Structural Inspection AI Using Digital Images for Damage Detection
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Solution Overview
Problem
Traditional processes for designing, constructing, and maintaining structures are manual, inefficient, and prone to errors, leading to potential damage and collapse, especially during natural disasters, with manual inspections being time-consuming and costly.
Innovation Solution
A machine learning tool that incorporates pre-trained models and algorithms to assess structure performance, identify components, and detect damage from images or drawings, automating the process and reducing errors through automated inspections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection processes are used to assess structure performance and detect damage, then professionals can provide expert judgment and detailed analysis, but the process becomes time-consuming, costly, and unable to meet overwhelming inspection needs after natural disasters
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated machine learning-based inspection system. The system uses computer vision algorithms to analyze images and drawings, automatically detecting structural components and damage without human intervention in the actual inspection process, thereby dramatically reducing inspection time while maintaining accuracy
Solution Approach 2:
The patent creates digital copies of physical structures through images and drawings, which are then processed by machine learning models. This allows multiple inspections and analyses to be performed on the digital copies simultaneously, eliminating the need for sequential manual inspections and significantly reducing the time required to assess multiple structures
2Measurement precision
If manual inspection processes are used to assess structure performance and detect damage, then professionals can provide expert judgment and detailed analysis, but the process becomes expensive and economically lossy due to downtime from repair and rebuilding
Solution Approach 1:
The patent replaces expensive manual professional inspection services with an automated machine learning system that provides comparable or superior inspection capabilities at lower cost. The system can process and analyze structural images and drawings rapidly, reducing both direct inspection costs and indirect economic losses from prolonged downtime
Solution Approach 2:
The patent enables continuous inspection capabilities through automated processing, allowing structures to be assessed immediately after disasters without the delays inherent in manual inspection scheduling. This continuity reduces downtime and associated economic losses by enabling faster decision-making for repairs and rebuilding
3Reliability
If traditional manual processes are used for structure design, construction, and maintenance, then professionals can apply expertise and experience, but the processes become inefficient and prone to errors leading to potential damage and collapse
Solution Approach 1:
The patent replaces manual design, construction, and maintenance processes with automated machine learning systems that eliminate human error in critical assessments. The system consistently applies trained models to analyze structural data, ensuring reliable and reproducible results without the variability and errors inherent in manual processes
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning system continuously learns from inspection results and structural performance data. This feedback loop improves the reliability of assessments over time by refining the models based on actual structural behavior and inspection outcomes, reducing errors in future assessments
4Ease of manufacture
If discontinuous inspections over decades-long intervals are performed to maintain structures, then manual inspection resources are conserved, but issues that could lead to major damage or collapse may be missed
Solution Approach 1:
The patent enables continuous or near-continuous monitoring and inspection capabilities through automated systems that can process structural data frequently without significant resource expenditure. This continuous action ensures structural issues are detected early before they lead to major damage, eliminating the risks associated with decade-long inspection intervals
Solution Approach 2:
The patent uses digital copies and automated analysis to enable frequent inspections at minimal cost. By processing digital images and drawings through machine learning models, the system can perform assessments continuously without the resource constraints that limit manual inspection frequency, thereby maintaining high reliability in damage detection
Data Source
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AI summary
A machine learning tool for structures to (1) design structures, (2) verify construction and (3) assess damage due to deterioration, change of properties or a destructive event. The tool comprises various pre-trained machine learning models and post-processing algorithms. The tool includes a user interface that allows users to upload their data, analyse it through one or more pre-trained machine learning models and post-process the machine learning results in various ways. The tool displays the results and allows users to export them in various formats.