## 2 Literature Review
Once an illustrative non-exhaustive content evaluation was conducted on the research projects that were determined to be able to generate cost of construction predictions over buildings utilizing ANNs, the subsequent research findings were identified as distinguished. It is well acknowledged that construction companies utilize cost estimation mostly to determine the price to assess for estimates of quantities that are created using a standard technique of measurement. According to [PERSON] et al. [17], it is critical for developers, suppliers, and stakeholders to recognize and comprehend particular traits that may be crucial to a building construction's ultimate completion. In an attempt to identify the critical managerial characteristics associated with successful financial efficiency, they then used an ANN approach. They recommended several of factors, such as the quantity of organizational levels between the project leader and the builders, the quantity of thorough planning finished before building, the extent of managerial discussions during constructing, the rate of funds revisions, the implementation of a constructability plan, group employee turnover, the amount of income allocated for project leadership, and the supervisor's expertise in the field. Forty-eight building initiatives from builders and twenty-seven from shareholder organizations were included in [PERSON] et al. 's study [17]. Upon completion of training, the finished model was applied as a predictor for estimating the manner in which a building cost will function. Even if the functional connection amongst the inputs and their results are not clearly defined, a financial performance model continues to be constructed through to this methodology [17]. Using data from 300 structures, [PERSON] et al. [18] employed ANNs to forecast the overall cost of construction. The data from building costs, construction documents, and the outcomes of a widely circulated interview questionnaire comprised the information that was gathered. The ultimate sum totals in the data allowed for the creation of a model that could evaluate the customer's overall expenses, which included both internal and external expenses in addition to building expenditures. The ANN approach's ability to simulate nonlinearity in the data was found to be its main advantage, as they used linear regression (LR) techniques to construct additional models to evaluate the models built using ANN. With an uncertain degree of accounting for customer modifications, the best model generated a mean absolute percentage error (MAPE) of 16.6%. The standard estimating method, which finds that the MAPE results fluctuate between 20.8% to 27.9%, differs sharply via findings. [PERSON] and [PERSON]\(\hbar\)an [19] investigated the effectiveness of ANN techniques in resolving cost estimating problems in the early phases of building planning processes. Utilizing cost and architectural information coming from 30 projects, an ANN-based simulation that included eight design factors was created and validated to determine the square meter cost of a building's concrete reinforcement support systems for four- to eight-story apartment buildings in Turkey. The model achieved a 93% accuracy rate. In addition, [PERSON] et al. [20] evaluated into the effectiveness of three cost estimation methods in an effort to show how important realistic cost forecasting could be for building tasks. They employed ANNs, multiple regression analysis (MRA), and case-based reasoning (CBR) utilizing past cost information for 530 structures. Construction companies in Seoul, Korea, built the household structures between 1997 and 2000. Although the most effective ANN forecasting model produced more precise forecasting outcomes compared to either the MRA as well as the CBR forecasting versions, the CBR estimating structure accomplished more in terms of lengthy utilization, time in addition to accuracy decisions, and available data derived from outcomes. To improve the precision of cost estimation, [PERSON] et al.