## 2 Literature Review
### Cost-Estimating Models for Sewerage Construction Projects  
In the context of the focus of this paper, the most crucial aspects are the attempts to develop models that assist in estimating construction costs for sewerage projects. The early work [19] investigated the application of nonlinear regression for sewer cost modeling. The study focused on estimating the empirical parameters within separable and generalized cost functions. To model the sewer cost function, the applied technique required the optimization of the values of the nonlinear parameters. The two types of analyzed nonlinear parametric cost models are reported to exhibit relative insensitivity to minor errors in the estimated values of their model parameters. The development of cost functions for open-cut and jacking methods in sanitary sewer system construction is the subject of another work [20]. The research resulted in the formulation of cost functions applicable to open-cut and jacking methods, which are construction techniques for sewer systems. These cost functions were derived using linear regression and expressed as functions of pipe size and excavation depth. The derived functions were validated using data from several actual sewer system construction projects to verify the accuracy of cost predictions. Another work [21] presents nonlinear unit cost functions for estimating costs associated with elements of waterborne sewer infrastructure, including gravity pipes, rising mains, pump stations, and wastewater treatment facilities. As a result, a model that combines several cost functions was developed to predict the unit cost of various sewer elements. Modeling the costs related to sewer systems has also been explored in the study [22]. The approach outlined in this research relies on the utilization of multiple linear regression techniques. The authors devised and validated cost functions that pertain to various components of sewer systems, including gravity and rising pipes, manholes, and pumping stations. The costs are delineated as functions of the principal physical attributes of these components. The process of estimating the cost functions involved the application of multiple linear regression analysis. In another work [23], a parametric approach for modeling the construction costs of sewer systems is presented. The authors aimed to establish an initial cost model on a municipal level, with population size serving as the primary variable for the cost functions. By maintaining population size as an independent factor, an empirical correlation has been deduced between population size and the expenses associated with sewerage systems. Various forms of cost functions were experimented with, encompassing both linear and nonlinear formulations. The matter of early cost estimates for sewerage lines is also present in [24]. The authors employed regression analysis to formulate models for predicting early-stage costs. The study devised models grounded in linear regression, featuring the technical attributes of sewerage lines as independent variables and the estimated costs as the dependent variable. It can be observed that several works are based on an approach in which the form of the cost function is assumed ex-ante. Both linear and nonlinear functions have been employed to model the costs of sewer systems; however, linear regression appears to be the most popular tool among researchers. Nonetheless, there are works that present attempts to employ artificial neural networks for the purpose of cost estimates in sewerage projects. In [25], a neural network is utilized as the cornerstone of a cost-estimating model designed for a budget estimation system focused on repair and/or replacement costs of sewer and water projects. The model incorporates 23 project-related factors, derived through Pareto analysis, as input variables (independent variables), while the budget estimate serves as the output (dependent variable).