## 3 Results and Discussion
### Strength Prediction and Relationship Analysis  
Inside a decade, analysts have investigated the capability of Artificial Neural Networks systems (ANNs), a nonlinear displaying approach, in anticipating the compressive quality of solids, because of its capacity to learn input-yield connection for any difficult issue in a productive way. An Artificial Neural Network (ANN) does not require particular condition frame [11]; rather, it just needs adequate input-yield information. It can likewise persistently retrain new information to adjust new information helpfully. ANNs have been explored to manage the issues including inadequate or lost data. The capacity of the manufactured neural system to be an all-inclusive estimator has been generally used to show issues in which the connection among needy and free factors is not unmistakably found. At the point when the quantity of segments increases, the connection between factors turns out to be generally complex. Furthermore, the utilization of a nonlinear displaying approach is required. Recently, ANNs have been connected to numerous structural building applications with some level of progress. ANNs have been connected to geotechnical issues such as the expectation of settlement of shallow establishments [43]. Scientists have additionally utilized ANN in auxiliary building [44]. A few scientists have recently proposed another technique of blend plan and forecast of solid quality utilizing neural organize. Additionally, a few works were accounted for on the utilization of neural organize based demonstrating approach in anticipating the solid quality [5; 6; 7; 8; 9; 10; 11; 12; 13]. A few endeavors have been made to portray the compressive quality properties utilizing conventional relapse investigation apparatuses and factual models [15; 16; 17]. In any case, the improvement of neural system models for anticipating the quality of SCC has not been completely explored. In this manner, it was required to create some reasonable technique to gauge the compressive quality of self-compacting concrete in view of its constituents at the season of the outline. Thus, the goal of the present examination was to inspect the capability of ANN for foreseeing the 28-day compressive quality of SCC blends, with information acquired from writing [11]. These models were additionally connected to forecast of quality at 7, 14, 21 and 28 days to the information acquired tentatively. The perplexing relationship between blend extents and building properties of SCC was produced in view of information acquired tentatively. It was observed that the neural system could successfully anticipate compressive quality, regardless of many-sided information and could be utilized as an instrument to help basic leadership, by enhancing the effectiveness of the procedure. Descriptive data used for ANN modeling purpose  
Figure 5: Comparative flexural strength trend with respect to cement replacement of previous studies ([PERSON] [9] and [PERSON] [42]). are shown in Table 7. Four Major factors were considered that have not previously been discussed for modeling purposes regarding SCC. As the target was to explore and predict the mechanical properties and impact of change of curing days, percentage replacement of cement, change in type of additive and elevation in temperature (from room temperature to 400 \({}^{\circ}\)C), this study is a new addition to the existing literature. Previously, only compressive strength was target predictor in research. The Artificial Neural System shown in Figure 6 displays analogies to the path varieties of neuron work in organic learning and memory. The major building pieces are units (\"hubs\") practically identical to neurons, weighted associations that can be compared to neuro transmitters in organic frameworks. Hubs are straightforward data handling components. The number of hubs in ANNs and the association examples of the hubs can differ. The aggregate number of hubs in the information and yield layers concur with the quantity of information and yield factors in the information set.