## 2 Literature Review
They centered on the necessity of developing a cost forecast methodology that could take into consideration all predicting elements from every standpoint and included a standard validation technique that might have been utilized to assess the level of precision of cost prediction proposition. In order to predict the total architectural cost of building projects in the Philippines, [PERSON] and [PERSON] [27] also used an ANN model. Thirty building projects' worth of data were collected and divided into three categories: sixty percent for training, twenty percent for performance verification, and twenty percent for an entirely independent test of network generalization. They included six independent variables in their ANN model, which was carried out in MATLAB for simulation: the overall ground region, the number of floors and basements, the total cement, the size of the structure, and the whole weight of the reinforcing steel. Applying the feedforward backpropagation method, the optimal model for the total building cost was developed. The amount of surface area, quantity of the flooring and foundations, amount of concrete, size of structure, thickness of reinforcing steel, post-tensioned sector, number of piles, etc., are some of the factors that the scientists proposed affect structural or civil engineering costs. However, additional building costs involve architectural fees, which vary according to the type and quality of supplies chosen for the exterior walls, ceilings, windows and doorways, flooring, the painting process, and other architectural elements. In conclusion, the building's entire construction cost is completed by the setting up of elevators, electricals, climate control, heating infrastructure, and wastewater and water supply networks. In a determination to use ANNs for building project cost planning, [PERSON] and [PERSON] [28] looked into and addressed problems in cost estimation at the very beginning of building planning during the preliminary design stage. For upgraded buildings made of concrete, a graphical user interface (GUI) cost prediction model was also developed and validated during the early design phase. As early as the engineering process, [PERSON] and [PERSON] [28] demonstrated that the ANN GUI model might be useful for managers in advising on the project's completion. Utilizing the first estimations of the costs of 25 construction factors, [PERSON] and [PERSON] [29] created an ANN-based mathematical model to calculate the total price of building development. They collected data from 501 building endeavors in Iraq that were completed among 2005 and 2015. The data contained the entire amount invested in different kinds of construction, such as excavation of foundations, filling landfills, filling them with subbase, creating moisture-proof layers, elements of construction, regular concrete for boundaries, building foundations, etc. With respect to the estimation of construction project costs in Iraq, the ANN algorithm demonstrated exceptional performance, as evidenced by the correlation values between the variable results of almost 100%, the error rate of roughly 5.81%, and a level of accuracy that was 94.19%. An ANN approach to forecast building construction costs was also developed by Indian researchers. In particular, using survey questionnaires and the guidance of building developers and experts, [PERSON] and [PERSON] [30] gathered the amount and cost information from 78 buildings, comprising cottages and smaller and mid-sized homes developed near and inside Mumbai, India, between 2017 and 2019. The following 11 independent variables were linked to the quantities of particular construction functions: the number of levels, the standard surface area, the elevator wall size, the wall's exterior size, the external plaster amount, the amount of floor area, the quantity of columns, the foundation's category, and the number of homes. The entire project's total expenditure was the sole outcome parameter.