Welcome to Francis Academic Press

Academic Journal of Architecture and Geotechnical Engineering, 2023, 5(1); doi: 10.25236/AJAGE.2023.050107.

Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm


Yin Bai

Corresponding Author:
Yin Bai

Liaoning Institute of Science and Technology, Benxi, 117004, China


Civil engineering includes not only all engineering construction on the ground, but also the maintenance, exploration and design of equipment and materials used in the whole construction process. During the construction of civil engineering, there is no scientific and reasonable supervision system for the supervision of construction, which greatly affects the quality of engineering and the control and management of engineering cost. In this paper, through the application of DT (Decision tree) algorithm, the research of civil engineering cost prediction is carried out. Aiming at the application of the algorithm in civil engineering cost management, this paper tries to improve the C4.5 algorithm. DT civil engineering life-cycle cost analysis and prediction model is trained by training set, and the optimal result of civil engineering life-cycle cost analysis is obtained by inputting sample data for model prediction. The application results show that the above algorithm has lower computational complexity and higher prediction efficiency in civil engineering cost prediction, which can better meet the needs of actual civil engineering cost analysis and prediction.


Decision tree; Civil engineering; Cost prediction

Cite This Paper

Yin Bai. Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm. Academic Journal of Architecture and Geotechnical Engineering (2023) Vol. 5, Issue 1: 39-44. https://doi.org/10.25236/AJAGE.2023.050107.


[1] Zhang Xiaobo. (2022). Research on the Application of Engineering Cost in Civil Engineering. Engineering Seismic Resistance and Reinforcement, 2022(003), 044.

[2] Zhang Yongcheng, Guo Shuai, &Ye Yanbing. (2020). Engineering cost data information service system from the perspective of big data. Journal of Civil Engineering and Management, 37(1), 6.

[3] Hu Danping, &Tao Xueming. (2018). Improved design of cost model of post-earthquake reconstruction project based on improved genetic algorithm. Journal of Earthquake Engineering, 40(4), 6.

[4] Wang Xinyue, Zeng Hui, &Liu Tongfei. (2021). Simulation Research on Dispute Resolution Factors of Construction Cost Based on netlogo. Construction Economy, 042(005), 113-116.

[5] Jiang Hongyan, &Bai Yuqing. (2019). Cost estimation of high-rise housing based on grey correlation pso-bp neural network. Journal of Engineering Management, 33(1), 5.

[6] Xu Bing,&Yao Junyi. (2019). Design and Implementation of Price Adjustment Formula Platform for Construction Engineering. Construction Economy, 40(2), 5.

[7] Wang, Y., Xia, S. T., & Wu, J. (2017). A less-greedy two-term tsallis entropy information metric approach for decision tree classification. Knowledge-Based Systems, 120(15), 34-42.

[8] Xu, H., Wang, L., & Gan, W. (2016). Application of improved decision tree method based on rough set in building smart medical analysis crm system. International Journal of Smart Home, 10(1), 251-266.

[9] Chen Dachuan, Yu Yi,&Liu Yuelong. (2021). Construction of construction project cost standard system based on system engineering. Journal of Civil Engineering and Management, 38(6), 6.

[10] Julia, Chen Fei, & Jing Liu. (2016). Engineering cost prediction model based on normalized network and generalized network. Practice and understanding of mathematics, 2016(7), 6.