AI Formulation of Construction Compositions Under Variable Inputs
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Solution Overview
Problem
Construction compositions are often over-engineered due to the inability to account for varying raw materials, mixing techniques, and environmental conditions, leading to inefficiencies and unnecessary waste of resources.
Innovation Solution
A predictive model using artificial intelligence and machine learning algorithms is employed to optimize construction compositions by considering a wide range of inputs, including raw materials, mixing techniques, and environmental conditions, to produce compositions that meet performance requirements while minimizing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If construction compositions are over-engineered to ensure meeting performance requirements under varying conditions, then reliability is improved, but loss of substance increases due to unnecessary waste of raw materials
Solution Approach 1:
The system dynamically adjusts formulation parameters (raw material ratios, admixture types and amounts) based on real-time inputs including available materials, environmental conditions, and performance requirements. This allows optimization of each batch to meet minimum performance thresholds without excessive material usage.
Solution Approach 2:
The system incorporates feedback loops that monitor actual performance results and use them to refine future formulations. By learning from historical data and actual outcomes, the system continuously improves its ability to predict optimal formulations that balance performance requirements with material efficiency.
2Adaptability or versatility
If multiple plants contribute to a project with different raw materials and conditions, then adaptability is improved, but manufacturing precision deteriorates due to variability in construction composition properties
Solution Approach 1:
The system is designed to work with multiple types of raw materials from different sources and can adapt to various environmental conditions. It maintains a database of material properties and uses this information to adjust formulations across different plants, ensuring consistent performance outcomes despite variations in input materials.
Solution Approach 2:
The system tailors each formulation to local conditions at each plant, including available raw materials, environmental factors, and specific project requirements. By optimizing formulations locally rather than using a single standardized recipe, the system achieves both adaptability to local conditions and consistency in meeting performance requirements.
3Manufacturing precision
If extensive experimentation is conducted to optimize construction compositions, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary computational analysis and predictions before actual production. By using machine learning models and historical data to predict optimal formulations in advance, the system reduces the need for extensive physical experimentation and accelerates the formulation development process.
Solution Approach 2:
The system uses virtual modeling and simulation to replicate experimentation conditions computationally. By creating digital twins of formulation processes and running simulations, the system can evaluate multiple formulation options quickly without the time and resource costs of physical experimentation.
Data Source
AI summary
Example embodiments provide systems and methods for formulating and evaluating a construction composition (such as a mixture 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 formulations that meet or best approximate the requirements. The formulations may be provided to a simulation to estimate or predict their performance. The performance characteristics of the output formulation(s) may be displayed. Optionally, the system may control mixing machinery to produce the formulation. Some embodiments may use these capabilities to evaluate an existing or proposed construction composition, rather than proposing a new construction composition.