Articlegold2023

Construction cost prediction system based on Random Forest optimized by the Bird Swarm Algorithm

Zhishan Zheng · Lin Yong Zhou · Han Wu · Lihong Zhou

31 citations0/0 files storedMathematical Biosciences & Engineering

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Construction Project Management and Performance Metaheuristic Optimization Algorithms Research Forecasting Techniques and Applications

Abstract

Predicting construction costs often involves disadvantages, such as low prediction accuracy, poor promotion value and unfavorable efficiency, owing to the complex composition of construction projects, a large number of personnel, long working periods and high levels of uncertainty. To address these concerns, a prediction index system and a prediction model were developed. First, the factors influencing construction cost were first identified, a prediction index system including 14 secondary indexes was constructed and the methods of obtaining data were presented elaborately. A prediction model based on the Random Forest (RF) algorithm was then constructed. Bird Swarm Algorithm (BSA) was used to optimize RF parameters and thereby avoid the effect of the random selection of RF parameters on prediction accuracy. Finally, the engineering data of a construction company in Xinyu, China were selected as a case study. The case study showed that the maximum relative error of the proposed model was only 1.24%, which met the requirements of engineering practice. For the selected cases, the minimum prediction index system that met the requirement of prediction accuracy included 11 secondary indexes. Compared with classical metaheuristic optimization algorithms (Particle Swarm Optimization, Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, Differential Evolution and Artificial Fish School), BSA could more quickly determine the optimal combination of calculation parameters, on average. Compared with the classical and latest forecasting methods (Back Propagation Neural Network, Support Vector Machines, Stacked Auto-Encoders and Extreme Learning Machine), the proposed model exhibited higher forecasting accuracy and efficiency. The prediction model proposed in this study could better support the prediction of construction cost, and the prediction results provided a basis for optimizing the cost management of construction projects.

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References

2018A Review on Sample Size Determination for Cronbach’s Alpha Test: A Simple Guide for Researchers 2020Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach 2020Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail 2009Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry 2019Estimation and prediction of construction cost index using neural networks, time series, and regression 2013Better estimation of small sobol' sensitivity indices 2011Time Series Models for Forecasting Construction Costs Using Time Series Indexes 2020Predicting the Volatility of Highway Construction Cost Index Using Long Short-Term Memory

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