Research on cost prediction for construction project based on Boruta-SO-BP model

• Autorzy: Cui Hao , Xia Junjie

Identyfikatory

DOI

10.24425/ace.2023.147667

• Języki publikacji: EN

Abstrakty

EN

Cost prediction for construction projects provides important information for project feasibility studies and design scheme selection. To improve the accuracy of early-stage cost estimation for construction projects, an improved neural network prediction model was proposed based on BP (back propagation) neural network and Snake Optimizer algorithm (SO). SO algorithm is adopted to optimize the initial weights and thresholds of the BP neural network. Cost data for 50 construction projects undertaken by Shandong Tianqi Real Estate Group in China was collected, and the data samples were clustered into three categories using cluster analysis. 18 engineering feature indicators were determined through a literature review and 10 feature indicators were selected using Boruta algorithm for the input set. Compared to BP neural network and PSO-BP neural network, the results show that the improved SO-BP model has higher prediction accuracy, stability, better generalization ability and applicability. Therefore, based on reasonable feature indicators, the method proposed in this paper has certain guiding significance for predicting engineering costs.

Słowa kluczowe

EN

Boruta; BP neural network; intelligent prediction; project cost; snake optimizer; algorithm

PL

Boruta; sieć neuronowa BP; przewidywanie inteligentne; koszt projektu; algorytm optymalizacji; optymalizator snake

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2023
• Tom: Vol. 69, nr 4
• Strony: 413--429
• Opis fizyczny: Bibliogr. 25 poz., il., tab.

Twórcy

autor

Cui Hao

• College of Civil Engineering, Jiangxi Science and Technology Normal University, Nanchang, China

• https://orcid.org/0000-0002-8292-9555

autor

Xia Junjie

• College of Civil Engineering, Jiangxi Science and Technology Normal University, Nanchang, China

• https://orcid.org/0000-0002-5993-6474

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).