## 2 Literature Review  
In the twentieth century, there has been a dramatic increase in academics' interest in utilizing ANNs for estimation. ANNs have a lot of potential, but there are a lot of unknowns as well, according to [PERSON] et al. [9] who held this belief even in 1998. It is still unknown to researchers what significant components mean in terms of ANN prediction efficacy. However, as the subsequent paragraphs will explain, ANNs have been utilized extensively in construction research [10]. [PERSON] [11] attempted to demonstrate multiple uses for ANNs. The ANNs focused on managing, engineering for construction, and building engineering categories. The simple backpropagation technique represents the foundation for an increasing amount of ANN usages in building engineering. The current research has primarily concentrated on methods to integrate ANN into various other approaches of programming, including fuzzy logic, evolutionary algorithms, and wavelet. The combinations that result improved the effectiveness of models based on ANN. In contrast to traditional causality and effect theories, [PERSON] [12] claims that ANNs represent multifarious, complicated, and dynamic phenomena that are unanticipated and uncontrollable. It is likely that in their basis, they are nonlinear. Whenever linear-based methodologies apply to planned interventions involving nonlinear dynamics, they claim, it is conceivable to employ ineffective approaches to intervention and make incorrect conclusions regarding what has transpired [12]. ANNs are considered indispensable in the building industry. Time series, classification, and forecasting are the three types of challenges that ANNs have already been used to solve. The ANN needs to be trained in order this process to occur. Considering that the qualities of ANNs are not dependent on a formal development procedure, there is a well-established technique. An ANN's typical form is a multi-layer perceptron. Several instances have demonstrated their flexibility [13]. A type of supervising ANN called radial basis function structures is significant because it utilizes guided machine learning (ML) to operate as a nonlinear categorization method. A more thorough evaluation is performed by nonlinear approaches to classification as by basic techniques by using complicated equations. The primary reasons for inaccurate cost estimation remain to be the following: inadequate time to create cost estimates, inadequate paperwork for tenders, and a large range of pricing from contractors. These factors have been related to the estimating function's inadequate practical knowledge about the process of building [14]. Cost estimate is usually a multidisciplinary process that involves feedback from the design team and is based on previous information. The category of project will influence the cost estimation, which is linked to evolving elements of construction and expertise [14]. Advances in models for predictions have been made possible by the combination of fundamental computational methods including fuzzy logic (FL), ANNs, and evolutionary algorithms (EAs). Different hybrid systems have been developed as a result of such comprehensive simulations, which allow the shortcomings of one approach to be offset by a variety of techniques. It is dependent on the software sector and the unique characteristics of the data available to decide which structure is best and provides accurate information for cost estimation [2]. In fact, a very prevalent category of construction projects, for which there are numerous cost prediction models, remains building. This was discovered in that 40 of the 92 papers that were examined made reference to buildings in the relevant review of literature that [PERSON] et al. [15] collected. Furthermore, [PERSON] et al. [16] discovered in their research that 31 further articles out of 51 that were published in 2021 and 2022 included models for estimating construction costs for building projects. Eleven studies out of the thirty-one included ANNs in the models they developed.