Research on Key Technologies and Safety Warning of Deep Excavation Support Structures Based on Multi Source Sensing and Real Time Data Fusion

Abstract

Deep excavation support structures exhibit nonlinear and spatiotemporally coupled deformation under complex geological conditions and construction disturbances, making single-sensor monitoring insufficient for real-time safety warning. This study proposes a multi-source sensing and real-time data fusion method for safety early warning of deep foundation pit support structures. A heterogeneous sensor network composed of strain gauges, inclinometers, axial force gauges, and hydrostatic levels is deployed to monitor pile bending moment, horizontal displacement, support axial force, and surface settlement. Sensor streams with different sampling rates are temporally aligned by cubic spline interpolation and spatially mapped onto a unified support-pile profile. Outliers are removed using the Pauta criterion, and adaptive inverse-variance weighting is combined with Kalman filtering to obtain robust fused feature sequences. An improved grey relational analysis model with exponential time weighting and dynamic thresholds is then used to identify typical failure modes and issue three-level warnings. Field experiments using 30 days of monitoring data show that the method achieves 98.3% effective data retention, 20.3 dB signal-to-noise ratio, RMSE of 0.34 mm, warning response time of 4.2 s, and false alarm rate of 8.6%. The method improves structural sensing reliability and real-time safety warning capability.