Selective Construction Error Reporting via Image Analysis
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Solution Overview
Problem
The construction industry faces inefficiencies in monitoring, analysis, and management of construction processes, leading to cost and schedule overruns, and quality issues due to manual methods, which are difficult, expensive, and prone to errors.
Innovation Solution
Systems and methods utilizing image sensors to capture and analyze construction site images, providing information on errors, quality assessment of materials like concrete, updating records, generating financial assessments, and ranking entities based on image data, with features for selective reporting and visual presentation of errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring and analysis methods are used for construction processes, then operational simplicity is maintained, but productivity and measurement precision deteriorate due to difficulty, expense, and errors
Solution Approach 1:
The patent replaces manual mechanical monitoring methods with an automated image processing system that uses computer vision algorithms to detect construction errors. The system automatically captures images of construction sites, processes them through machine learning models, and generates error reports without manual intervention, thereby improving productivity while managing complexity through software automation.
Solution Approach 2:
The system enables self-service monitoring where the construction site itself provides the data through captured images, and the automated processing system independently identifies errors without requiring external expert analysis. The machine learning model continuously improves its detection capabilities through self-training on accumulated data, reducing the need for manual calibration and intervention.
2Loss of information
If comprehensive error reporting is provided for all identified construction errors, then information completeness is improved, but information overload and processing complexity worsen
Solution Approach 1:
The patent applies local quality by providing different levels of error reporting based on the specific type, severity, and location of construction errors. Critical errors receive detailed immediate reports with precise location markers and remediation guidance, while minor errors are aggregated into periodic summaries. This selective reporting approach maintains information completeness for important issues while reducing overall reporting complexity through prioritization.
3Measurement precision
If image data analysis is performed on all construction site images, then measurement precision is improved, but processing time and computational resources worsen
Solution Approach 1:
The system performs partial analysis by initially processing only the most critical regions of construction site images using machine learning models, such as areas with visible structural elements or potential hazard zones. Less critical areas are either skipped or processed at lower resolution. This approach maintains high measurement precision for important features while reducing overall processing time and computational resource requirements.
Solution Approach 2:
The patent segments the construction site images into multiple regions of interest based on construction phase, structural importance, and error probability. Each segment is processed independently with appropriate analysis depth, allowing the system to focus computational resources on high-priority areas while maintaining accurate error detection where it matters most, thereby balancing precision with processing efficiency.
Data Source
AI summary
Systems and methods for selective reporting of construction errors are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. The image data may be analyzed to identify a construction error. The image data may be analyzed to identify a degree of the construction error. The identified degree of the construction error may be compared with a threshold selected based on an analysis of a construction plan associated with the construction site. In response to a first result of the comparison, a first severity may be assigned to the construction error, and in response to a second result of the comparison, a second severity may be assigned to the construction error, the second severity differs from the first severity. Further, information related to the construction error may be provided based on the severity assigned to the construction error.