Construction Composition Quality Control With AI Batch Adjustment
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Solution Overview
Problem
Construction compositions such as asphalt, concrete, and mortar often require over-engineering due to variability in raw materials, mixing techniques, and site conditions, leading to inconsistent performance and unnecessary material waste, as existing methods struggle to account for diverse inputs and changing conditions effectively.
Innovation Solution
A predictive model and machine learning algorithm are used to optimize construction composition formulations by analyzing job specifications, environmental conditions, and real-time data, allowing for adjustments to the construction mixture and admixture to ensure consistent performance and reduced material usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction compositions are over-engineered to meet minimum performance requirements, then reliability is improved, but loss of substance and cost increase
Solution Approach 1:
The system dynamically adjusts formulation parameters (admixture types and dosages) based on real-time sensor data from raw materials and environmental conditions. This allows optimization of each batch's composition 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 measuring actual raw material properties with sensors, comparing them against target specifications, and automatically adjusting admixture dosages 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
Solution Approach 1:
The system uses universal sensor types and a standardized machine learning model that can be deployed across multiple plants with different equipment. The model learns to account for plant-specific variations in raw materials and processing, enabling consistent performance outcomes across diverse manufacturing environments while maintaining each plant's operational flexibility.
Solution Approach 2:
The machine learning model acts as an intermediary that translates varying raw material properties and environmental conditions into standardized admixture adjustments. This intermediary layer harmonizes differences between plants, ensuring consistent composition performance despite variations in raw materials and manufacturing techniques.
3Device complexity
If traditional quality control methods are used without real-time data, then device complexity is reduced, but measurement precision and response to changing conditions worsen
Solution Approach 1:
The system replaces complex manual quality control procedures with automated sensor measurements and machine learning-based formulation adjustments. This substitution maintains operational simplicity while dramatically improving measurement precision and the ability to respond to changing raw material properties and environmental conditions.
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.