## 6 Discussion, Conclusions, and Future Research  
The primary allure of utilizing neural networks is their potential to autonomously generate information via training data, devoid of preconceived notions. Finding every relevant component that serves as the logical foundation for choices in cost estimating, nevertheless, is challenging and demanding on time. One sector that is particularly vulnerable to changes in the global economy is the building sector, where cyclical economic conditions are common. While the developers are currently in an era of low interest rates and minimal price inflation, data on costs might have been collected during instances of significant interest rates with elevated inflation in previous years, which would be necessary to construct a useful training database. Undoubtedly, that could influence the high accuracy on the cost modelling that rely on neural networks. Converging with [PERSON]'s [41] perspective future research efforts should attempt to create a method for normalizing and softening data used as training to ensure variables like alterations in the economy might be taken into consideration. Because project costs and unpredicfi engineering parameters have nonlinear correlations and interactions, it is advised that practitioners adopt ANNs. The training process allows for deficiencies in the ANN due to its high precision and capacity to learn complex functions. They speed up and increase the evaluation's reliability. The proposed models are in summary accurate, easy to use, and time-saving. An appropriate method for addressing problems arising from multiple uncertainties, such as early cost estimation, may be the ANN models used above. Therefore, the findings support the need for more study on project management that incorporates a hybrid RBF-MLP model, or fuzzy logic and other cutting-edge methodologies. In the present research, the ANNs utilizing RBFs and MLPs incorporate known input variables that have a noteworthy influence on building renovation costs. This study also demonstrated how these factors are utilized to calculate the building costs of civic projects in Greece. Consequently, professionals ought to think about using these factors when evaluating the total cost of building renovation. The results in Section 5 illustrate an adequate coinciding with the actual cost in the training and testing sample and the predicted final cost determined using the RBF and MLP methods. The real expenses of the chosen projects varied significantly. For this reason, the ANNs found it very challenging to build predicting models that could also have a usage for an ex-post verification of building renovation total final cost. In contrast to the MLP approach, which yields less accurate results, the RBF method makes predictions that simulate the real cost. As [PERSON] et al. also concluded, the estimated costs derived from RBF showed a 0.28% variance in excess of the real costs, but the estimated costs derived from MLP showed a 1.11% variance in excess of the actual values [55]. This emphasizes the necessity to constantly enhance artificial neural network techniques in order to increase their precision and usefulness in the field of building reha  
Figure 13: Comparison of actual final cost and estimated final cost with RBF and MLP (testing sample). bilitation cost prediction and verification. There is an enormous amount of a forum for improvement, as seen by the present limits found in the predictive models. To better handle the unpredictability and complexity of real-world data, these procedures could be improved by adding advanced algorithms, integrating new data sources, and using advanced methods. Rendering this modification could strengthen and bolster the prediction models, allowing them to be more accurate in estimating costs. Ensuring that the projected costs accurately resemble the actual end costs would result from this, as well as improving the models' ex-post verification efficacy and renovation expense predictions.