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出 处:《地下空间与工程学报》2012年第4期777-784,846,共9页Chinese Journal of Underground Space and Engineering
基 金:国家自然科学基金项目(51078292);上海市科委2009年科技攻关项目(09231200200);上海市科委2010年科技攻关项目(10231200200)
摘 要: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。Ground settlement is an important indicator for construction quality control, environmental impacts, and also for evaluation of TBM tunneling parameters. However, because of the complexity and variation of specific construction methods, geological conditions and surface boundary conditions, differences always exist between theoret- ically predicted settlement curves and field measured ones. So analysing these differences and making out the poten- tial mechanism are of great significance for the informational tunneling construction. This paper focuses on relating typical characteristics of settlement curves with mechanical procedures of TBM tunneling along with geological and boundary conditions, establishing corresponding surface settlement patterns by explicit interpretation of field measured data, in order to optimize the tunneling parameters. Moreover, in reality, due to the large volume, discrepancy and discreteness of measured data, sufficient experienced experts and qualified staff are usually required on site and a great deal of time spent to process the data in time. So a method and a procedure for pattern-recognition based on the theory of fuzzy mathematics are established for automatic data processing on site, to realize the quick response to risks indicated by the variation of field data, which has been proved practicable in actual projects.
分 类 号:U455.3[建筑科学—桥梁与隧道工程]
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