Geological Hazard Assessment Of Permafrost In The Permafrost Region Of The National Highway 345

Permafrost quality disasters(thawing and frost heaving)are the most common geological disasters in central and western China,and are also the diseases that road engineering strives to avoid from the design to the completion and operation stage.This article takes the national highway 345 line as many as Chawula section The permafrost area is the research object,and the various causes and mechanism of the geological disasters induced by the permafrost area are studied.Based on the basic theory of extreme learning machine neural network,a risk assessment model for geological disasters in the frozen soil area was established to carry out risk assessment for the geological disasters in the frozen soil area from the Zao to Chawula section of National Highway 345.The main work and results of the paper are as follows:(1)Based on the permafrost investigation project in the permafrost area of ??the National Highway 345 line to the Chawula section and the indoor permafrost experiment,introduce the preparations for the early stage of the permafrost survey and the specific content of the survey and the engineering characteristics of the permafrost area.Through the investigation of the frozen soil survey data and the results of the laboratory tests of the frozen soil,it is found that the geological disasters occurring in the frozen soil section of the National Highway 345 line from the Zadu to Chawula section are mainly thawing and frost heaving;the main inducing factors are: terrain Geomorphology,stratigraphic origin and lithology,development of frozen layer upper water,type of frozen soil,thickness and upper limit of frozen soil,classification of frost heave of seasonal thawing layer,classification of thawing sediment of frozen soil,development of unfavorable frozen soil phenomenon,geothermal zoning,Engineering behavior,etc.(2)Research on the main impact factors of geological disasters in frozen soil areas,and on this basis,perform data processing on the impact factors that induce geological disasters in frozen soil areas,and assign weights to each of the disaster-causing impact factors.(3)By comparing the prediction accuracy of the extreme learning machine model and the BP neural network evaluation model,it is found that the extreme learning machine evaluation model is superior to the BP neural network evaluation model in terms of running speed and accuracy.The evaluation model is used as the risk assessment model for geological hazards in the frozen soil area of Zaduo to Chawula section of National Highway 345.(4)An extreme learning machine evaluation model was used to evaluate the risk of geological disasters in the 12 frozen soil assessment areas.The result was that there were 3 areas where extremely dangerous geological hazards might occur;8 areas where high-risk geological hazards occurred;moderate There are 1 area for dangerous geological disasters;0 area for mildly dangerous geological disasters.