Wang Yudan, Li Jue, Zhang Xinqiang, Yao Yongsheng, Peng Yi
School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China.
Sensors (Basel). 2024 Apr 26;24(9):2777. doi: 10.3390/s24092777.
Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.
智能压实（IC）已成为一项突破性技术，它利用先进的传感、数据传输和控制系统来优化沥青路面的压实质量和效率。然而，在实际施工条件下，准确评估压实状态仍然具有挑战性。本文综述了智能传感器和机器学习（ML）在解决智能压实现有局限性方面的最新进展和应用。介绍了智能压实系统中部署的各种先进传感器的原理和组件，包括智能岩石传感器、光纤布拉格光栅传感器和集成电路压电加速度传感器。回顾了利用这些传感器进行颗粒行为监测、应变测量和冲击数据采集的案例研究。同时，讨论了包括回归、分类、聚类和人工神经网络在内的常见机器学习算法。研究了应用机器学习通过监督和无监督模型估计力学性能、评估整体压实质量和预测土壤坚实度的实际例子。结果表明，智能传感器增强了压实监测能力，但需要提高其稳健性。机器学习提供了一种数据驱动的方法来补充传统的经验方法，但需要进行广泛的现场验证。还探索了与建筑信息模型和增强现实等数字施工技术的潜在集成。总之，利用新兴的传感技术和人工智能为优化智能压实过程和应对关键挑战提供了机会。然而，跨学科合作对于在实际条件下测试和完善技术至关重要。本研究有助于增进理解，并突出未来研究的重点领域，以实现智能压实的全部潜力。
