Author(s)

Jinshuo Zhang ¹

Affiliation(s)

¹ Washington University of St.Louis, Saint Louis, Missouri, 63105, USA

Corresponding Author

Jinshuo Zhang

ABSTRACT

In this paper, an innovative methodology based on data-driven and machine learning algorithm is constructed for optimization and decision support in highway engineering design. With the rapid development of big data and intelligent technology, the traditional engineering design model is gradually being replaced by data analysis and intelligent algorithms, which significantly improves the efficiency and accuracy of engineering solutions. Based on the research of Xuanda expressway electromechanical engineering, this paper deeply analyzes the key bottlenecks and deficiencies in the current design mode, and puts forward a series of improvement strategies, such as optimizing the monitoring system, improving the CCTV layout accuracy and refining the construction drawing design. By combining machine learning techniques, this paper shows how data-driven models can be used to aid decision making, making design solutions not only more intelligent, but also more flexible and adaptable. This study provides a new idea for highway engineering design and lays a theoretical foundation for promoting the further development of intelligent transportation infrastructure.

KEYWORDS

Machine learning algorithm; Highway engineering; data-driven

CITE THIS PAPER

Jinshuo Zhang, Optimization of Highway Engineering Design and Data-Driven Decision Support Based on Machine Learning Algorithm. Journal of Network Computing and Applications (2025) Vol. 10: 8-13. DOI: http://dx.doi.org/10.23977/jnca.2025.100102.
 

REFERENCES

[1] Yang Z, Liu Y, Hu L Y. Optimization of Drilling Parameters of Target Wells Based on Machine Learning and Data Analysis[J]. Arabian journal for science and engineering, 2023, 48(7): 9069-9084.
[2] Yang K, Deng X, Ti Z, et al. A data-driven layout optimization framework of large-scale wind farms based on machine learning [J]. Renewable energy, 2023, (12): 218.
[3] Yang Z, Liu Y, Qin X, et al. Optimization of Drilling Parameters of Target Wells Based on Machine Learning and Data Analysis[J]. Arabian Journal for Science and Engineering, 2022, 48(7): 9069-9084. DOI: 10.1007/s 13369- 022- 07103-x. 
[4] Zhao X, Fan H, Lin Gao, Bo Fang, Zhecheng Yang, Wulong Li, Mian Wang, Jianghao Lu, Xiuyang Li, Bolong Wu, Kejun Fu. Multi-objective optimization of radially stirred tank based on CFD and machine learning [J]. AIChE Journal, 2024, 70(3):18324-18326. DOI:10.1002/aic.18324.

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