Concrete Mixture Optimization for Robust Extrapolation
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Solution Overview
Problem
Existing machine learning models struggle with overfitting and lack robustness in predicting concrete mixtures, leading to inaccurate predictions for data that differs from training data, and conventional optimization techniques face challenges like getting stuck in local minima and inefficiencies due to large search spaces, resulting in memory issues and suboptimal recommendations.
Innovation Solution
Implementing machine learning algorithms that cluster data, optimize concrete mixtures using robust optimization functions, and consider confidence intervals to balance performance and uncertainty, while generating batches to avoid memory issues and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are used for concrete mixture predictions, then interpolated predictions are accurate, but extrapolated predictions suffer from overfitting and lack robustness
Solution Approach 1:
The patent transforms the machine learning approach by changing from standard training methods to a transformation-based methodology where input data is converted into transformed spaces (e.g., using coordinate transformations or feature space mappings). This allows the model to learn invariant relationships that generalize better to unseen data distributions, improving both accuracy and robustness simultaneously.
Solution Approach 2:
The patent introduces additional dimensional transformations by mapping input features into higher-dimensional spaces or alternative coordinate systems. This dimensional change enables the model to capture complex relationships that are not apparent in the original feature space, thereby improving extrapolation capability and reducing overfitting.
2Productivity
If conventional optimization techniques are used to search for optimal concrete mixtures, then comprehensive search is performed, but the process gets stuck in local minima and is inefficient due to large search spaces
Solution Approach 1:
The patent replaces traditional mechanical optimization algorithms (like gradient descent or evolutionary algorithms) with a transformation-based prediction system. Instead of iteratively searching through the solution space using optimization mechanics, the system transforms the input parameters and directly predicts optimal mixtures, eliminating the need for iterative search and avoiding local minima traps.
Solution Approach 2:
The patent performs preliminary transformations on the input data and pre-computes relationship mappings before the actual optimization query is made. By pre-processing the feature spaces and establishing transformation rules in advance, the system能够快速响应 optimization requests without performing time-consuming iterative searches during query time.
3Measurement precision
If the machine learning model processes all candidate concrete mixtures, then comprehensive evaluation is achieved, but memory issues occur due to large data volumes
Solution Approach 1:
The patent extracts and processes only the essential features and transformed representations of candidate mixtures rather than processing complete raw datasets. By extracting key predictive features and working with transformed compact representations, the system maintains evaluation completeness while significantly reducing memory consumption.
Solution Approach 2:
The patent segments the large dataset of candidate concrete mixtures into smaller batches or groups that can be processed independently. The transformation-based approach allows processing of segmented data while maintaining the ability to evaluate all candidates comprehensively by aggregating results from each segment.
Data Source
AI summary
Artificial intelligence and machine learning models are used to make concrete-related predictions. Many permutations of concrete mixtures are generated. Machine learning algorithms are used to evaluate and recommend a generated concrete mixture based on a set of specifications. The generated concrete mixture can be sent to a plant for production. The actual concrete mixture that was used to manufacture the concrete product can be received from the manufacturer. An amount of emission reductions and/or cost savings can be determined from the actual as-batched concrete mixture and an associated reference concrete mixture. The real-world data are used to train the machine learning models.