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地表沉降是盾构施工质量及环境控制的重要指标。然而由于具体施工参数、地质条件及地表边界条件的复杂性和不确定性,实测地表沉降大小及其分布形态往往与理论、经验的预测结果之间存在较大的差异,正确解读这种差异性,分析其隐含的物理、力学意义,对信息化施工控制意义重大。鉴于此,根据大量实测数据以及盾构施工力学原理,归纳提出几种常见的典型沉降曲线模式,并将其形态特征与施工工况条件对应起来,可以将实测地表沉降数据的变化及时、科学地解读出来并指导施工合理进行。此外,由于施工数据与监测数据量巨大且离散性大,人工模式识别需要实时在线且经验丰富的专家,且存在耗时长、工作量大等缺点,针对这种情况,本文建立的计算机模式识别和远程数据传输可有效解决该问题,且经过工程实例验证是可行的。
Surface subsidence is an important indicator of shield construction quality and environmental control. However, due to the complexity and uncertainty of the specific construction parameters, geological conditions and surface boundary conditions, the measured surface subsidence size and its distribution patterns often differ greatly from the theoretical and empirical predictions, and the correct interpretation of this difference , Analysis of its implied physical and mechanical significance of the construction of information control is of great significance. In view of this, according to a large number of measured data and the theory of shield construction mechanics, several typical typical settlement curve patterns are summarized and their morphological characteristics are correlated with construction condition conditions. The changes of measured ground settlement data can be timely and scientifically Interpretation and guide the construction to proceed properly. In addition, due to the huge amount of construction data and monitoring data and large discreteness, artificial pattern recognition needs real-time online and experienced experts, and has the disadvantages of time consuming and heavy workload. In view of this situation, the computer pattern recognition and Remote data transmission can effectively solve the problem, and verified by engineering examples is feasible.