Meocci Monica
Dipartimento di Ingegneria Civile e Ambientale, Università degli Studi di Firenze, 50139 Firenze, Italy.
Sensors (Basel). 2024 Jan 31;24(3):925. doi: 10.3390/s24030925.
Road pavement monitoring represents the starting point for the pavement maintenance process. To quickly fix a damaged road, relevant authorities need a high-efficiency methodology that allows them to obtain data describing the current conditions of a road network. In urban areas, large-scale monitoring campaigns may be more expensive and not fast enough to describe how pavement degradation has evolved over time. Furthermore, at low speeds, many technologies are inadequate for monitoring the streets. In such a context, employing black-box-equipped vehicles to perform a routine inspection could be an excellent starting point. However, the vibration-based methodologies used to detect road anomalies are strongly affected by the speed of the monitoring vehicles. This study uses a statistical method to analyze the effects of speed on road pavement conditions at different severity levels, through data recorded by taxi vehicles. Likewise, the study introduces a process to overcome the speed effect in the measurements. The process relies on a machine learning approach to define the decision boundaries to predict the severity level of the road surface condition based on two recorded parameters only: speed and pavement deterioration index. The methodology has succeeded in predicting the correct damage severity level in more than 80% of the dataset, through a user-friendly real-time method.
道路路面监测是路面养护过程的起点。为了快速修复受损道路，相关部门需要一种高效的方法，以便能够获取描述道路网络当前状况的数据。在城市地区，大规模监测活动可能成本更高，而且描述路面退化随时间的演变情况不够迅速。此外，在低速行驶时，许多技术都不足以用于街道监测。在这种情况下，使用配备黑匣子的车辆进行例行检查可能是一个很好的起点。然而，用于检测道路异常的基于振动的方法会受到监测车辆速度的强烈影响。本研究通过出租车记录的数据，使用统计方法来分析速度对不同严重程度的道路路面状况的影响。同样，该研究还介绍了一种克服测量中速度影响的方法。该方法依靠机器学习方法来定义决策边界，仅基于两个记录参数（速度和路面劣化指数）预测路面状况的严重程度等级。通过一种用户友好的实时方法，该方法成功地在超过80%的数据集中预测出了正确的损坏严重程度等级。
