The ramp slip test is a reliable and widely used method to evaluate the slip resistance of interspersed synthetic resin floors, common in industrial and parking areas. Following EN 16165 and using a ramp with varying inclinations, the floor R-class can be determined. This test can, however, only be performed in a laboratory, and not on-site. Here, we propose a method to determine the floor R-class from its surface topography, which can easily be measured on-site. In this study, floors of various slip resistances were prepared following the conventional method of spreading quartz sand onto a liquid resin. The interspersed surface creates a 3-dimensional topography, which is characteristic for the anti-slip properties. After the resin hardening, the R-class was measured and, in parallel, a convolutional neural network was developed and trained with the numerous photographs taken from the specimen surfaces. The used convolutional neural network classifies 95% of the surfaces into the correct R-class. This result makes it possible, for the first time, to determine the R-class slip resistance on-site with a high probability of correctness.

斜坡滑移试验是一种可靠且广泛使用的方法，用于评估工业和停车场中常见的散布合成树脂地板的防滑性。遵循 EN 16165 并使用具有不同倾斜度的坡道，可以确定地板 R 级。但是，此测试只能在实验室中进行，而不能在现场进行。在这里，我们提出了一种根据表面形貌确定地板 R 级的方法，可以在现场轻松测量。在这项研究中，按照将石英砂撒在液体树脂上的传统方法制备了各种防滑性的地板。散布的表面形成 3 维地形，这是防滑特性的特征。树脂硬化后，测量 R 级，同时，开发了一个卷积神经网络，并使用从标本表面拍摄的大量照片进行训练。使用的卷积神经网络将 95% 的表面分类为正确的 R 类。该结果首次使现场确定 R 级防滑性成为可能，并且正确率很高。