Adaptive multi-modal data fusion bridge health monitoring method and system

By employing nonlinear dynamics modeling, adaptive deep learning, and multimodal data fusion technologies, combined with edge computing and cloud platforms, we have achieved accurate, real-time, and comprehensive intelligent assessment of bridge health monitoring. This solves the problems of monitoring accuracy and real-time performance in existing technologies, and improves the efficiency and safety of bridge management.

CN122174608APending Publication Date: 2026-06-09GUANGXI NEW DEV TRANSPORT GRP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUANGXI NEW DEV TRANSPORT GRP CO LTD
Filing Date
2026-01-28
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing bridge health monitoring technologies have limitations in handling nonlinear responses of bridges, fusion of multiple data sources, real-time performance, and intelligent decision-making. This results in low monitoring accuracy, poor real-time performance, and insufficient intelligence in complex environments and extreme working conditions. Furthermore, the data transmission latency and security issues of cloud platforms have not been effectively resolved.

Method used

By employing nonlinear dynamics modeling, adaptive deep learning algorithms, multimodal data fusion, intelligent feedback control and early warning mechanisms, edge computing and cloud platform technologies, combined with artificial intelligence-assisted decision-making, we can achieve accurate, real-time and comprehensive monitoring and assessment of bridge health.

Benefits of technology

It improves the accuracy, intelligence, and real-time response capabilities of bridge health monitoring, enabling accurate identification of damage in complex environments, reducing data transmission delays, providing intelligent decision support, and ensuring the safety and stability of bridge structures.

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Abstract

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.
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