Prefabricated building cost prediction model combined with BIM and deep learning

Yaoyao Zhang

doi:10.1117/12.2685915

28 July 2023 Prefabricated building cost prediction model combined with BIM and deep learning

Yaoyao Zhang

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127564A (2023) https://doi.org/10.1117/12.2685915
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

Since the COVID-19 pandemic spread around the world at the beginning of 2020, the construction industry at home and abroad is facing challenges such as shortage of resources and labor, which provides a new opportunity for the development of prefabricated buildings. This, coupled with the environmental pollution and resource shortage caused by the extensive development of China's construction industry in recent thousands of years, makes the traditional construction industry in urgent need of new construction technology transformation to achieve sustainable development. This paper first analyzes the construction cost by combining BIM and multiple linear regression, and then builds a prediction model of prefabricated construction cost based on LSTM. Specifically, the model uses BIM technology to obtain more accurate and comprehensive architectural design and construction data. Secondly, the model combines multiple linear regression method in the process of architectural design and construction, which can comprehensively and accurately analyze and process data; Finally, the cost prediction model based on LSTM can effectively predict the total cost of construction projects and provide valuable reference and guidance information.

Citation Download Citation

Yaoyao Zhang "Prefabricated building cost prediction model combined with BIM and deep learning", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127564A (28 July 2023); https://doi.org/10.1117/12.2685915

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KEYWORDS

Design and modelling

Data modeling

Industry

Education and training

Instrument modeling

Deep learning

Engineering

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