Risk prediction models based on BP neural network and V/S analysis are proposed in this paper to evaluate the safety status and risk trend of tunnels under operation. First of all, the deformation mechanism is analyzed in line with the operating environment of the tunnel; secondly, the safety status of tunnel is assessed according to relevant safety classification standard; finally, the deformation prediction model of the tunnel is established using optimized BP neural network, and the risk development trend of tunnel is then examined based on V/S risk analysis. Case study shows that the deformation of tunnel is mainly affected by seepage, cavities, lining deterioration, and long-term stress. The safety status of the tunnel is at level V, high risk, and the risk level acquired based on settlement is higher than that of horizontal convergence. The deformation prediction result is well consistent with trend analysis result which indicates that the risk of the tunnel will further exacerbate. Both the deformation prediction model and the risk trend analysis model are verified applicable and reliable in predicting the risks of tunnel in operation.
Key words
tunnel in operation /
deformation mechanism /
hazard /
BP neural network /
V/S analysis
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 苏东兰, 郭忠印, 周亚枫,等. 高速公路隧道运营风险分析及动态预警系统[J] . 公路工程, 2016, 41(6):10-15.
[2] 邱子锋, 沈 简, 傅旭东,等. 基于最优加权组合预测的隧道监控量测数据分析[J] . 长江科学院院报, 2016, 33(5):53-57.
[3] 程红战, 陈 健, 胡之锋,等. 基于稳定系数法的盾构隧道开挖面失稳概率分析[J] . 长江科学院院报, 2018, 35(1):133-136.
[4] 孙文龙. 毛坪隧道衬砌缺陷致害机理与加固措施研究[D] . 重庆:重庆交通大学, 2014.
[5] 张建伟. 运营隧道健康检测评估模型研究[D] . 重庆:重庆交通大学, 2012.
[6] 王 勇. 运营公路隧道结构安全动态评估体系及评价方法研究[D] . 成都:西南交通大学, 2009.
[7] 任 松, 王新胜, 姜德义,等. 隧道长期安全监测及评价模型[J] . 重庆大学学报:自然科学版, 2009, 32(11):1345-1351.
[8] 刘 涛. 运营公路隧道纵向稳定性的长期监测研究[J] . 山西建筑, 2010, 36(14):331-332.
[9] 叶 超. 基于BP神经网络修正的自适应灰色模型的隧道变形预测研究[J] . 铁道标准设计, 2017, 61(11):76-81.
[10] 胡纪元, 鸿 雁, 周 吕,等. 基于小波神经网络的隧道变形预测模型研究[J] . 公路工程, 2014,39(4):26-29.
[11] 张志强, 李化云, 阚 呈,等. 大相岭隧道断层破碎带围岩变形的GA-BP神经网络预测技术[J] . 现代隧道技术, 2014, 51(2):83-89.
[12] 李 昂, 王 旭, 杨 姝,等. 基于GA-BP模型的临近深基坑桥墩变形预测[J] . 铁道建筑, 2016(11):84-87.
[13] 邬长福, 涂志刚, 万佳威,等. 基于R/S分析与V/S分析的滑坡变形趋势判断及稳定性研究[J] . 水电能源科学, 2015,33(1):111-114.
[14] 张 碧. 基于多种优化支持向量机及V/S分析法的隧道变形预测及趋势判断[J] . 长江科学院院报, 2018, 35(4):67-71.
[15] 左昌群, 刘代国, 丁少林,等. 基于分形理论的隧道地表沉降分析及预测[J] . 长江科学院院报, 2016, 33(4):51-56.
[16] 王立伟, 谢谟文, 柴小庆. 滑坡变形空间评价的位移速率比方法研究[J] . 岩土力学, 2014,35(2):519-528.
[17] 张云鹏,李利平,贺 鹏,等.隧道围岩大变形高斯过程回归预测模型及其工程应用[J] .科学技术与工程,2018,18(1):122-127.
PDF(1799 KB)
