Concrete Mixture Characterization for Real-Time Cement Control
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Solution Overview
Problem
The manufacturing process of concrete produces significant greenhouse gas emissions due to the high usage of cement, which is environmentally unfriendly and costly, and there are uncertainties regarding the qualities of coarse and fine aggregates, leading to inefficient concrete production.
Innovation Solution
Implementing computer vision and machine learning to monitor parameters of concrete materials and mixtures, such as aggregate moisture, specific gravity, and mixture properties, enabling real-time adjustments of aggregates and water in the concrete mixture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If higher amounts of cement are used in concrete mixture as insurance against quality uncertainties, then reliability of concrete strength is improved, but environmental harm and cost increase
Solution Approach 1:
The system implements real-time feedback by using sensors (moisture sensors, cameras, load cells) to continuously monitor aggregate properties during production. This feedback loop allows the system to adjust cement usage dynamically based on actual aggregate quality, eliminating the need for excessive cement as insurance while maintaining concrete strength reliability.
Solution Approach 2:
The patent replaces traditional mechanical quality control methods with automated sensing and machine learning systems. Moisture sensors, cameras, and other detectors substitute for manual inspection, providing precise real-time data that enables optimal cement dosage without over-compensating for quality uncertainties.
2Reliability
If higher amounts of cement are used in concrete mixture, then reliability of concrete strength is improved, but manufacturing cost increases
Solution Approach 1:
Real-time feedback from moisture sensors and other detectors enables precise control of aggregate properties, allowing the system to optimize cement dosage dynamically. This eliminates wasteful overuse of cement while maintaining concrete strength, directly reducing production costs.
Solution Approach 2:
The system changes key parameters (moisture content, particle size distribution, packing density) in real-time through automated monitoring and adjustment. By controlling these parameters precisely, the system achieves consistent concrete quality with optimized cement usage, reducing material costs.
3Device complexity
If traditional concrete production process is used without real-time monitoring, then device complexity is minimized, but manufacturing precision deteriorates
Solution Approach 1:
The system enables self-service quality control where the production process automatically monitors and adjusts its own parameters. Sensors and detectors continuously measure aggregate properties, and the system automatically adjusts mixing parameters without external intervention, achieving high precision while adding minimal operational complexity.
Solution Approach 2:
Traditional manual quality control methods are replaced with automated sensing systems. Moisture sensors, cameras, and load cells automatically detect and measure aggregate properties, substituting for complex human judgment and manual adjustment processes while improving precision.
4Productivity
If real-time monitoring and adjustment systems are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The monitoring system is segmented into independent functional modules (moisture sensing, visual inspection, weight measurement, control algorithms). This modular architecture improves productivity through specialized functions while managing complexity by allowing independent development and maintenance of each component.
Solution Approach 2:
The system employs multi-functional sensors and detectors that perform multiple measurement tasks simultaneously. For example, the sensing system monitors moisture content, particle size, and packing density using integrated sensors, reducing the number of separate devices needed while maintaining high productivity.
Data Source
AI summary
Systems, methods, and computer-readable media of characterizing concrete materials or concrete mixtures may include: obtaining, using sensors that comprise at least two different types of sensors, sensor data corresponding to the concrete materials or the concrete mixtures; analyzing, using a trained machine learning model, the sensor data to generate the characterization of the concrete materials or the concrete mixture; and outputting the characterization of the concrete materials or the concrete mixture. Training the trained machine learning model may include: obtaining a set of training data for historical concrete materials or a historical concrete mixture, including a plurality of characterizations of the historical concrete materials or the historical concrete mixture; classifying the set of training data into a plurality of subsets each corresponding to a different characterization or range of characterizations of the plurality of characterizations; and generating the trained machine learning model using the plurality of subsets of training data.