## 2 Literature Review
They utilized a backpropagation approach to build a multilayer feedforward artificial neural network for their model. To increase the ANN's generalization performance and avoid overfitting, early ending and Bayesian regularization procedures were applied. It was shown that early stopping during the building cost forecast was not as effective as the Bayesian regularization process. The results showed that the trained ANN model could accurately predict the overall cost of building construction [30]. With the help of historical information from 136 structures built between 2011 and 2015, a study was conducted in Yemen, another growing economy, to present a cost forecasting tool using ANN models via [PERSON] and [PERSON] [31]. To provide an initial estimate of the entire building expense, they incorporated 17 independent factors into their model. The project's classification, number of stories, length of floors, kind of foundation, number of lifts, outside finishing structure, decorating style, air conditioning category, HVAC, bottom floor, flooring height, foundation type, location region, tile kind, electrical and mechanical installations, and project's address were the 17 independent factors. Using the NeuroSolutions 6 tool, they designed, trained, tested, and conducted sensitivity assessments on the building. The results of the evaluation, sensitivity analysis, and education were quite satisfactory, having less than 1% inaccuracy and good effectiveness and validity. The cost predicted model that was determined to be specific to public buildings was offered by [PERSON] et al. [32]. They indicated that there are two commonly used methods for determining the expenditures for publicly owned buildings: a thorough prediction that has the benefits of a more accurate cost approximation, yet negative consequences concerning the demand for a definitive successful completion and a related demand for an extended period to complete, and consequently being absent of set financing deadlines. The study employed data from 50 governmental buildings that were finished in Thailand in 2020. There were eleven independent variables that were considered: total space usable, mean perimeter size, mean story height, entire height of the construction, the number of floors, total area under roofing, total area in bathrooms, ground surface slab, entire area of apertures, the kind of roof, and kind of slab construction. Utilizing a model with two hidden layers of ten and eight nodes, the artificial neural network (ANN) approach was employed to forecast the outcomes. With a correlation coefficient (R\({}^{2}\)) of 0.914 and a root mean square error (RMSE) of 0.331, the model's accuracy was demonstrated. For estimating building costs, lowering tolerances, and minimizing effort, this approach provides a safe option for public tendering. Building Cost Estimation--The Present State of Research: The Special Issue of Building  
Renovation Cost Estimation  
The issue of cost estimation in the construction sector has been extensively studied over the past few decades by many researchers from a variety of professions [16; 18; 20; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43] who designed and developed a variety of models. One considerable gap involves the fact that neither of the previously mentioned studies address cost estimation in building renovation projects during the predesign process [10; 44]. Furthermore, as long that it is aware, the accuracy of the final cost after completing a building renovation project has also not been confirmed. This testing is essential to verify the model's accuracy and prevent future forecasting errors. The comparative evaluation of two ANN methods, radial basis function and multi-layer perceptron, for prediction is remarkable. Specifically, the evaluation of outcomes, using initial and final costs as independent variables, is interesting as it resulted in the following paragraphs.