Concrete Mix Catalog Clustering for Slump-Based Design Selection
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Solution Overview
Problem
Concrete producers face challenges in managing their growing mix design catalogs, leading to inefficiencies in confirming whether existing mix designs meet specific customer requirements, due to the proliferation of duplicative and similar mix designs, which results in overdesign and increased costs.
Innovation Solution
A process and system that utilize in-transit concrete delivery monitoring data to cluster slump curve data and assign strength values, allowing for the reduction of mix designs to a manageable number, focusing on cost, performance, and other selection factors, thereby optimizing mix design selection and quality control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If concrete producers retain all historical mix designs in their catalog, then they preserve past testing investments and potential future project needs, but the catalog grows to thousands of duplicative designs making quality control and performance verification increasingly difficult
Solution Approach 1:
The patent merges multiple duplicative mix designs into unified mix design clusters based on similarity analysis. Mix designs with comparable performance characteristics, material compositions, and application suitability are consolidated into single representative designs, reducing the catalog from thousands of individual designs to a manageable number of clusters while preserving the essential performance data through aggregation.
Solution Approach 2:
The patent transforms the mix design catalog management approach by changing the organizational parameters from individual design retention to cluster-based representation. By analyzing and grouping designs based on multiple parameters (material composition, performance characteristics, application type), the system restructures the catalog to improve verifyability while maintaining the underlying performance information through clustered data aggregation.
2Adaptability or versatility
If concrete producers add new mix designs to satisfy customer requests, then they expand their product offerings and meet diverse application requirements, but the number of duplicative designs increases and dilutes quality control resources
Solution Approach 1:
The patent creates mix design clusters that serve multiple functions simultaneously. Each cluster represents a family of designs that can satisfy various customer requirements within a specific performance category, making the catalog more versatile without proportionally increasing the number of individual designs. A single cluster can address multiple applications and customer needs that previously required separate designs.
Solution Approach 2:
When new mix designs are added to satisfy customer requests, the system merges them into existing clusters if they share similar characteristics, rather than creating entirely new individual entries. This approach maintains product diversity and adaptability while consolidating quality control efforts around representative cluster designs rather than分散 resources across numerous similar individual designs.
3Manufacturing precision
If concrete producers re-evaluate existing mix designs to confirm performance, then they ensure quality standards are met, but the large number of designs requires excessive time and resources to sift through
Solution Approach 1:
The patent extracts the essential performance verification function from individual mix design review and applies it to cluster-level representation. By taking out the redundant verification steps that would be required for each individual design within a cluster, the system performs quality confirmation on the representative cluster design, which then validates all member designs, dramatically reducing the time and resources required for performance confirmation.
Solution Approach 2:
Instead of performing full verification on every mix design in the catalog, the system applies verification to a partial set - specifically, the representative designs of each cluster. This partial action approach is sufficient to ensure quality standards are met across the entire catalog while avoiding the excessive time consumption of reviewing every individual design, achieving efficient quality control through strategic sampling at the cluster level.
Data Source
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AI summary
Disclosed are exemplary process and system for managing a mix design catalog of a concrete producer that involves collecting slump curve data obtained during in-transit monitoring of delivered concrete loads made from a plurality of various mix designs, wherein each mix design is identified by a different identification code (regardless of whether components are different), clustering slump curve data having same movement characteristics according to assigned strength value, and selecting a mix design to produce, to display, or both to produce and to display, from among the two or more slump data curves of individual mix designs within the same data curve cluster. The selection is based on same movement characteristic and assigned strength value, and at least one factor relative to cost, performance, physical aspect, quality, or other characteristic of the concrete mix or its components. Exemplary methods for generating new mix designs are also disclosed.