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Summary

This study introduces an automated framework using computer vision and document recognition to monitor temporary structures like jack supports on construction sites. The system ensures safety and compliance by accurately tracking installation and dismantling against project documentation.

Keywords:

document information jack support monitoring natural language processing (NLP)object detection optical character recognition (OCR)

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Area of Science:

Construction Engineering and Management
Computer Vision
Data Analytics

Background:

Ensuring safety and adherence to timelines in construction relies on accurate monitoring of temporary structures, such as jack supports.
Discrepancies between on-site data and construction documentation pose a significant challenge to effective project management and safety compliance.

Purpose of the Study:

To develop and validate an integrated monitoring framework combining computer vision and document recognition for temporary structures.
To enhance accuracy in tracking installation, retention, and dismantling timelines of jack supports, aligning on-site data with project documentation.

Main Methods:

Utilized YOLOv5 for object detection of jack supports in construction drawings and on-site images from wearable cameras.
Employed optical character recognition (OCR) and natural language processing (NLP) to extract timeline data from work orders.
Implemented techniques like color differentiation, plan overlays, and vertical segmentation to improve detection reliability under varied conditions.

Main Results:

Achieved an average detection accuracy of 94.1% in monitoring 23 jack supports over 28 days.
Effectively identified discrepancies between on-site status and documented requirements, reducing misclassifications.
Demonstrated improved performance despite environmental variations (lighting, structural changes) and structural similarities.

Conclusions:

The integrated framework effectively aligns visual and textual data for continuous monitoring in dynamic construction environments.
Automated monitoring systems significantly improve accuracy, safety, and reduce manual intervention in construction site management.
The study provides practical insights for enhancing future construction site management through data integration and automation.