Development of Artificial Neural Networks for Predicting the Construction Costs of WWTPs in Greece

The construction cost estimation process is crucial for Wastewater Treatment Plants (WWTPs). It is considered an integral aspect of the broader project planning process and is influenced by the environmental considerations and engineering decisions made during the design phase of a WWTP. The rapid and accurate estimation of it poses a challenge for engineers. The application of artificial intelligence (AI) in recent years has made significant strides and is used for predicting project attributes. This work investigates the application of machine learning (ML) techniques, specifically artificial neural networks (ANNs), for predicting the construction costs of WWTPs in the Greek context. This research aims to demonstrate the effectiveness of ML, particularly ANNs, in predicting construction costs for WWTPs in Greece.