基于变权-云模型理论的尉-且沙漠公路沙害综合评价OA

Assessing aeolian sand hazards is fundamental to the construction,operation,and maintenance of des-ert highways.However,conventional evaluation methods often suffer from excessive subjectivity,highlighting the need for an objective and robust assessment framework.Focusing on the Yuli-Qiemo desert highway in Chi-na,this study proposes a novel,data-driven method for evaluating aeolian sand-hazard risks,based on extensive field investigations conducted in 2023 and analyses.First,a variable-weight cloud model was established,incor-porating ten key indicators.A modified analytic hierarchy was used to determine the indicator fixed weights.Second,a dual-score evaluation method,integrated with computational algorithms,enabled automated batch pro-cessing of indicator stratification and dynamic weight adjustment based on variable-weight theory.Third,the variable-weight cloud model was used to classify hazard levels,which were validated against historical sand haz-ard records.The results indicate that(1)The proposed method enables efficient and accurate assessment of aeoli-an sand hazards along entire highways,transitioning from isolated segment evaluation to full-route analysis.This is achieved through the automated computation of state values and real-time adjustment of indicator weights.(2)Comparison with historical sand hazard records yielded a correlation coefficient of 0.91(P<0.001),indicating a significant positive correlation within the 95%confidence interval and demonstrating the method's ability to reduce human subjectivity.(3)The overall aeolian sand-hazard risk of the Yuli-Qiemo desert highway is high.Grade III hazard segments dominate(65.46%),followed by grade IV(30.91%),with no segments classified as grade I.The risk is low at the northern and southern ends of the highway,high in the middle segments,and gradually increases in severity from north to south.Middle segments K180-K250 and K30-K60 are the most se-verely affected and warrant prioritized mitigation efforts.This novel method for identifying and predicting aeoli-an sand hazard risks along desert highways offers critical insights to inform targeted prevention and control strate-gies.