高速公路隧道健康诊断及预警的模糊神经网络方法  

Method of fuzzy neural network for expressway tunnel health diagnosis and early-warning

作　　者：王洪德[1,2] 高秀鑫[1] 

机构地区：[1]大连交通大学土木与安全工程学院,辽宁大连116028 [2]大连交通大学隧道与地下结构工程技术研究中心,辽宁大连116028

出　　处：《中国安全科学学报》2014年第2期9-15,共7页China Safety Science Journal

基　　金：辽宁省自然科学基金资助(201202022);大连市科技计划项目(2011E15SF118)

摘　　要：以高速公路隧道健康状态定量诊断和隧道衬砌结构劣化趋势预警为目标,建立高速公路隧道健康诊断指标体系,量化隧道健康状态诊断指标。根据指标体系中诊断指标特性,建立判定标准,实现对各诊断指标的准确判定。根据高速公路隧道健康状态诊断特点,采用模糊层次分析法(FAHP)确定各诊断指标权重。基于隧道劣化趋势及劣化程度分析,建立隧道健康状况诊断标准及预警等级划分原则。最后,结合实例分析,融合模糊逻辑与神经网络技术,构建隧道健康综合诊断模式,划分隧道健康状况劣化等级,并据此提出相应对策措施。诊断结果表明,实例隧道031区段与032区段的健康预警等级均为2级,即隧道衬砌结构虽有劣化趋势,但支护状况较好。Taking quantitative diagnosis of expressway tunnel health state, ana eany-warmng t,~ exr, L^- way tunnel lining structure deterioration trend as objective, a health diagnosis indexes system was estab- lished. Basing on characteristics of each diagnostic index, diagnostic criteria were set up to achieve accu- rate determination of diagnostic criteria. According to diagnostic characteristics for expressway tunnel health state, FAHP was used to determine the weights of diagnostic indexes, by analysis of tunnel deteriora- tion tendency and its deterioration degree, tunnel health diagnosis standards and early-warning grades dif- ferentiation principles were established. Finally, neural network is combined with fuzzy logic to construct the tunnel health diagnosis mode, and to grade the tunnel health deterioration classes. The diagnostic results show that health early-warning grade of No. 031 and No. 032 tunnels sections belong to 2^th grade, i.e. Tunnel supporting status is in a good condition though deterioration tendency of its lining structure has appeared.

关 键 词：公路隧道 健康诊断 劣化趋势 量化指标 判定标准 指标权重 模糊层次分析法(FAHP) 

分 类 号：X951[环境科学与工程—安全科学]