## 2 Literature Review
### Cost-Estimating Models for Sewerage Construction Projects
The authors emphasize that the proposed model not only saves time but also enhances the precision of estimates, offering clients a means to compare cost alternatives and facilitating decision-making processes in cases involving the rehabilitation of sewer and water systems. Similar work [26] deals with the problems of conceptual cost estimating for water supply and sewerage projects. This paper presents a backpropagation neural network-based model that is supposed to assist municipal authorities in the development of more accurate cost estimates for their water supply and sewer projects. Cost predictors, representing project technical parameters and serving as the model's input, were identified on the basis of contractors' bids analysis and covered 80% of construction work costs. The benefits of the presented model include but are not limited to, better utilization of financial resources, the provision of decision-making guidelines, and the ability to compare alternatives. Additionally, the authors claim that the model fulfills the needs of funding entities for more accurate cost estimates. In comparison to the parametric approach, modeling costs using neural networks eliminates the need for assumptions about the equation that binds the independent variables of the models to the cost, which serves as the dependent variable. Applications of Neural Networks and Ensembles of Neural Networks for Construction Management Problems and Cost Estimation in Construction  
Artificial neural networks (ANN) are a subset of artificial intelligence tools inspired by the learning and knowledge storage patterns observed in neurobiology. They can be employed to address various classification or regression challenges. The concept and theory of neural networks have been extensively discussed in numerous works [27; 28; 29; 30]. Neural networks possess the capacity to process data with the aim of uncovering concealed patterns. The procedure of data processing to acquire knowledge, referred to as training, is executed through specific algorithms. Following the training phase, these networks are anticipated to possess the ability to generate predictions for novel data that were not utilized in the training process. The ability to generalize knowledge is a key attribute of artificial neural networks, rendering them valuable for a range of engineering problems. Particularly, ANN has found application in addressing cost-related challenges within the construction sector. An exemplary illustration of this is a fuzzy neural network model aimed at aiding contractors in estimating and selecting a suitable markup [31]. Investigative efforts in other work [32] centered on the evaluation of multilayer perceptron and general regression neural networks for their potential in early cost estimation for road tunnel projects. In [33], the outcomes from the utilization of general regression neural networks to predict maintenance costs associated with construction equipment are shared. Another work [34] delved into the utilization of multilayer perceptron neural networks to estimate building construction costs during the initial design phase. In [35], research results on optimizing both the cost and timeline of construction projects through the implementation of neural networks are introduced. A hybrid approach, combining multivariate regression and multilayer perceptron neural networks, was employed in another research [36] to estimate capital costs for earthmoving, loading, and unloading equipment. There are also some interesting works that explore the utilization of ANN and machine learning in the analysis of wind speed and wind direction [37; 38], settlement prediction [39], and the assessment of their impact on existing structures, that is, bridges and metro, respectively. Ensembles (also called committees) of neural networks (EoNN) have their origins in the realm of ensemble learning systems.