Expressway differential settlement prediction and regulation system based on machine learning

By integrating multi-source data and physical constraints through a machine learning system, high-precision differential settlement prediction and control of highways in complex soft soil environments has been achieved. This solves the problems of insufficient prediction accuracy and lack of global optimization in existing technologies, thereby improving engineering quality and safety.

CN121809778AActive Publication Date: 2026-04-07NO 6 ENGINEERING CO LTD OF FHEC OF CCCC +1
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Patent Information

Application Number
CN202610267511.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-06
Publication Date
2026-04-07
Estimated Expiration
2046-03-06

AI Technical Summary

Technical Problem

In complex soft soil environments, existing technologies struggle to achieve high-precision prediction of differential settlement on highways, and the lack of a global optimization mechanism for construction control makes it difficult to guarantee project quality and operational safety.

Method used

A system based on machine learning, multi-source data fusion acquisition, spatiotemporal feature engineering, physical constraint spatiotemporal graph network prediction, and reinforcement learning inverse control is constructed by combining sensor data, geological information, and physical constraints to build an intelligent closed-loop system for settlement prediction and control.

Benefits of technology

It improves the accuracy of settlement prediction and the efficiency of construction control, realizes high-precision settlement control in complex soft soil environments, and optimizes the balance between construction period, cost and settlement control effect.

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Abstract

The invention discloses an expressway differential settlement prediction and regulation system based on machine learning, and belongs to the technical field of intelligent traffic infrastructure construction and maintenance. According to the system, a closed-loop system including multi-source data fusion acquisition, spatial-temporal characteristic engineering, physical constraint spatial-temporal diagram network prediction, reinforcement learning reverse regulation and control and visual decision early warning is constructed for solving the problem of differential settlement control in alluvial plain deep and thick soft soil area highway reconstruction and extension projects. According to the system, Biot consolidation theory constraints are embedded in a geological weighted graph network, so that the problems of over-fitting and physical consistency deficiency of a pure data driven model under complex geological conditions are effectively solved; meanwhile, the reinforcement learning strategy network is used for replacing artificial experience, and global optimization regulation and control of construction parameters are achieved on the premise that the differential settlement control requirement is met. The method is mainly used for precise settlement prediction and intelligent construction management of highway reconstruction and extension projects.
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