AI Concrete Mix Quality Control for Batch Consistency
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Solution Overview
Problem
Construction compositions, such as concrete, asphalt, and mortar, are often over-engineered due to the inability to account for varying raw materials, mixing techniques, and environmental conditions at different production facilities and job sites, leading to inefficiencies and unnecessary waste.
Innovation Solution
A predictive model and machine learning algorithm are used to optimize construction compositions by considering historical data, real-time sensor inputs, and job specifications to determine optimal formulations for construction admixtures, allowing for consistent performance and reduced material usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction compositions are over-engineered to meet performance requirements, then reliability is improved, but loss of substance increases due to unnecessary material waste
Solution Approach 1:
The system dynamically adjusts formulation parameters (admixture ratios, component proportions) based on real-time sensor data from raw materials and environmental conditions. This allows optimization of each batch to meet minimum performance requirements without excessive material usage, resolving the contradiction between reliability and material waste.
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring raw material properties (moisture, temperature, composition) and environmental conditions, then adjusting the formulation parameters accordingly. This feedback mechanism ensures consistent performance while minimizing material waste by avoiding over-engineering.
2Adaptability or versatility
If different plants produce construction compositions with varying raw materials and techniques, then adaptability is improved, but manufacturing precision deteriorates due to inconsistency
Solution Approach 1:
The system uses a universal predictive model and sensor framework that can be deployed at any production plant regardless of specific raw materials or techniques. This universal approach ensures consistent formulation optimization across different plants, maintaining manufacturing precision while allowing adaptability to local conditions.
Solution Approach 2:
The system performs preliminary analysis of raw material properties and environmental conditions before production, using predictive models to determine optimal formulation parameters in advance. This preliminary action ensures consistent results across different plants by pre-calculating adjustments needed for varying materials and conditions.
3Device complexity
If traditional quality control methods are used without real-time data, then device complexity is reduced, but measurement precision deteriorates due to inability to account for varying conditions
Solution Approach 1:
The system replaces traditional mechanical quality control methods with sensor-based measurement and predictive modeling. Sensors automatically measure raw material properties and environmental conditions, substituting manual testing with automated digital measurement, thereby improving precision without significantly increasing operational complexity.
Data Source
AI summary
Example embodiments provide systems and methods for performing quality control of a construction composition. According to exemplary embodiments, a predictive model, artificial intelligence, machine learning algorithm, etc., may be trained using historical performance data and current deployment information. Based on a job specification that identifies various requirements for the construction composition and a set of available inputs, the AI/ML/model may output one or more formulations that meet or best approximate the requirements, and an initial batch of the construction composition may be produced. During or after deployment of the construction composition, information about the composition's performance may be received and applied to the AI/ML/model. The system may make real-time updates to the construction composition to improve the consistency or performance of the construction composition, within predefined acceptable change parameters. Optionally, the system may control mixing machinery to produce the updated construction composition.