长春工程学院学报:自然科学版
長春工程學院學報:自然科學版
장춘공정학원학보:자연과학판
Journal of Changchun Institute of Technology(Social Science Edition)
2012年
3期
46-49
,共4页
崩塌危险度%改进灰色聚类法%评价因子%地质灾害
崩塌危險度%改進灰色聚類法%評價因子%地質災害
붕탑위험도%개진회색취류법%평개인자%지질재해
collapse risk%improved grey clustering method%evaluation factors%geological disaster
以"5.12"地震重灾县———青川县(青川生命线道路沿线)作为研究区域,选取产生崩塌及影响其稳定性的9个因素为评价因子,将崩塌危险度分为无危险、低度危险、中度危险和高度危险4个等级,建立了基于改进灰色聚类法的崩塌危险度评价模型。应用该模型对青川生命线道路沿线崩塌灾害点进行评价,将此评价结果与经典灰色聚类法所得的评价结果进行对比。研究结果表明,基于改进灰色聚类法的崩塌危险度评价模型所得的评价结果比经典灰色聚类法所得的评级结果更接近于现场专家勘查结果。由此可见,采用这种基于改进灰色聚类法的评价模型对崩塌危险度进行快速、科学、有效地评价是切实可行的。
以"5.12"地震重災縣———青川縣(青川生命線道路沿線)作為研究區域,選取產生崩塌及影響其穩定性的9箇因素為評價因子,將崩塌危險度分為無危險、低度危險、中度危險和高度危險4箇等級,建立瞭基于改進灰色聚類法的崩塌危險度評價模型。應用該模型對青川生命線道路沿線崩塌災害點進行評價,將此評價結果與經典灰色聚類法所得的評價結果進行對比。研究結果錶明,基于改進灰色聚類法的崩塌危險度評價模型所得的評價結果比經典灰色聚類法所得的評級結果更接近于現場專傢勘查結果。由此可見,採用這種基于改進灰色聚類法的評價模型對崩塌危險度進行快速、科學、有效地評價是切實可行的。
이"5.12"지진중재현———청천현(청천생명선도로연선)작위연구구역,선취산생붕탑급영향기은정성적9개인소위평개인자,장붕탑위험도분위무위험、저도위험、중도위험화고도위험4개등급,건립료기우개진회색취류법적붕탑위험도평개모형。응용해모형대청천생명선도로연선붕탑재해점진행평개,장차평개결과여경전회색취류법소득적평개결과진행대비。연구결과표명,기우개진회색취류법적붕탑위험도평개모형소득적평개결과비경전회색취류법소득적평급결과경접근우현장전가감사결과。유차가견,채용저충기우개진회색취류법적평개모형대붕탑위험도진행쾌속、과학、유효지평개시절실가행적。
We take Qingchuan county(Qingchuan Line-road)— a hard hit area in the "5.12" Earthquake as the research area.Nine elements that contribute the collapse and affect its stability have been selected as evaluation factors.The collapse risk levels have been divided into four levels as non-hazardous,low dangerous,moderate risk and high risk.The evaluation model of highway collapse risk based on the improved grey clustering method has been established.By applying this model,we evaluate the geological disaster area along Qingchuan line-road.With the comparison between this evaluation results and the classical grey clustering method,we can see that the results got from improved grey clustering model to evaluate the collapse are much closer to experts’s site investigation findings than classical grey clustering method.Therefore,it is practical and feasible to have a quick,scientific and efficient evaluation for the collapse risk by using this improved grey clustering evaluation model.