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ارزیابی و اولویتبندی عوامل مؤثر بر هوشمندسازی مدیریت پروژههای ساخت سدهای بتنی با رویکرد ترکیبی تصمیمگیری چند معیاره فازی و یادگیری ماشین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 57، شماره 1، فروردین 1405، صفحه 39-67 اصل مقاله (2.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2026.409192.670080 | ||
| نویسندگان | ||
| اُسامَه عبدالطیف عبداله الموسوی1؛ میرعلی محمدی* 2؛ محمد خردرنجبر3؛ شاهین رفیعی4 | ||
| 1دانشجوی دکتری گروه مهندسی عمران، دانشکده فنی مهندسی، دانشگاه ارومیه، ارومیه، ایران. | ||
| 2گروه مهندسی عمران، دانشکده فنی، دانشکاه ارومیه | ||
| 3استادیار گروه مهندسی عمران، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران. | ||
| 4استاد گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران. | ||
| چکیده | ||
| با توجه به پیچیدگی روزافزون پروژههای ساخت سدهای بتنی و گسترش کاربرد فناوریهای هوشمند، فقدان یک چارچوب بومی و نظاممند برای شناسایی و اولویتبندی عوامل مؤثر بر هوشمندسازی مدیریت این پروژهها، تصمیمگیریهای مدیریتی و فنی را با عدم قطعیت مواجه کرده است. هدف این پژوهش، توسعه یک چارچوب ترکیبی و دادهمحور برای ارزیابی و اولویتبندی عوامل مؤثر بر هوشمندسازی مدیریت پروژههای سدهای بتنی است. در این راستا، ۱۰۰ عامل کلیدی در قالب ۹ معیار اصلی شامل فنی، زمانی، اقتصادی، ایمنی، فرهنگی، محیطی، قانونی، نظارتی و فناوری شناسایی شد. دادهها از طریق دو پرسشنامه ساختاریافته گردآوری گردید؛ بهگونهای که ارزیابی عوامل توسط ۳۳ کارشناس و مقایسههای زوجی معیارها توسط ۴۲ متخصص باتجربه انجام شد. در گام نخست، از روشهای تصمیمگیری چندمعیاره فازی برای وزندهی معیارها، تحلیل روابط علّی میان آنها و محاسبه امتیاز نهایی هر عامل استفاده شد. در گام دوم، خروجیهای حاصل از این مرحله بهعنوان دادههای ورودی به 12 مدلهای یادگیری ماشین بهکار گرفته شد تا قابلیت پیشبینی وزنها، پایداری رتبهبندی و تحلیل حساسیت نتایج مورد ارزیابی قرار گیرد. در چارچوب تلفیقی، سه مدل رگرسیون شامل رگرسیون حداقل مربعات جزئی، رگرسیون بیزی و رگرسیون ریج بررسی شد که نتایج نشان داد مدل حداقل مربعات جزئی از دقت و پایداری بالاتری برخوردار است. یافتهها حاکی از آن است که عوامل فنی و بومی، بهویژه محدودیتهای ناشی از تحریمها و نیاز به سامانههای هوشمند پایش، بیشترین اثرگذاری را دارند. چارچوب ارائهشده، با توجه به ماهیت دادهمحور و ساختار انعطافپذیر، برای مناطق دارای شرایط فنی، مدیریتی و نهادی مشابه قابل تعمیم است. | ||
| کلیدواژهها | ||
| تصمیمگیری چندمعیاره؛ ارزیابی فازی؛ تحلیل روابط علّی؛ پیشبینی دادهمحور؛ فناوریهای هوشمند | ||
| عنوان مقاله [English] | ||
| Evaluation and Prioritization of Factors Affecting Smart Management of Concrete Dam Construction Projects Using a Hybrid Fuzzy MCDM and Machine Learning Approach | ||
| نویسندگان [English] | ||
| Osamah Abdulateef Abdullah AlMusawi1؛ Mirali Mohammadi2؛ Mohammad Kheradranjbar3؛ Shahin Rafiee4 | ||
| 1Department of Civil Eng., Faculty of Eng., Urmia, Iran. | ||
| 2Department of Civil Eng., Faculty of Eng., Urmia University, Urmia, Iran | ||
| 3Department of Civil Engineering, Ka.C., Islamic Azad University, Karaj, Iran. | ||
| 4Department of Biosystems Mechanical Engineering, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| چکیده [English] | ||
| Given the increasing complexity of concrete dam construction projects and the growing demand for intelligent technologies, the absence of a comprehensive and locally adapted framework for identifying and prioritizing the factors influencing smart project management creates significant technical and managerial challenges. This study aims to develop a multi-criteria evaluation framework by identifying 100 critical factors categorized into nine major criteria: technical, temporal, economic, safety, cultural, environmental, legal, supervisory, and technological. Data were collected through two structured questionnaires: the first for assessing the 100 factors by 33 experts, and the second for pairwise comparison of the nine criteria by 42 experienced specialists. Fuzzy multi-criteria decision-making methods were applied to determine the weights of criteria and rank the factors. To enhance the robustness and predictive accuracy of the results, three machine learning models—Partial Least Squares Regression, Bayesian Ridge, and Ridge Regression—were employed. Among these, the PLSR model demonstrated superior performance and was therefore selected for weight prediction and sensitivity analysis. The results indicate that technical–local factors, particularly sanctions-related constraints, challenges of constructing massive concrete structures, and the need for intelligent monitoring systems for phenomena such as erosion and settlement, have the highest influence, accounting for approximately 30–50% of the total impact. Furthermore, emerging technologies such as digital twin systems, Internet of Things platforms, and integrated supervisory tools ranked highest in priority. The proposed framework provides a practical basis for policymakers and project managers to develop more targeted smart-management strategies, prioritize technological investments, and mitigate operational risks in large-scale dam construction projects. | ||
| کلیدواژهها [English] | ||
| Multi-criteria decision making, Fuzzy evaluation, Causal relationship analysis, Data-driven prediction, Intelligent technologies | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 59 تعداد دریافت فایل اصل مقاله: 60 |
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