Concrete Mixture Recipe Optimization via Particle Characterization
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Solution Overview
Problem
Concrete production is hindered by material inconsistency due to variations in aggregate ingredients, leading to overuse and inefficiency, as existing methods fail to optimize the use of locally available materials effectively.
Innovation Solution
A system and process that characterizes aggregate particles using optical and mechanical sensors, applies empirical rules and physics simulators to predict mixture performance, and iteratively adjusts recipes to achieve desired properties, utilizing machine learning models and discrete element methods to optimize concrete mix recipes based on particle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional concrete production methods are used with standard safety margins, then structural reliability is ensured, but material overuse occurs and cost increases
Solution Approach 1:
The system changes the parameters of aggregate particles by applying mechanical treatments (crushing, grinding, screening) to modify size distribution, shape, and surface texture. This allows optimization of concrete mix designs to achieve required structural reliability with reduced material quantities, eliminating excessive safety margins while maintaining performance
Solution Approach 2:
The system performs preliminary characterization of aggregate particles using optical and mechanical sensors before concrete mixing. By pre-analyzing particle properties and predicting mixture performance, the system enables precise formulation of concrete recipes that optimize material usage while ensuring structural reliability, avoiding both overuse and underperformance
2Measurement precision
If comprehensive particle characterization is performed using multiple sensors, then prediction accuracy of mixture performance is improved, but system complexity and measurement time increase
Solution Approach 1:
The system segments the particle characterization process into distinct measurement stages: optical characterization (size, shape, surface area) using image analysis, and mechanical characterization (hardness, density, elasticity) using separate sensors. This modular segmentation improves measurement precision while managing system complexity through organized, independent measurement modules
Solution Approach 2:
The system introduces an intermediary computational model that processes sensor data and predicts mixture performance. This intermediary layer translates complex sensor measurements into actionable predictions, improving accuracy while shielding users from the underlying system complexity through a user-friendly interface
3Productivity
If iterative recipe optimization is performed using performance prediction, then material efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary prediction of mixture performance using empirical rules and physics-based models before actual mixing. This preliminary action identifies optimal recipe parameters in advance, enabling rapid iteration and reducing the time required for physical testing and optimization cycles
Solution Approach 2:
The system implements feedback loops where actual mixture performance results are fed back into the prediction models to refine future predictions. This feedback mechanism improves material efficiency over time while reducing processing time through increasingly accurate predictions that require fewer iterative trials
Data Source
AI summary
Methods, systems, and apparatus for generating a recipe for a concrete mixture, comprising: obtaining an optical characterization of a set of particles; determining, based on the optical characterization, physical characteristics of the set of particles; generating a multispherical approximation of the set of particles; selecting, based on the physical characteristics of the set of particles and from a database of performance rules, performance rules applicable to the set of particles; predicting performance of a proposed recipe for a concrete mixture formed from the set of particles by: determining a wet flowability rating of the proposed recipe based on the selected performance rules; and determining a dry packing rating of the proposed recipe based on the multispherical approximation; iteratively altering the proposed recipe and predicting performance of the altered proposed recipe until the predicted performance satisfies performance criteria to obtain a final recipe; and outputting the final recipe.