Soft Soil Pile Foundation Early Warning With Meta-Learning
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Solution Overview
Problem
Traditional methods for monitoring and predicting the attenuation of bearing capacity in pile foundations are passive and cannot effectively predict future trends, while finite element analysis is resource-intensive and difficult to transfer between projects.
Innovation Solution
A meta-learning strategy using a loop loading test and a bidirectional echo state network, combined with a Reptile meta-learning algorithm, to construct an intelligent early warning model that can quickly adapt to new scenarios with minimal field data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If finite element analysis method is used to simulate pile-soil interaction and predict bearing capacity attenuation, then prediction capability is improved, but computational time and resource consumption increase significantly
Solution Approach 1:
The patent creates a simplified copy of the complex finite element model by training an artificial neural network on finite element analysis results. The trained network serves as a lightweight surrogate model that reproduces prediction results without requiring repeated complex simulations, thus resolving the contradiction between prediction accuracy and computational efficiency
Solution Approach 2:
The patent performs comprehensive finite element analysis and model training in advance during the offline phase. The pre-trained neural network model is then deployed for rapid predictions during the online phase, eliminating the need for real-time complex computations and achieving fast predictions without sacrificing accuracy
2Measurement precision
If finite element model is established for specific project with accurate geological model and material relationships, then prediction accuracy is improved, but model transferability to other projects deteriorates
Solution Approach 1:
The patent develops a universal neural network model architecture that can be trained on data from one project and applied to predict bearing capacity attenuation in other projects with different geological conditions. The model learns generalizable patterns from training data, enabling it to function across multiple projects rather than being project-specific
Solution Approach 2:
The patent enables the model to adapt to different projects by changing input parameters such as geological properties, pile dimensions, and loading conditions. The neural network accepts these varying parameters as inputs and produces appropriate predictions for each specific project context while maintaining the same underlying model structure
3Measurement precision
If traditional settlement observation and stress-strain monitoring methods are used, then real-time monitoring capability is improved, but ability to predict future trends and implement early warning deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the neural network model continuously learns from real-time monitoring data and adjusts its predictions accordingly. The system compares predicted attenuation trends with actual measurements and refines its model, enabling both real-time monitoring and predictive early warning capabilities to coexist
Data Source
AI summary
The present disclosure relates to a meta-learning strategy based method for early warning of attenuation of a bearing capacity of a soft soil pile foundation. The method includes: S1, building a loop loading test device; S2, setting up a data set; S3, building and training a base model; S4, introducing a meta-learning strategy into the base model obtained in S3, inputting a pre-training data set for pre-training, and obtaining a pre-trained model; and S5, inputting a target data set into the pre-training data set for pre-training, and obtaining a model prediction result. The method constructs an intelligent early warning model with fast adaptability, such that the intelligent early warning model can make full use of existing engineering experience.