RESUMEN
Based on the examination of the fundamental characteristics of structures, structural health monitoring (SHM) has received increased attention in recent years. Studies have shown that the SHM method using entropy analysis can precisely identify the damaged location of the structure, which is very helpful for the daily inspection or maintenance of civil structures. Although entropy analysis has shown excellent accuracy, it still consumes too much time and too many resources in terms of data processing. To improve the dilemma, in this study, modified multi-scale symbolic dynamic entropy (MMSDE) is adopted to identify the damaged location of the civil structure. A damage index (DI) based on the entropy diagram is also proposed to clearly indicate the damage location. A seven-story numerical model was created to verify the efficiency of the proposed SHM system. The results of the analysis of each case of damage show that the MMSDE curve for the damaged floor is lower than that for the healthy floor, and the structural damage can be correctly diagnosed by the damage index. Subsequently, a scaled-down steel benchmark experiment, including 15 damage cases, was conducted to verify the practical performance of the SHM system. The confusion matrix was used to further evaluate the SHM system. The results demonstrated that the MMSD-based system can quickly diagnose structural safety with reliability and accuracy. It can be used in the field of long-term structural health monitoring in the near future.
RESUMEN
OBJECTIVE: Lane departure, caused by inattention, distraction, drowsiness, or any unusual driver behavior, is a typical risk threatening the driver as well as other road users. Accurate perception of such situations through effective warnings would help drivers to avoid serious consequences. With regard to critical functions of warning symbols for risk communication, the present study focused on providing effective and easily perceivable symbols, compatible with human cognitive capabilities. Thus, the main purpose of the present study was to design and cognitively appraise 6 newly designed dynamic symbols, candidates for a new type of lane departure warning system. METHODS: Simplicity, familiarity, concreteness, meaningfulness, and semantic closeness were the major assessment criteria, defining cognitive features by the earlier researchers in the field. A total number of 187 driving license applicants, with a mean age of 20.58 years (SD = 3.20), participated in the present survey. The participants rated cognitive features of the 6 dynamic symbols along a 0-100 scale. RESULTS: Significant main effect of the element factor type of the designed symbols on rating cognitive features revealed that the existence of car element was the best predictor for illustrating lane departure. The interaction of both element factor and location of element factor significantly affected the ratings. However, the location of element factor did not solely have any strong effect on the ratings. The results also demonstrated that semantic closeness received the highest overall mean score across symbols (M = 61.80), especially within the symbols that include the car element (M = 75.67). Moreover, a significant difference was observed between the average ratings of the cognitive features, despite the fact that a significant correlation was found between cognitive features. CONCLUSION: The most considerable result of the current study was the match between the symbol with the highest ratings and the International Organization for Standardization (ISO)-related icon in appearance. Because previous studies demonstrated a strong correlation between comprehension scores of the symbol and both semantic closeness and meaningfulness, high-level comprehensibility of the best ranked symbol is expected.