A method, system and device for dynamic monitoring of subgrade modulus of resilience

By constructing various test conditions and nonlinear constitutive models, and combining finite element simulation and ensemble learning, the error problem caused by soil nonlinearity and local heterogeneity in traditional roadbed resilient modulus monitoring was solved, and real-time and accurate evaluation of roadbed resilient modulus was achieved.

CN121997781BActive Publication Date: 2026-07-24TONGJI UNIV
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Patent Information

Application Number
CN202610466743.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-10
Publication Date
2026-07-24
Estimated Expiration
2046-04-10

AI Technical Summary

Technical Problem

Traditional methods for monitoring the resilient modulus of subgrade fail to fully consider the nonlinearity and local heterogeneity of the soil, resulting in inaccurate monitoring and affecting the accuracy of compaction quality assessment.

Method used

By controlling the physical and mechanical environmental parameters of the soil, various test conditions are constructed, hysteresis curves are generated, the initial dynamic elastic modulus is corrected, a nonlinear constitutive model is established, and the roadbed resilient modulus is predicted in real time by combining finite element simulation and ensemble learning model.

Benefits of technology

It enables real-time, non-destructive, full-section intelligent sensing and visualization assessment of the roadbed rebound modulus, improving monitoring accuracy and the real-time performance of compaction quality assessment.

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Abstract

The application relates to the technical field of resilience modulus monitoring, in particular to a dynamic monitoring method, system and equipment for resilience modulus of a roadbed, which comprises the following steps: for each working condition, preparing multiple groups of soil sample, respectively performing multistage loading dynamic triaxial tests on the soil sample, and generating hysteresis curves of each strain level; obtaining discrete coefficients of each soil sample at each strain level, correcting initial dynamic elastic modulus extracted from the hysteresis curves, and establishing a nonlinear constitutive model responding to state changes of the soil; generating a simulation data set containing multiple characteristic vectors and resilience modulus thereof, and using the simulation data set to train an integrated learning model; and at a roadbed construction site, using the integrated learning model to predict the resilience modulus according to the obtained characteristic vectors, and evaluating the compaction quality of the roadbed. The application improves the monitoring accuracy of the resilience modulus of the roadbed in real time.
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