Construction Admixture Formulation Using AI and Real-Time Feedback
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Solution Overview
Problem
Construction compositions are often over-engineered due to the inability to account for the inability to account for the inability to account for the inability to account for the wide variety of available inputs, rapidly-changing conditions at the plant and the construction site, and other factors that might affect the performance of the construction mixture, leading to inefficiencies and unnecessary waste.
Innovation Solution
A predictive model, artificial intelligence, and machine learning algorithm are used to formulate and evaluate construction admixtures, considering historical performance data, real-time sensor readings, and job-specific requirements to optimize construction compositions, allowing for real-time adjustments and simulations to ensure compliance with engineering specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction compositions are over-engineered to meet minimum engineering requirements, then reliability is improved, but loss of substance and cost increase
Solution Approach 1:
The system dynamically adjusts formulation parameters (admixture types, dosages, combinations) based on real-time sensor data and simulated conditions. This allows optimization of raw material usage while maintaining compliance with engineering requirements, eliminating the need for consistent over-engineering across all production scenarios.
Solution Approach 2:
The system incorporates feedback loops where sensor readings from actual construction conditions are fed back into the formulation model. This enables continuous refinement of admixture formulations to achieve minimum required performance with optimized material usage, rather than applying fixed over-engineered formulations universally.
2Manufacturing precision
If traditional trial-and-error methods are used to develop construction admixtures, then manufacturing precision can be achieved, but productivity decreases
Solution Approach 1:
The system creates virtual copies of physical testing scenarios through computer simulations. These digital twins allow formulation developers to test and evaluate admixture performance in silico before physical production, dramatically reducing the number of physical trials needed while maintaining formulation accuracy.
Solution Approach 2:
The system performs preliminary computational analysis and simulation of admixture formulations before physical manufacturing. By pre-evaluating multiple formulation options through algorithms and simulations, the system identifies optimal formulations in advance, reducing iterative physical testing and accelerating development timelines.
3Productivity
If multiple batches are produced at different plants with varying conditions, then productivity increases, but consistency of construction composition deteriorates
Solution Approach 1:
The system provides a universal formulation platform that can be deployed across multiple plants with varying conditions. The adaptive algorithm adjusts to local sensor readings and environmental factors at each plant, maintaining consistent performance outcomes despite differences in production locations, equipment, and ambient conditions.
Solution Approach 2:
The formulation system is dynamically adaptive rather than static. It continuously adjusts admixture formulations based on real-time sensor data from each plant's specific conditions, allowing multiple plants to produce consistent quality compositions despite variations in equipment, environment, and operational parameters.
4Manufacturing precision
If extensive experimentation is conducted to optimize construction compositions, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The system replaces extensive physical experimentation with computational simulations and digital modeling. Virtual experiments allow rapid evaluation of numerous formulation variables and scenarios without the time-consuming nature of physical trial-and-error testing, while maintaining optimization accuracy.
Solution Approach 2:
The system performs preliminary computational optimization and sensitivity analysis before physical experimentation. By pre-identifying critical parameters and optimal formulation ranges through algorithms, the system reduces the scope and duration of required physical experiments while maintaining precision.
Data Source
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AI summary
Example embodiments provide systems and methods for formulating and evaluating a construction admixture (such as an admixture for concrete, asphalt, mortar, etc.). 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 (e.g., raw materials, mixing techniques, etc.), the model, AI, or algorithm, may output one or more construction admixtures that meet or best approximate the requirements. The construction admixtures may be provided to a simulation to estimate or predict their performance. The performance characteristics of the output construction admixture(s) may be displayed. Optionally, the system may control mixing machinery to produce the construction admixture. Some embodiments may use these capabilities to evaluate an existing or proposed construction admixture, rather than proposing a new construction admixture.