Machine Learning Concrete Mix Optimization
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Solution Overview
Problem
The cement and concrete industry faces challenges in optimizing concrete formulations due to limited raw materials and high carbon emissions, necessitating innovative methods for developing cost-effective, durable, and sustainable solutions.
Innovation Solution
A machine learning-based method that utilizes customer data, proprietary datasets, and specific machine learning techniques to optimize concrete mixtures in real-time, considering worksite context parameters, and employs historical data sets to generate and validate models for predicting optimal concrete mixing parameters, such as resistivity and compression test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional concrete formulations are used, then construction needs are met, but carbon emissions are high and raw materials are depleted
Solution Approach 1:
The patent applies parameter changes by systematically varying concrete formulation parameters (cement content, admixture types and dosages, supplementary materials) to find optimal mixes that reduce carbon emissions while maintaining performance. The machine learning models analyze how changes in these parameters affect both environmental impact and concrete properties, enabling optimization of the trade-off between sustainability and reliability.
Solution Approach 2:
The patent employs composite materials by combining multiple cementitious materials (Portland cement, fly ash, slag, silica fume) and various admixtures in optimized proportions. This composite approach allows the concrete to achieve desired performance characteristics while reducing the carbon-intensive Portland cement content, thus lowering emissions without sacrificing reliability.
2Strength
If more raw materials are used, then concrete strength is improved, but material costs and environmental impact increase
Solution Approach 1:
The patent uses parameter changes to optimize material quantities by precisely controlling the proportions of each component in the concrete mix. The machine learning models identify the minimum necessary amounts of each material required to achieve target strength levels, eliminating excess material usage while maintaining or improving strength performance.
Solution Approach 2:
The patent employs computational copying by using machine learning models trained on historical data to predict optimal formulations without requiring extensive physical experimentation. The models replicate the relationship between material composition and strength outcomes, enabling virtual optimization that reduces the need for trial-and-error material consumption.
3Manufacturing precision
If extensive testing is performed, then formulation accuracy is improved, but time and resource consumption increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive historical formulation and performance data before actual concrete development. This preliminary computational work builds a knowledge base that accelerates future formulation optimization, reducing the need for extensive new testing while maintaining high accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where test results from laboratory trials are fed back into the machine learning models to refine and update formulation predictions. This closed-loop approach continuously improves formulation accuracy while reducing the number of iterations needed, as the system learns from each test and makes more accurate predictions subsequently.
Data Source
AI summary
A method comprising: pulling a set of customer data and augment it with data sets designed to optimized machine learning operations; Selecting specified machine learning techniques from a specified machine learning database; taking into account a set of worksite context parameters; and optimizing and adjusting a concrete mix in real time to be able to deliver concrete products that meet requirements of project.