There often exist behaviors of moving against the main direction of evacuation in order to rescue or find the important missing people in real situations. However, the traditional social force model (SFM) often lacks consideration of such “counter flow”. Motivated by this, we improve the traditional SFM to study the counter flow and its influence on evacuation out of multi-exit rooms. We call the person to be rescued “superior” and the rescuers “subordinate”. Two different rescue situations are proposed: superior waiting in place (case 1) and superior moving towards the exit (case 2). The results show that the counter flow will always reduce the evacuation efficiency to a certain extent, and the evacuation efficiency of case 1 is lower than that of case 2. At the same time, for these two cases, increasing the number of rescuers increases the evacuation time. We also find that the existence of counter flow can enlarge the effect of “faster-is-slower”, while increasing the number of exports can significantly improve the rescue efficiency. We hope that this result can help to improve the efficiency of emergency evacuation with rescue.

A scientifically accurate assessment of tunnel health is the prerequisite for the safe maintenance and sustainability of the in-service road tunnel. The changing trend of tunnel health is not considered in existing research. Most evaluation indicators are static and the ambiguities or randomness at the boundary of the health level intervals is neglected in most evaluation methods. In this paper, the evaluation index system combined with dynamic, and static is set to solve these problems. The changing trend of the health state of tunnels is analyzed through the cubic b-spline function. The weights of evaluation indicators are calculated based on the AHP-improved entropy method. The health evaluation method is proposed based on combing the extension theory and the cloud model improved by the cloud entropy optimization algorithm. Finally, the evaluation results of the proposed method are compared with the detection data of the Beilongmen Tunnel of Zhangzhuo Expressway. The results demonstrate that 80% of the evaluation results in the sample tunnel data are consistent with the standard results, and the remaining 20% show a lower level of health than the standard results. This reflects the evaluation results are impacted by the trend of tunnel health status changes. The health state can be more accurately evaluated by dynamic indicators. The improved extension cloud model is feasible and applicable in the health assessment of tunnels. This work provides innovative ideas for the evaluation of the health state of tunnels and theoretical support for the formulation of reasonable maintenance measures.

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