| The construction industry is the pillar industry of the national economy and plays an important role in the economic development.However,because of the characteristics of the production process of its products,there have been many major quality accidents.Resulting in huge economic losses and casualties.Human error is one of the main reasons for many construction safety accidents.Therefore,how to do a good job in the construction of human error prevention,reduce the probability of human accidents,is an important subject worthy of in-depth study and discussion.Based on the theoretical analysis of human reliability and human error,combined with the characteristics of the construction industry,this paper compares the existing related theories.Cognitive Reliability and Error Analysis Method(CREAM)is chosen as the basic method of this study.Firstly,based on the REASON model,the analysis framework of the factors influencing the human error is constructed.Secondly,based on the CREAM retrospective analysis method,combined with the characteristics of the building construction,the root cause analysis frame of the construction accident is constructed.Thirdly,draw lessons from the DEMATEL and ANP,deeply analyze the weight of factors,the influence paths and the key factors of the factors caused by human errors.Finally,in the quantitative analysis of the probability of human error,aiming at the complexity and uncertainty of the construction,the Bayesian network analysis method is used for reference,based on the CREAM prediction method.An improved prediction method of human error probability is put forward,and the probability of human error in construction construction is quantitatively forecasted.The calculation and analysis of examples show that the improved method can effectively solve the uncertainty problem in the process of common performance condition level evaluation and cognitive control pattern recognition.To some extent,it alleviates the subjectivity of expert evaluation,and provides theoretical support for improving the prediction accuracy of human error probability and risk control of construction enterprises. |
