Construction engineering cost estimation based on artificial intelligence technology

Abstract

The determination of construction project cost is one of the important contents of construction project management, but the estimation of construction project cost generally has the disadvantages of large errors and long preparation time. With the continuous development of computer science, artificial intelligence theory is one of the hot research topics. The purpose of this article is to study the construction cost estimation based on artificial intelligence technology. Based on the theoretical basis of artificial neural network, genetic algorithm, and engineering cost, this paper proposes an optimized radial basic function (RBF) model based on genetic algorithm (GA). The search feature combines the width, center, and hidden layer weights of the RBF network with genetic algorithms to self-correct, thereby greatly improving the accuracy of the model calculation results. In this paper, according to the model’s error (actual output-expected output), the four test samples were tested separately, and the error values obtained were 0.0125, 0.1009, –0.0791, and 0.0514. This shows the accuracy of the experimental results of the model [R] higher.