Optimization Analysis of Stability and Deformation Control Methods for Deep Excavation Support Structures Based on Field Measurements

Ke Yan

Simen Owen Academic Proceedings Series, 2026, vol. 3, 226-235

Abstract: Deep excavation support systems in urban environments present demanding engineering challenges due to their inherent stability risks and potential for inducing detrimental deformations in adjacent infrastructure. Conventional design methodologies often inadequately address the complex soil-structure interaction dynamics and temporal construction effects, leading to either excessive conservatism or unforeseen performance issues. This study develops an integrated optimization framework that synergistically combines high-frequency field instrumentation data with advanced computational modeling to enhance the stability and deformation control of deep excavation support structures. The proposed methodology employs a physics-informed Bayesian calibration approach to continuously update finite element models using real-time measurements from inclinometers, strain gauges, and piezometers. A multi-objective optimization algorithm subsequently identifies optimal support configurations that simultaneously maximize stability margins, minimize deformation, and reduce material costs. Validation through a major metropolitan excavation case study demonstrates that this field measurement-driven approach achieves significant improvements in deformation control while maintaining structural integrity. The framework's ability to adaptively refine support designs during construction phases offers substantial advancements over static design paradigms. By transforming conventional excavation support into a responsive, data-informed process, this research provides a foundation for intelligent infrastructure development in spatially constrained urban environments.

Keywords: deep excavation; support structure optimization; field instrumentation; deformation control; Bayesian calibration; adaptive design (search for similar items in EconPapers)
Date: 2026
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