## 2 Literature Review
[21] proposed the AI process known as the evolutionary fuzzy neural inference model (EFNIM). In order to help the EFNIM locate workable solutions in difficult circumstances, the advantageous properties of ANNs, evolutionary algorithms, and fuzzy logic were successfully added to the model. Whenever these approaches were combined, they enhanced the advantages of each approach and somewhat offset any shortcomings that resulted from using them alone. Fuzzy logic (FL) handled risks as well as roughly inferences, genetic algorithms (GA) have been utilized for optimization, and ANNs were implemented for fuzzy input-output visualization. Because of the above, [PERSON] et al. [21] provided two approaches that might be used to determine conceptual building expenses prior to projects even starting. An early-stage building initiative's cost was estimated using an ANN-based model by [PERSON] and [PERSON] [22]. Seventy-one building projects across the Gaza Strip were taken from a database. These included household buildings, schools, preschools, and government or NGO buildings. Key characteristics from architectural plans were identified. These were used to calculate building costs. The parameters included the number of stories, area, basement space, groundwork type, column quantity, elevators, and typical flooring size. An ANN model was developed with these inputs. It approximated building costs with a single output neuron, one hidden layer, and seven input neurons. The model showed that ANNs could estimate early construction costs using basic data. The most important factors were the number of stories, ground level dimensions, groundwork type, and elevator count. [PERSON] et al. [23] presented an intriguing perspective. They innovated by developing models that used support vector machine (SVM) models and ANNs to anticipate construction costs as well as performance utilizing early planning information as parameter inputs. By gathering preliminary scheduling and building feedback from 92 building endeavors via a pertinent business questionnaire research, they found that the initial preparation stage could potentially effectively be employed to forecast successful completion of projects via ANNs. The 92 building initiatives consisted of 80 novel building endeavors and 12 renovations, of which 32 were private and 60 public. It was demonstrated that models constructed with bootstrap-aggregated ANNs proved more reliable and precise than models made from single ANNs. A wide range of both qualitative and quantitative measures were implemented to identify ANN algorithms by [PERSON] and by [PERSON] and [PERSON] [24; 25] in order to obtain cost information for each phase of the building process and precise building construction expenditure estimates. The database consisted of 169 accomplished building initiatives in the region of Gaza. Past projects of building examples from districts, federal offices, engineering education institutions, outside contractors, and specialists served as the source material for the structures. As independent input variables, eleven significant parameters were taken into consideration, and the overall project cost was considered the dependent output parameter. The software called NeuroSolutions was utilized to train the created models. As the mean inaccuracy for the improved model remained overall acceptable, or less than 6%, their ANN models additionally showed that an ANN could accurately calculate building project expenses despite demanding major definite drawings. The sensitivity analysis's findings showed that the number of stories and the typical ground area provided the most effects on building costs. Eleven elements should be considered distinct inputs that impact the overall project price, the researchers concluded. It was firmly held by [PERSON] et al. [26] that a number of unique elements influence cost estimation in construction projects. The factors unique to estimation methods and the variables unique to concepts and projects could be separated into two distinct groups.