Analysis of the Current Development Status of the Engineering Cost Industry in the Era of Artificial Intelligence

Jiaying Zhou

doi:10.54691/jg6x0s28

Authors

Jiaying Zhou Liaoning Communication University, Shenyang, 110136, China

DOI:

https://doi.org/10.54691/jg6x0s28

Keywords:

Engineering cost industry, artificial intelligence, cost estimation.

Abstract

Cost estimation is a very important department in construction engineering. Accurate cost estimation can improve the accuracy of the cost budget for construction engineering projects. Traditional cost estimation mainly based on manual estimation methods, which has low accuracy in the budgeting process of engineering projects and easily leads to significant errors in data estimation, resulting in economic losses for construction engineering projects. Application of artificial intelligence technology in construction cost estimation can adopt scientific calculation methods to estimate the cost of construction engineering projects, thereby improving the accuracy of cost estimation for construction engineering projects.

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References

[1] Xu, B. (2016). Research on construction engineering cost estimation based on artificial intelligence technology. Journal of Hunan City University (Natural Science), (04).

[2] Chen, H., Li, P., Liu, G., & Li, H. (2012). Multi-step predictive control of nonlinear systems based on neural model and improved optimal retention genetic algorithm. Journal of Hebei Academy of Sciences, (04).

[3] Niu, D., Qi, J., & Xing, M. (201?). Research on variable structure neural network model for construction engineering cost prediction. Journal of North China Electric Power University, (04).

[4] Wang, S., Yu, H., Tong, J., & Wu, N. (1999). Object-oriented hybrid expert system for concrete pump boom design. Mechanical Design and Manufacturing, (01).

[5] Qu, L. (2014). Strengthening construction management is an effective measure to improve enterprise competitiveness and implement. In Proceedings of the 2014 Construction Technology and Management Academic Exchange Conference [Conference proceeding].