Author(s)

Tianyu Yang ¹

Affiliation(s)

¹ Northeastern University, Shenyang, Liaoning, China

Corresponding Author

Tianyu Yang

ABSTRACT

This paper focuses on the innovative application of big data technology in the field of engineering project resource allocation, deeply analyzing its core functional mechanisms in resource evaluation and dynamic optimization. By constructing an evaluation index system for resource allocation that integrates multi-source data, and combining data mining, machine learning, and deep learning algorithms, an intelligent dynamic optimization model for resource allocation is established. Taking a super-high-rise commercial complex construction project as a typical case, this paper details the full-process practice of big data technology from data collection and analysis to optimization decision-making, and quantitatively analyzes its significant effects in improving resource utilization efficiency, reducing project costs, and ensuring construction progress. The study shows that big data technology can provide scientific and precise decision-making basis for engineering project resource allocation, strongly promote the transformation of engineering project management towards intelligence and refinement, and provide new technical paths and practical references for industry development.

KEYWORDS

Big Data Technology; Engineering Projects; Resource Allocation; Evaluation; Dynamic Optimization

CITE THIS PAPER

Tianyu Yang, Evaluation and Dynamic Optimization of Big Data Technology in Engineering Project Resource Allocation. Journal of Electronics and Information Science (2025) Vol. 10: 64-73. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100208.

REFERENCES

[1] Wang, G., et al. (2024). Dynamic Resource Optimization in Smart Construction Projects Using Hybrid Deep Learning and Swarm Intelligence Algorithms. Automation in Construction, 158, 104023.
[2] Liu, Y., et al. (2023). Big Data-Driven Evaluation System for Resource Allocation Efficiency in Complex Engineering Projects. Journal of Construction Engineering and Management, 149 (7), 04023036.
[3] Zhang, H., et al. (2024). A Blockchain-Enabled Big Data Framework for Trustworthy Resource Allocation in Construction Projects. Computers & Industrial Engineering, 189, 108976.
[4] Li, X., et al. (2023). Real-time Resource Scheduling in Prefabricated Construction Based on IoT-Enabled Big Data Analytics. Journal of Management in Engineering, 39 (3), 04023012.
[5] Zhao, M., et al. (2024). Hybrid Deep Learning Model for Resource Demand Forecasting in Engineering Projects Using Multi-source Big Data. IEEE Transactions on Engineering Management, 71 (2), 1568-1580.
[6] Sun, J., et al. (2023). Dynamic Optimization of Construction Resources under Uncertainty: A Big Data-Driven Scenario Planning Approach. International Journal of Project Management, 41 (5), 1025-1038.
[7] Wu, S., et al. (2024). Big Data Visualization and Interactive Optimization for Construction Resource Allocation: A Case Study of Super-high-rise Projects. Journal of Computing in Civil Engineering, 38 (2), 04023045.
[8] Zheng, L., et al. (2023). Multi-objective Resource Allocation in Engineering Projects Using Big Data and Improved Genetic Algorithms: A Case of Commercial Complex Construction. Construction Innovation, 23 (3), 678-695.
[9] Chen, T., et al. (2024). Smart Resource Management in Construction: A Big Data Analytics Framework for Real-time Allocation and Optimization. Procedia Engineering, 392, 123-132.
[10] Chen, S., et al. (2023). A Real-time Resource Allocation Model for Construction Projects Based on Edge Computing and Big Data. IEEE Transactions on Industrial Informatics, 19(11), 14785-14794.

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