大口径桩基施工监控与预警综合系统研究

Research on integrated monitoring and early warning system for large-diameter pile foundation construction

摘要:

针对大口径桩基施工存在参数控制依赖经验、监测数据分散、电磁干扰易产生野值，进而导致预警滞后及施工状态缺乏实时量化表征等问题，提出一种集状态监测、风险预警、基于模糊 PID 的参数自适应调节与超限紧急停机有限闭环控制于一体的综合系统。系统基于物联网分层架构构建，集成桩机姿态、动力、钻进、环境及泥浆液位、泥浆密度传感单元，配套基于红外测距与红外对射判定的非接触式孔深监测单元与智能监控平台，建立面向桩基施工场景的两级数据融合方法、基于综合风险指数R的分级预警模型（权重由层次分析法确定）和钻进参数自适应优化策略。现场试验表明：以人工测绳为对照，孔深监测平均误差为±6.2 mm；泥浆液位报警响应时间＜2.1 s；以实际风险事件为对照，预警准确率达到92.3%；相比人工经验操作，平均单桩成孔时间缩短约18%。研究成果可为复杂地层条件下大口径桩基施工的智能化决策、精细化操作与安全施工提供技术支撑。

Abstract:

To address the problems in large-diameter pile foundation construction， such as experience-dependent parameter control， scattered monitoring data， and outliers easily caused by electromagnetic interference， which further lead to delayed early warning and lack of real-time quantitative characterization of construction status， an integrated system is proposed. This system combines condition monitoring， risk warning， fuzzy PID-based parameter adaptive regulation， and limited closed-loop control for over-limit emergency shutdown. Constructed upon a layered Internet of Things （IoT） architecture， the system integrates sensing units for rig attitude， power systems， drilling parameters， environmental conditions， mud level， and mud density. It is equipped with a non-contact hole depth monitoring unit based on infrared ranging and infrared mutual beam detection， along with an intelligent monitoring platform. A two-level data fusion method， a graded early warning model based on a comprehensive risk index R （with weights determined by the analytic hierarchy process）， and an adaptive optimization strategy for drilling parameters are established for pile foundation construction scenarios. Field test results indicate that， compared with manual sounding rope measurements， the average error of hole depth monitoring is ±6.2 mm； the response time for mud level alarms is less than 2.1 s； the early warning accuracy reaches 92.3% when validated against actual risk events； and the average hole completion time per pile is reduced by approximately 18% compared to manual empirical operation. The research results can provide technical support for intelligent decision-making， refined operation， and safe construction of large-diameter pile foundations under complex geological conditions.