Safety risk management of underground engineering in China：Progress, challenges and strategies

Underground construction in China is featured by large scale, high speed, long construction period,complex operation and frustrating situations regarding project safety. Various accidents have been reported from time to time, resulting in serious social impact and huge economic loss. This paper presents the main progress in the safety risk management of underground engineering in China over the last decade, i.e.（1） establishment of laws and regulations for safety risk management of underground engineering,（2） implementation of the safety risk management plan,（3） establishment of decision support system for risk management and early-warning based on information technology, and（4） strengthening the study on safety risk management, prediction and prevention. Based on the analysis of the typical accidents in China in the last decade, the new challenges in the safety risk management for underground engineering are identified as follows：（1） control of unsafe human behaviors;（2） technological innovation in safety risk management; and（3） design of safety risk management regulations. Finally, the strategies for safety risk management of underground engineering in China are proposed in six aspects, i.e. the safety risk management system and policy, law, administration, economy, education and technology.

Estimation of the behavior factor of existing RC-MRF buildings

In recent years, several research groups have studied a new generation of analysis methods for seismic response assessment of existing buildings. Nevertheless, many important developments are still needed in order to define more reliable and effective assessment procedures. Moreover, regarding existing buildings, it should be highlighted that due to the low knowledge level, the linear elastic analysis is the only analysis method allowed. The same codes（such as NTC2008, EC8） consider the linear dynamic analysis with behavior factor as the reference method for the evaluation of seismic demand. This type of analysis is based on a linear-elastic structural model subject to a design spectrum, obtained by reducing the elastic spectrum through a behavior factor. The behavior factor（reduction factor or q factor in some codes） is used to reduce the elastic spectrum ordinate or the forces obtained from a linear analysis in order to take into account the non-linear structural capacities. The behavior factors should be defined based on several parameters that influence the seismic nonlinear capacity, such as mechanical materials characteristics, structural system, irregularity and design procedures. In practical applications, there is still an evident lack of detailed rules and accurate behavior factor values adequate for existing buildings. In this work, some investigations of the seismic capacity of the main existing RC-MRF building types have been carried out. In order to make a correct evaluation of the seismic force demand, actual behavior factor values coherent with force based seismic safety assessment procedure have been proposed and compared with the values reported in the Italian seismic code, NTC08.

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits,safety behaviors,and anxiety factors in the community during the novel coronavirus disease(COVID-19)pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents.In other words,this paper aims to provide empirical insights into the correlation and the correspondence between sociodemographic factors(gender,nationality,age,citizenship factors,income,and education),and psycho-behavioral effects on individuals in response to the emergence of this new pandemic.To focus on the interaction between these variables and their effects,we suggest different methods of analysis,comprising regression trees and support vector machine regression(SVMR)algorithms.According to the regression tree results,the age variable plays a predominant role in health habits,safety behaviors,and anxiety.The health habit index,which focuses on the extent of behavioral change toward the commitment to use the health and protection methods,is highly affected by gender and age factors.The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period.The results of the SVMR model reveal a strong positive effect of income,with R^(2) values of 99.59%,99.93%and 99.88%corresponding to health habits,safety behaviors,and anxiety.

Oral but not written test anxiety is related to social anxiety

AIM To examine the associations of test anxiety(TA) in written vs oral exam situations with social anxiety(SA).METHODS A convenience sample of 204 students was recruited at the Technische Universit?t Dresden(TU Dresden,Germany) and contacted via e-mail asking to complete a cross-sectional online survey based on established questionnaires.The study protocol was approved by the ethics committee of the TU Dresden.Full data of n = 96 students were available for dependent t-tests and correlation analyses on the associations of SA and TA respectively with trigger events,cognitions,safety behaviors,physical symptoms and depersonalization.Analyses were run using SPSS.RESULTS Levels of TA were higher for fear in oral exams than for fear in written exams(M = 48.1,SD = 11.5 vs M = 43.7,SD = 10.1 P < 0.001).Oral TA and SA were positively correlated(Spearman's r = 0.343,P < 0.001;Pearson's r = 0.38,P < 0.001) contrasting written TA and SA(Spearman's r = 0.17,P > 0.05;Pearson's r = 0.223,P > 0.05).Compared to written TA,triggerevents were more often reported for oral TA(18.2% vs 30.3%,P = 0.007);which was also accompanied more often by test-anxious cognitions(7.9% vs 8.5%,P = 0.001),safety behavior(8.9% vs 10.3%,P < 0.001) and physical symptoms(for all,P < 0.001).CONCLUSION Written,but not oral TA emerged being unrelated to SA and may rather not be considered as a typical facet of SA disorder.

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive（AR） time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.