杭州市隧道运营期结构健康监测数据

隧道运营期结构健康监测数据用于隧道结构运营其健康状况分析，其数据包括混凝土表面应变、收敛变形、混凝土表面接缝宽度等数据，帮助隧道运营养护方客观、科学、精准了解隧道结构健康状况。1、界定隧道结构健康状况为两个层次，第一层次为结构变形与结构内力，在第二层次将结构变形细分为接缝张开度、拱顶收敛，结构内力细分为混凝土表面应变；2、采用三标度法确定各层次各指标权重。对于第一层次，结构变形重要程度大于结构内力,采用三标度法，建立比较矩阵[0 1][-1 0],各指标归一化权重后，求得各自权重分别为 0.73、 0.27。同理，对于第二层次，结构变形下指标各自权重依次为 0.73、 0.27；3、根据对应的隶属度函数形式计算各层次评价指标对各预警等级的隶属度。 先对最底层各评价指标在一个断面内的多处监测数据进行加权平均融合，将加权平均融合值代入隶属度函数计算相应评价指标对各预警等级的隶属度； 将最底层计算出的隶属度代入模糊关系方程组，层层向上代入计算，得到所有评价指标对各预警等级的隶属度；4、将上述算法植入自动化监测软件，计算断面一段时期内整体安全性的连续实时评价结果，评价结果为四组预警，哪个预警值最大，评价结果即为该区间，预警等级分为绿、蓝、橙、红四个等级，四个等级分别对应四组预警区间，如预警值（0.949,0.084,0.0588,0.0077）预警等级为绿色相对安全

Structural health monitoring (SHM) data during the tunnel operation period is used for the health condition analysis of tunnel structures during their service phase. The dataset includes concrete surface strain, convergence deformation, concrete joint width, and other monitoring data, enabling tunnel operation and maintenance parties to objectively, scientifically, and accurately grasp the health status of tunnel structures. 1. Define the health status of tunnel structures into two levels: the first level covers structural deformation and structural internal force; the second level subdivides structural deformation into joint opening and vault convergence, and subdivides structural internal force into concrete surface strain. 2. The three-scale method is adopted to determine the weights of each indicator at each level. For the first level, structural deformation is more important than structural internal force. Using the three-scale method, the comparison matrix [[0, 1], [-1, 0]] is established. After normalizing the weights of each indicator, their respective weights are calculated as 0.73 and 0.27, respectively. Similarly, for the second level, the weights of the indicators under structural deformation are 0.73 and 0.27 in sequence. 3. Calculate the membership degree of each evaluation indicator at each level to each early warning level according to the corresponding membership function. First, perform weighted average fusion on multiple monitoring data of each bottom-level evaluation indicator within a single cross-section. Substitute the fused weighted average value into the membership function to calculate the membership degree of the corresponding evaluation indicator to each early warning level; then substitute the membership degrees calculated at the bottom level into the fuzzy relation equation system, and conduct layer-by-layer upward calculation to obtain the membership degrees of all evaluation indicators to each early warning level. 4. Embed the aforementioned algorithm into automated monitoring software to calculate the continuous real-time evaluation results of the overall safety of a cross-section over a certain period. The evaluation results are categorized into four groups of early warning values, and the interval corresponding to the maximum early warning value is taken as the evaluation result of this section. The early warning levels are divided into four grades: green, blue, orange, and red, which respectively correspond to four groups of early warning intervals. For example, for the early warning value (0.949, 0.084, 0.0588, 0.0077), the corresponding early warning level is green, indicating relatively safe.

搜集汇总

数据集介绍

特点

杭州市隧道运营期结构健康监测数据集包含997条记录，每年更新一次，数据涵盖混凝土表面应变、收敛变形、接缝宽度等关键指标，用于隧道结构健康状况的实时监测与预警。通过三标度法和模糊关系计算，生成四个预警等级，帮助运营养护方科学评估隧道结构的安全性。