Concrete life prediction method and system based on dynamic game and evolutionary calibration

By combining a multimodal intelligent sensor array with a dynamic competition model, the problem of dynamic changes in biological factors in concrete life prediction is solved, resulting in more accurate life prediction and improved adaptability and stability.

CN122436084APending Publication Date: 2026-07-21SHANDONG JIANZHU UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG JIANZHU UNIV
Filing Date
2026-04-27
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing methods for predicting the lifespan of concrete cannot accurately capture the dynamic changes in biological factors, leading to overestimation or underestimation of the remaining lifespan of structures under long-term working conditions, thus affecting the accuracy of the assessment.

Method used

A multimodal intelligent sensor array is used to collect data. A dynamic competitive model is constructed by combining a digital twin algorithm and an improved Lotka-Volterra equation. The model bias is evaluated by a Bayesian recursive filtering module, and a swarm intelligence evolution compensation mechanism is triggered to calibrate the parameters.

Benefits of technology

It significantly improves the accuracy and long-term stability of predicting the degradation evolution trend and remaining life of concrete under biological, physical and chemical erosion environments, and enhances its adaptability to biological erosion processes.

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Abstract

The application provides a concrete life prediction method and system based on dynamic game and evolution calibration, comprising: using initial service environment data, adopting a digital twin algorithm to construct a micro simulation scene, and introducing a dynamic competition model; comparing the measured degradation feedback data with the theoretical acid production data output by the dynamic competition model in the state space, calculating the residual energy representing the prediction deviation degree of the dynamic competition model; judging whether the residual energy meets the preset evolution threshold condition; if yes, triggering the group intelligence evolution compensation mechanism to calibrate the game parameters in the dynamic competition model; based on the calibrated game parameters, generating a concrete degradation evolution trajectory, and outputting a residual life prediction result. The technical scheme of the application can realize dynamic modeling and continuous correction of the concrete degradation process under the conditions of complex service environment and microbial evolution uncertainty, and improve the accuracy and reliability of the residual life prediction.
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