Abstract
Construction project cost prediction is an important function in construction-related fields; it can provide an important basis for project feasibility study and design scheme comparison and selection, and its accuracy will directly affect the investment decision of the project. The successful realization of construction cost prediction can bring great convenience to the control and management of construction cost. The purpose of this paper is to study a fast, accurate, convenient, deducible, and rational construction project cost prediction method, to provide a basis for the cost management of the whole life cycle of the project. Therefore, this paper uses particle swarm optimization algorithm to improve BP neural network and proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network. Aiming at the defects of BP neural network updating weights and thresholds with the gradient descent method, this paper uses the advantages of particle swarm optimization in the field of parameter optimization to optimize BP neural network with PSO algorithm. The structure of BP neural network weights and the threshold of each neuron in the coding, through intelligent search for each particle, find the most suitable weights and thresholds, so that the BP neural network has faster convergence speed, better generalization ability, and higher prediction precision. Simulation results also show that the proposed algorithm is competitive enough.
Highlights
Construction project management [1,2,3] primarily consists of preliminary investment estimation [4, 5], plan design expansion design [6], construction drawing design [7], stage design budget, project budget in the bidding stage, project settlement, and project final accounts after completion, among other things. e investment estimation of construction costs [8,9,10] is the focus of construction project management. e profitability of a project is determined by the investment estimate of the construction cost [11]. e cost of construction and installation works, or the cost of construction works, plays a significant role in estimating the investment value of construction projects
E main contributions of this article are as follows: (1) is paper proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network, which can predict the construction project cost more accurately and provide a basis for the cost management of the whole life cycle of the project
Many domestic and foreign experts and scholars have proposed a new method of construction project cost prediction in order to achieve accurate and rapid construction cost forecasting, that is, forecasting based on traditional statistical analysis methods
Summary
Construction project management [1,2,3] primarily consists of preliminary investment estimation [4, 5], plan design expansion design [6], construction drawing design [7], stage design budget, project budget in the bidding stage, project settlement, and project final accounts after completion, among other things. e investment estimation of construction costs [8,9,10] is the focus of construction project management. e profitability of a project is determined by the investment estimate of the construction cost [11]. e cost of construction and installation works, or the cost of construction works, plays a significant role in estimating the investment value of construction projects. We discovered through extensive scientific research that the traditional construction engineering cost estimation method has several flaws, including low calculation accuracy and long calculation times. E realization of high-precision cost forecasting through mathematical modeling has piqued the interest of industry professionals and academics [21, 22], thanks to the rapid advancement of computer and neural network technologies [23,24,25]. Erefore, this paper intends to use the combination of particle swarm optimization algorithm and BP neural network to quickly predict the project cost. (1) is paper proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network, which can predict the construction project cost more accurately and provide a basis for the cost management of the whole life cycle of the project.
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