Early warning method for erosion damage of concrete member influenced by multiple factors in salt-fresh water area

By collecting data in real time through a sensor network in the brackish water confluence area, and combining tensor column block decomposition and heterogeneous graph neural network, the corrosion process of concrete components can be dynamically predicted. This solves the problem of the difficulty in dynamically collecting and modeling environmental factors in the brackish water confluence area in the existing technology, and realizes efficient corrosion early warning and accurate protection decision-making.

CN121838431APending Publication Date: 2026-04-10CCCC FOURTH HARBOR ENG CO LTD +1
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Patent Information

Application Number
CN202610168803.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-05
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

Existing methods are unable to comprehensively and dynamically collect complex and ever-changing environmental factors in brackish water confluence areas, lack the ability to deeply mine environmental data and model high-dimensional features, cannot effectively extract potential destructive driving factors, struggle to characterize the spatial coupling and temporal evolution of corrosion processes, and lack refined early warning mechanisms.

Method used

By deploying a sensor network in the brackish water confluence area to collect environmental parameters of concrete components in real time, an original data matrix of environmental corrosion is constructed. Tensor column block decomposition and multimodal extraction algorithms are used to construct a feature map of perturbation factors. Corrosion evolution prediction is performed using heterogeneous graph neural networks and dynamic Bayesian networks. In combination with Monte Carlo simulation, a multi-level early warning indicator set is generated, triggering graded alarms and generating protection decision schemes.

Benefits of technology

It enables dynamic sensing and efficient early warning of corrosion of concrete components in brackish water areas, improving the timeliness, accuracy and interpretability of early warning, and supporting precise life cycle management and customized protection decisions.

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Abstract

The invention relates to the field of monitoring and early warning, in particular to a concrete member erosion damage early warning method influenced by multiple factors in a salt and fresh water area. Comprising the following steps: collecting environmental parameters of a concrete member in a salt-fresh water intersection area through a sensor network, constructing a corrosion data matrix, extracting a disturbance factor characteristic spectrum by adopting a tensor column block decomposition algorithm, and constructing a corrosion evolution trajectory model based on a heterogeneous graph neural network. A dynamic Bayesian network and Monte Carlo simulation are combined to predict a multi-stage corrosion risk, short-term, medium-term and long-term early warning indexes are generated, finally, graded alarm and protection strategy generation are realized through a fuzzy logic mechanism, and intelligent sensing, modeling and prediction of a corrosion process are realized. According to the method, intelligent sensing, modeling and prediction of the erosion process of the concrete member are realized, and the timeliness, accuracy and interpretability of erosion early warning of the concrete member are remarkably improved.
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