Modeling labor productivity in high-rise building construction projects using neural networks

• Autorzy: Nguyen Duc Anh , Tran Dung Quang , Nguyen Thoan Ngoc , Tran Hai Hong

Identyfikatory

DOI

10.24425/ace.2023.144195

• Języki publikacji: EN

Abstrakty

EN

Labor productivity in building construction has long been a focused research topic due to the high contribution of labor cost in the building total costs. This study, among a few studies that used scaled data that were collected directly from measuring equipment and onsite activities, utilized neural networks to model the productivity of two main construction tasks and influencing factors. The neural networks show their ability to predict the behaviors of labor productivity of the formwork and rebar tasks in a test case of a high-rise building. A multilayer perceptron that had two layers and used sigmoid as its activation function provided the best effectiveness in predicting the relations among data. Among eleven independent factors, weather (e.g., temperature, precipitation, sun) generally played the most important role while crew factors were distributed in the mid of the ranking and the site factor (working floor height) played a mild role. This study confirms the robustness of neural networks in productivity research problems and the importance of working environments to labor productivity in building construction. Managerial implications, including careful environmental factors and crew structure deliberation, evolved from the study when labor productivity improvement is considered.

Słowa kluczowe

EN

construction project; high-rise building; labor productivity; modelling; neural network

PL

projekt budowlany; budynek wysokościowy; produktywność pracy; modelowanie; sieć neuronowa

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2023
• Tom: Vol. 69, nr 1
• Strony: 675--692
• Opis fizyczny: Bibliogr. 47 poz., il., tab.

Twórcy

autor

Nguyen Duc Anh

• Hanoi University of Civil Engineering, Department of Building and Industrial Construction, Hanoi, Vietnam

• https://orcid.org/0000-0001-5886-8310

autor

Tran Dung Quang

• Hanoi University of Civil Engineering, Department of Building and Industrial Construction, Hanoi, Vietnam

• https://orcid.org/0000-0003-3928-0962

autor

Nguyen Thoan Ngoc

• Hanoi University of Civil Engineering, Department of Building and Industrial Construction, Hanoi, Vietnam

• https://orcid.org/0000-0002-5114-4130

autor

Tran Hai Hong

• Hanoi University of Civil Engineering, Department of Building and Industrial Construction, Hanoi, Vietnam

• https://orcid.org/0000-0001-7766-5121

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