College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USA.
Sensors (Basel). 2018 Aug 17;18(8):2713. doi: 10.3390/s18082713.
Grooving is widely used to improve airport runway pavement skid resistance during wet weather. However, runway grooves deteriorate over time due to the combined effects of traffic loading, climate, and weather, which brings about a potential safety risk at the time of the aircraft takeoff and landing. Accordingly, periodic measurement and evaluation of groove performance are critical for runways to maintain adequate skid resistance. Nevertheless, such evaluation is difficult to implement due to the lack of sufficient technologies to identify shallow or worn grooves and slab joints. This paper proposes a new strategy to automatically identify airport runway grooves and slab joints using high resolution laser profiling data. First, K-means clustering based filter and moving window traversal algorithm are developed to locate the deepest point of the potential dips (including noises, true grooves, and slab joints). Subsequently the improved moving average filter and traversal algorithms are used to determine the left and right endpoint positions of each identified dip. Finally, the modified heuristic method is used to separate out slab joints from the identified dips, and then the polynomial support vector machine is introduced to distinguish out noises from the candidate grooves (including noises and true grooves), so that PCC slab-based runway safety evaluation can be performed. The performance of the proposed strategy is compared with that of the other two methods, and findings indicate that the new method is more powerful in runway groove and joint identification, with the F-measure score of 0.98. This study would be beneficial in airport runway groove safety evaluation and the subsequent maintenance and rehabilitation of airport runway.
刻槽广泛应用于提高机场跑道在湿滑天气下的抗滑性能。然而，由于交通荷载、气候和天气的综合影响，跑道刻槽会随着时间的推移而恶化，这在飞机起飞和降落时会带来潜在的安全风险。因此，定期测量和评估槽性能对于保持跑道足够的抗滑性能至关重要。然而，由于缺乏足够的技术来识别浅槽和磨损的槽以及板缝，因此这种评估很难实施。本文提出了一种使用高分辨率激光剖面数据自动识别机场跑道槽和板缝的新策略。首先，开发了基于 K-means 聚类的滤波器和移动窗口遍历算法，以定位潜在凹陷（包括噪声、真实槽和板缝）的最深点。随后，使用改进的移动平均滤波器和遍历算法来确定每个识别出的凹陷的左右端点位置。最后，使用改进的启发式方法将板缝从识别出的凹陷中分离出来，然后引入多项式支持向量机来区分出候选槽中的噪声（包括噪声和真实槽），从而可以进行基于 PCC 板的跑道安全评估。将所提出的策略的性能与另外两种方法进行了比较，结果表明，该新方法在跑道槽和接头识别方面更具优势，F 值为 0.98。这项研究将有助于机场跑道槽的安全评估以及随后的机场跑道维护和修复。
