An Algorithm for Construction Project Cost Forecast Based on Particle Swarm Optimization-Guided BP Neural Network

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.

An integrated DEMATEL and system dynamic model for project cost prediction

“…Cost issues that affect the owner cover the variation of the contract cost from the anticipated cost during the planning phase (cost deviation) and the change in the contract cost at the project's completion (change in contract cost and scope). Various types of research investigated cost deviation or contract cost change by identifying the most critical factors or developing forecast models for them [ [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] ].…”

AEHO: Apriori-Based Optimized Model for Building Construction to Time-Cost Tradeoff Modeling

“…The efficient control and management of construction costs may be assisted by an accurate construction cost estimation, which has the potential to be successfully realized. Ye [31] introduced a unique construction project of a cost prediction system based on a PS-guided Back propagation neural network and improved BPNN using the PSO technique. PSO approach is utilized to enhance BPNN.…”

Design of Engineering Cost Prediction Algorithm Based on Neural Network Model

“…Its accuracy directly affects the scientific nature and investment economic effect of project investment decision, capital construction scale determination, project design scheme formulation, etc. Improving the level of project cost prediction is the premise of reasonably determining the project cost, effectively controlling the construction cost, and realizing the lean project cost management, and is the basis of system planning and decision-making [2][3]. Therefore, how to correctly reflect this nonlinear relationship and predict it easily, quickly and accurately is the key to build a highway engineering cost prediction model.…”