What Is the Knowledge of Evacuation Procedures in Road Tunnels? Survey Results of Users in Poland

This paper presents a preliminary assessment of road tunnel safety issues among respondents—specifically, real and potential users of road tunnels. We recruited a group of respondents to study their knowledge of evacuation procedures and awareness of safety issues in road tunnels. We conducted surveys with 504 participants, 12.7% of whom had previously participated in real-scale evacuation experiments in a road tunnel. Analysis of respondents’ answers reveals that their knowledge of safety procedures is unfortunately not sufficient. On average, the respondents selected the most recommended answer for approximately 5.35 out of 15 questions. Only 16% of respondents correctly answered more than 50% of the survey questions; moreover, no respondent provided the correct answers for 12 or more questions. Interestingly, most respondents were convinced that they had a better knowledge of road tunnel safety issues than was actually the case. The results of the survey demonstrate a significant educational role of evacuation exercises. Individuals who have participated in an evacuation have better knowledge, allowing them to apply the correct rules of road tunnel safety procedures. Various aspects addressed in this paper can be taken into consideration in an information campaign regarding safety in road tunnels during a fire.

Recognition of Tunnel Lining Cracks Based on Digital Image Processing

Most of the tunnel projects are related to the national economy and people’s livelihood, and their operational safety is of paramount importance. Tunnel safety accidents or hidden safety hazards often start from subtleties. Therefore, the identification of tunnel cracks is a key part of tunnel safety control. The development of computer vision technology has made it possible for the automatic detection of tunnel cracks. Aiming at the problem of low recognition accuracy of existing crack recognition algorithms, this paper uses an improved homomorphic filtering algorithm to dehaze and clear the collected images according to the characteristics of tunnel images and uses an adaptive median filter to denoise the grayscaled image. The extended difference of Gaussian function is used for edge extraction, and the morphological opening and closing operations are used to remove noise. The breakpoints of the binary image are connected after removing the noise to make the image more in line with the actual situation. Aiming at the identification of tunnel crack types, the block index is proposed and used to distinguish linear cracks and network cracks. Using the histogram-like method to distinguish linear cracks in different directions can well solve the mixed crack situation in an image. Compared with the traditional method, the recognition rate of the new algorithm is increased to 94.5%, which is much higher than the traditional crack recognition algorithm. The average processing time of an image is 5.2 s which is moderate, and the crack type discrimination accuracy is more than 92%. In general, the new algorithm has good prospects for theoretical promotion and high engineering application value.