## 2 Literature Review and Cost Estimation Methods
### Cost Estimation Techniques  
We can say that, in the field of cost estimation, many methods and techniques are used, including artificial neural networks (ANNs). These hybrid models use secondary artificial intelligence or meta-heuristic methods, radial functional neural networks (RBFNN), case-based reasoning (CBR), regression analysis (RA), particle swarm optimization (PSO), decision trees, or expert systems. All these are practices dedicated to the operation and implementation of cost estimation systems. Thus, artificial neural networks are part of those numerous algorithms that model all computer learning processes. They manage to create classifiers called machine learning. Specifically, machine learning is a process of computer programming to optimize data processing performance and experiences [15]. [PERSON] and [PERSON] studied the first mathematical models based on an artificial neural network in 1943 [16]. Artificial neural networks are also called non-parametric processing systems that are based on analogy, being inspired by the principles of functionality and structure in the biological neural networks of the human brain. We can say that there is also a problem with the use of neural networks, such as pattern recognition, clustering, forecasting, and the predictability of certain categories [17]. When we talk about forecasting, neural networks are based on training models with past databases, and depending on their capabilities they can make generalizations and further predictions or estimates. According to studies, ANN is data-driven and has lower performance in prediction and estimation, fueling suspicions of over-specification. The application of heuristic methods and rules prevents the model from continuing its training process with observed aspects and in a mixed-use way with the MSE network. Thus, the use of hybrid ANN models with secondary intelligent algorithms or meta-heuristic methods, such as genetic algorithms and bee or ant colony algorithms, has been proposed in many articles to compensate for the disadvantages of ANNs, facilitating the application in practical aspects [18]. Genetic algorithms (GAs) are part of those meta-heuristic methods, but they are also based on some assimilated models from evolutionary computation, which was first in vented in 1959 by [PERSON] [19]. The emergence of problems is found in dynamic environments; they need a response from the environment, which addresses the failure or success of the strategy applied by exploiting all the assimilated data. All this is achieved through recombination strategies, and also the current methods of reproducing the models that are the basis of the calculation [20]. When we talk about CBR, it is more of an ideal data mining technique for retaining information and implementing it in cases of similarity filtering and solving new problems. The RA technique is based on classifiers and is identified as a method oriented to the raw data and less to the features behind the data, being structured on linear and non-linear problems [21]. We can say that a large part of the analysis shows that decision trees are the most used in solving classification problems. The construction is continuously based on features that satisfy the branching rule, a process performed iteratively for each branch. Therefore, decision tree and classification or regression mainly deals with the prediction of a dependent variable based on another predictor variable. This process is based on a response variable and includes a set of values, which in turn contains a continuous or discrete set of variables [22]. These regression trees are a good substitute for basic regression methods. In the field of construction cost estimation, the decision tree is built mainly based on those attributes in the data set that are relevant for the classification process, being the selection of some features [23].