ABSTRACT
Clarifying the relationship between the man-machine environment and its impact on the tunnel wall drilling task performance (TWDTP) is crucial for enhancing the task performance. Based on a questionnaire survey, indicators of the man-machine environment that affect the TWDTP were proposed in this study, and exploratory factor analysis and a structural equation model were employed to examine the potential factors influencing the task performance and their degrees of influence. By comparing the discrepancy between the perceived performance and importance, the satisfaction of potential factors was evaluated, and the priority order for optimizing these factors was determined by considering the degree of influence and dissatisfaction. The results of survey data analysis based on actual tunnel drilling operation scenarios indicated that tools had the greatest impact on the TWDTP, followed by the quality of the physical environment, while human factors had the least influence on the task performance. Convenient functional maintenance is the key to improving the TWDTP, along with enhancing the quality of the working environment. Once these main aspects are optimized, it is important to consider additional factors such as availability of spare tools, efficient personnel organization, man-tool matching, and safety and health assurance. This research approach provides significant guidance in understanding the relationships between the man-machine environmental factors affecting the performance of complex engineering tasks and identifying key influencing factors, thus providing essential insights for optimizing the TWDTP.
ABSTRACT
This study investigated the impact of subway car interior design on passenger evacuation and boarding/alighting efficiency. The usability of pedestrian agent models was verified through real-life experiments. A seven-factor orthogonal simulation experiment was designed, using key geometric features of the subway car interior as variables. The results of the computer simulation showed that the impact of subway car interior design factors on evacuation and boarding/alighting time was not entirely consistent, with seat layout and door width being the most important factors affecting passenger movement. In the evacuation scenario, only the connectivity of the subway car has no effect on evacuation time, while in the boarding and alighting scenario, seat layout, car type, door width, and foyer width all significantly affect boarding and alighting time. Multivariate regression models were established to predict evacuation and boarding/alighting times through design features, which can explain 86.7% and 58.9% of the time variation, respectively. The research results were used to guide subway car design, and the proposed new scheme demonstrated better performance.
ABSTRACT
To make appropriate decisions in the evaluation phase of the exterior design of subway trains, an optimal selection method was proposed based on multi-level gray relational analysis. The exterior design factors of subway trains were analyzed to construct an index system for design evaluation. The significance of each index was compared through an analytic hierarchy process. The correlation coefficient of each index in the plan was calculated through gray relational analysis to obtain the weighted correlation degree of each design scheme. The optimal selection of the exterior design of Guangzhou Metro Line 6 in China was considered as an example. Four types of subjects were recruited: professional designers, students majoring in design, subway train design experts, and subway passengers in Guangzhou. The weight of each index in the evaluation system was calculated using questionnaire scoring. Virtual simulation software was applied to evaluate the human factors related to each scheme. The indices in each plan were then scored to calculate the correlation coefficient and the overall correlation degree; and finally, the optimal selection was obtained. The results showed that it was practical to evaluate and optimize the exterior design of subway trains based on multi-level gray relational analysis. In the evaluation index system, the weights of technology, human factors, aesthetics, and culture were 0.517, 0.297, 0.099, and 0.087, respectively, which showed that technology had the greatest impact on the system, while human factors, aesthetics, and culture were useful complements. Our results showed that Design Scheme 1 was unsuitable as an optimization scheme due to the high escape window. Meanwhile, Design Scheme 2 was optimal overall, from a technical perspective. Design Scheme 3 was the best in terms of the escape window index (a human factor). Design Schemes 3 and 4 were optimally assessed from aesthetic and cultural perspectives. This study is conducive to the optimization of the exterior design of subway trains, can be used to inform design iteration, and provides a reference for the optimal selection of design schemes for other urban rail trains.
