مهندسی سازه و ساخت

مهندسی سازه و ساخت

ارائه چارچوبی برای پایش خود‌کار پیشرفت پروژه‌های ساختمانی بتنی مبتنی بر بینایی رایانه و مدل‌سازی اطلاعات ساختمان

نوع مقاله : علمی - پژوهشی

نویسندگان
1 کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
2 استادیار، دانشکده مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
3 استادیار، دانشکده مهندسی کامپیوتر، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
چکیده
پایش پیشرفت پروژه‌های ساختمانی یکی از جنبه‌های حیاتی مدیریت پروژه است که نقش بسزایی در بهبود بهره‌وری، کنترل هزینه‌ها و کاهش زمان اجرای پروژه‌ها دارد. پایش دقیق و به‌موقع پیشرفت پروژه‌ها، امکان تصمیم‌گیری بهتر و بهینه‌تر را فراهم می‌کند و از بروز مشکلات و تأخیرات جلوگیری می‌نماید. با این حال، روش‌های سنتی پایش پروژه که عمدتاً دستی و زمان‌بر هستند، دارای محدودیت‌هایی همچون هزینه‌های بالا، نیاز به نیروی متخصص و تجهیزات پیشرفته می‌باشند. این پژوهش به توسعه یک چارچوب نوین برای پایش خودکار پیشرفت پروژه‌های ساختمانی با استفاده از تکنیک‌های بینایی رایانه‌ای و مدل‌سازی اطلاعات ساختمان پرداخته است. در این چارچوب، از پردازش تصویر برای شناسایی و تشخیص ستون‌های بتنی و از مدل سازی اطلاعات ساختمان برای تعیین میزان پیشرفت واقعی استفاده شده است. پس از تشخیص ستون‌ها، تعداد طبقات ساختمان و میزان پیشرفت پروژه با تحلیل تصاویر به‌دست‌آمده محاسبه می‌شود. مطالعات موردی نشان می‌دهند که این روش با دقت بالا و حداقل نیاز به ورودی تصویری، عملکرد مناسبی در ارزیابی پیشرفت پروژه داشته است. این چارچوب با استفاده از تنها یک تصویر از هر نما، بدون نیاز به اسکنرهای لیزری یا مجموعه داده‌های گسترده، می‌تواند میزان پیشرفت سازه‌های بتنی را با دقت بالا ارزیابی کند. این ویژگی باعث کاهش قابل‌توجه هزینه‌ها و افزایش قابلیت اجرا و دسترسی‌پذیری روشهای خودکار پایش پیشرفت در پروژه‌های کوچک و متوسط می‌شود. این دستاورد گامی مؤثر در جهت تسریع، بهینه‌سازی و هوشمندسازی فرایندهای مدیریت پروژه در مهندسی عمران محسوب می‌شود.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

A framework for automated progress monitoring of concrete building projects based on computer vision and building information modeling

نویسندگان English

SeyedSaeed Ghanbari Dazmiri 1
Naimeh Sadeghi 2
Behrooz Nasihatkon 3
1 MSc, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 Assistant Professor, Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Assistant Professor, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
چکیده English

Construction progress monitoring is a critical aspect of project management, playing a decisive role in improving productivity, controlling costs, and shortening the overall schedule. Accurate and timely progress tracking enables better, more informed decisions and helps prevent problems and delays. Traditional progress monitoring methods, however, are largely manual and time-consuming, suffering from high costs and a dependence on specialized labor and advanced equipment. This study develops a novel automated construction progress monitoring framework by combining computer-vision techniques with Building Information Modeling (BIM). The framework employs image processing to detect and localize concrete columns, while BIM data provides the planned-status reference against which actual progress is measured. Once the columns are identified, the number of completed stories—and thus overall project progress—is computed from the analyzed images. Case studies demonstrate that the proposed method achieves high accuracy with minimal image input. Using only one photograph per façade, and without laser scanners or large image datasets, the framework can reliably quantify progress in concrete structures. These capabilities substantially reduce cost and improve the practicality and accessibility of automated monitoring, especially for small- and medium-sized projects. Overall, the presented approach constitutes an effective step toward accelerating, optimizing, and smartening project-management processes in civil engineering.

کلیدواژه‌ها English

Construction Automation
Computer Vision
Progress Monitoring
Building Information
Modeling
Industry Foundation Classes (IFC)
Artificial Intelligence (AI)
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  • تاریخ دریافت 31 اردیبهشت 1404
  • تاریخ بازنگری 13 شهریور 1404
  • تاریخ پذیرش 05 مهر 1404