The construction industry has suffers from extra ordinary utilization of cement and sand in all the part of globe. For overcome the future scarcity issue partial replacement to cement with option of fly ash material and fine aggregate or sand with bottom ash prove better solution. For this study, effect of different water cement ratio mainly 0.5, 0.55, 0.6 evaluated on binder content 300kg/m3 of concrete. f/c (fly ash/cement) ratio also taken into account with wide range as 0, 0.11, 0.25, 0.43, 0.67 and 1.0. The mechanical strength of concrete specimens evaluated. The traditional practice generally neglected the issue of splitting to compressive ratio(ft/fc). In this experimental investigation, the value of ft/fc ratio found between ranges 6.76 to 11. This range are principally important for fly ash + bottom ash involvement in concrete for determining mechanical strength which has interlink to each other.
Supplementary materials (SM) for cement replacement became more feasible in the previous decade due to their pozzolanic strength and durability properties. The strength variation according to the age of the binding material is a critical subject for SM concrete. The time of water curing is critical in order to maintain the pozzolanic reaction in SM concrete, which assists in the development of strength in cementitious properties.In this study, the laboratory results of concrete specimens were assessed for various mix designs, and the obtained corresponding strengths were also predicted with ANN techniques. The difference between experimental and ANN predicted values was marginally low. Thus, the ANN model applicability emphasised the productive use in predicting the strength of supplementary materials like fly ash or any similar pozzalanic mineral admixture. This research proposes an ANN-based technique for predicting the strength of fly ash added SM concrete. Typical experimental data is used to build, train, and test the artificial neural network (ANN) model. With ANN and input parameters, a total of 324 distinct data points for SM concrete were utilised to estimate SM concrete strength. Various combinations of layers, number of neurons, and learning rate were examined during the training phase. When the root mean square error (RMSE) reached or remained below 0.001, the training was stopped, and the findings were verified using a test data set. With respect to the relative error provided for trained model data, the results achieved were typically below 10% for compressive strength and below 5% for split tensile strength. The ANN models predict concrete strength with excellent accuracy, and the findings show that utilising ANNs to predict concrete strength is both practicable and advantageous.
The supplementary or alternative material to cement has been an emerging field in civil engineering. The concrete ingredients have become modern due to the need for reducing global warming and material scarcity problem. Fly ash is used to replace cement partially in concrete, therefore the concrete mix has changed their characteristics as obtained by normal concrete. The proper investigation always required for performance measurement and to measure the fresh concrete property, the slump cone test, and compaction factor test are the tool. The Slump Test has become the most frequently performed due to the practicality of the recommended equipment and the experiment protocol. The slump test involves the cone’s behavior under the action of gravitational forces. The slump check is a realistic way to gauge the workability. The concrete slump test and compaction factor test are used to determine the consistency of fresh concrete before it sets. This paper has focused on testing the 54 mixes having various water-cm ratios like 0.5, 0.55, 0.6, 0.45, and 0.4. Fly ash material possesses satisfactory workability properties due to their similar oxide compositions. Due to the fineness of fly ash, less bleeding observed than control concretes.
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