PRELIMINARY QUANTITY ESTIMATION IN CONSTRUCTION USING MACHINE LEARNING METHODS

Abstract

This paper analyses the problem of estimating the required quantities of major work items in the construction of residential and residential-commercial buildings using machine learning algorithms. The goal is to form a model that will provide a fast and sufficiently accurate estimate of the quantities of major work items, based on a small amount of known information on the technical characteristics and the environment of future residential and residential-commercial buildings. The case study included 71 projects of residential and residential-commercial buildings construction realised on the territory of the Republic of Serbia. Several models have been developed, and the paper presents those models that had the best performances. The models developed in this way can significantly contribute to resource planning and the accuracy of cost estimates in the early project phases.