Infrastructure Model Element Classification via ML
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Solution Overview
Problem
Manual classification of individual elements in infrastructure models is impractical due to the vast number of elements, frequent updates, and inconsistencies in classification information from distributed data sources with different nomenclatures, leading to inefficiencies in analytics and monitoring.
Innovation Solution
Training machine learning algorithms on classified infrastructure models to produce a classification model that maps features to labels, enabling automatic classification of elements, which can handle diverse data sources and reduce manual classification burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification is performed on infrastructure model elements, then classification accuracy can be maintained, but the time consumption becomes extreme due to millions of elements and frequent updates
Solution Approach 1:
The patent replaces manual classification (mechanical human operation) with an automated classification system that uses machine learning algorithms and natural language processing. The system automatically extracts classification information from element descriptions, metadata, and other data sources, eliminating the need for manual classification of millions of elements while maintaining accuracy through trained classification models.
Solution Approach 2:
The classification system enables elements to self-classify by automatically extracting and processing their own description data, metadata, and contextual information. The automated system performs classification without external manual intervention, allowing the infrastructure model to maintain up-to-date classification labels through self-service classification even as elements are added or modified.
2Stability of the object's composition
If classification standards are established and enforced across distributed data sources, then classification consistency improves, but the complexity of data federation increases due to different nomenclatures and sources
Solution Approach 1:
The patent introduces an intermediary classification system that sits between distributed data sources and the analytics layer. This intermediary automatically standardizes classification labels by processing element descriptions and metadata through NLP and machine learning, translating diverse nomenclatures from different sources into a unified classification scheme without requiring changes to the underlying data sources or complex enforcement mechanisms.
Solution Approach 2:
The system dynamically adjusts classification parameters by extracting features from element descriptions, metadata, and contextual data. The machine learning model processes varying input formats and nomenclatures from different sources, transforming them into standardized classification labels through parameter transformation and feature extraction, thereby achieving consistency without rigid standard enforcement.
3Reliability
If classification information is maintained during translation and conversion of data from distributed sources, then data fidelity improves, but information loss occurs during the translation process
Solution Approach 1:
The patent performs preliminary classification by extracting and processing classification-relevant information from element descriptions and metadata before the actual classification decision is made. The system pre-processes text data, extracts key features, and prepares classification candidates in advance, ensuring that classification information is preserved and enhanced during the translation and conversion process rather than lost.
Data Source
AI summary
In example embodiments, techniques are provided to automatically classify individual elements of an infrastructure model by training one or more machine learning algorithms on classified infrastructure models, producing a classification model that maps features to classification labels, and utilizing the classification model to classify the individual elements of the infrastructure model. The resulting classified elements may then be readily subject to analytics, for example, enabling the display of dashboards for monitoring project performance and the impact of design changes. Such techniques enable classification of elements of new infrastructure models or in updates to existing infrastructure models.