate cohesion meter values with an average error of 0.15%.eninfo:eu-repo/semantics/openAccessNano titanium oxidefly ashworkability of groutsbleedingstability of groutsANNArticle19113515810.32604/fdmp.2022.0210142-s2.0-85135520404Q3WOS:000843125500011N/A