AR Structural Transformation Modeling for Real-Time Building Change Detection
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Solution Overview
Problem
Conventional methods for detecting structural transformations in architectural structures are static and fail to timely identify or predict subtle, often hidden, changes that can lead to catastrophic events, necessitating a real-time and dynamic detection system.
Innovation Solution
A system utilizing a mesh network of nodes that collect and analyze positional data to estimate and predict structural transformations, employing VR or AR technologies for real-time presentation of transformation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional static identification methods are used, then the approach is simple and easy to implement, but it fails to timely detect and report slowly developing structural transformations
Solution Approach 1:
The patent implements dynamic detection by continuously monitoring structural transformations in real-time rather than using static periodic inspections. The system dynamically adjusts monitoring based on detected changes, enabling timely detection of slowly developing transformations while maintaining manageable complexity through automated adaptive processes.
Solution Approach 2:
The patent replaces manual mechanical inspection methods with automated sensor-based detection systems. This substitution enables continuous real-time monitoring without human intervention, significantly improving detection timeliness while the automated nature keeps system complexity manageable.
2Loss of time
If real-time detection systems are implemented, then detection timeliness is improved, but the system complexity increases
Solution Approach 1:
The patent divides the structure into multiple monitored segments with distributed sensors. Each segment is monitored independently, allowing parallel processing and reducing the complexity burden on any single processing unit while achieving comprehensive real-time coverage across the entire structure.
Solution Approach 2:
The system implements self-service through automated data collection, processing, and alert generation without requiring constant human intervention. The automated nature enables real-time detection while keeping operational complexity manageable through self-managing processes.
3Measurement precision
If comprehensive structural monitoring is performed, then measurement precision is improved, but the difficulty of detecting and measuring hidden transformations increases
Solution Approach 1:
The patent applies different monitoring strategies to different parts of the structure based on their specific characteristics and risk profiles. High-risk areas with hidden transformations receive more sophisticated local monitoring, while lower-risk areas use simpler methods, optimizing detection accuracy without uniformly increasing overall difficulty.
Solution Approach 2:
The system detects hidden transformations by measuring changes in multiple dimensions including position, orientation, and structural relationships between components. This multi-dimensional approach enables detection of subtle hidden transformations that would be invisible in single-dimension measurements.
Data Source
AI summary
Individual nodes of a plurality of nodes can be fixed to the architectural structure and associated with a different segment of a plurality of segments of a first model of an architectural structure. Positional data associated with the nodes can be received. A structural change to the architectural structure can be estimated based on a comparison of positional data associated with the nodes. A second model representing the architectural structure as modified by the structural change can be generated. The second model can be generated based on (i) the first model and (ii) the estimated change to the architectural structure.