Economic Sustainability of Building and Construction Projects Based on Artificial Intelligence Techniques

Abstract

Artificial intelligence (AI) has been shown to be an effective replacement for conventional modelling approaches. AI is a subfield of computer science that develops software and tools that mimic human intelligence. AI offers advantages over traditional methods for handling ambiguous circumstances. In addition, AI-based solutions can successfully replace testing when identifying engineering design parameters, saving a lot of time and resources. AI can also increase computer efficiency, decrease mistake rates, and speed up decision-making. Recently, there has been a lot of interest in machine learning (ML), a new area of cutting-edge intelligent methods for use in structural engineering. Consequently, this work presents a study on the economic management of building and construction projects based on creating ML techniques. It begins with an overview of the value of applying AI techniques in building and construction industry. The analysis of the prediction of reinforced concrete’s compressive strength while taking cost into account is then done using empirical data based on a case study. Accordingly, the findings showed that the support vector regression (SVR) and k-Nearest Neighbour (KNN) intelligence techniques are helpful in the construction business for controlling the strength of concrete based on sustainable cost reduction.