With the rapid development of engineering technology, the application of construction vehicles in fields such as construction and transportation is becoming increasingly widespread. Their dynamic stability directly impacts the safety and efficiency of operations. This article aims to explore a dynamic stability identification and warning system for construction vehicles that integrates machine learning and data-driven technology. It clarifies the importance of dynamic stability analysis for construction vehicles and highlights the limitations of traditional methods. This study proposes a dynamic stability identification model based on sensor data. This model employs various machine learning algorithms, such as random forest and support vector machine, to conduct in-depth analysis of the dynamic behavior of construction vehicles under different working conditions. To enhance the accuracy and robustness of the model, this article also explores effective methods for feature selection and data preprocessing. By training and testing the collected data, the performance of the model in dynamic stability recognition was evaluated and compared with traditional methods. The results demonstrated that the machine learning model exhibits significant advantages in terms of accuracy and real-time performance. This article designs a warning system based on identification results, which realizes real-time monitoring and warning of dynamic stability risks of construction vehicles. The dynamic stability identification and warning system that integrates machine learning and data-driven technology holds promising application prospects in enhancing the safety of construction vehicles. Future research will focus on optimizing model performance under more complex operating conditions and promoting the implementation of the system in practical applications. 

Jinshuo Zhang. Research on Dynamic Stability Identification and Early Warning System for Engineering Vehicles Integrating Machine Learning and Data Driven Technology. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 2: 51-55. https://doi.org/10.25236/AJCIS.2025.080207.

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