ارايه‌ي مدل تحليل ريسك مبتني بر سيستم‌هاي خبره‌ي فازي براي مديريت پروژه‌هاي ساخت

Risk Assessment Model based on Fuzzy Expert Systems for Construction Project Management

پروژه‌هاي عمراني موجود در صنعت ساخت و ساز به دليل بزرگي و طبيعت پيچيده، منحصر به فرد و پويا بودن با عدم قطعي‌‌هاي بزرگي مواجه هستند. اين مسيله نياز جدي صنعت مذكور به روشي مناسب براي ارزيابي و تحليل ريسك را در فرآيند مديريت پروژه نشان مي‌دهد. هدف اين نوشتار، تعيين روشي نوين بر مبناي سيستم‌هاي خبره‌ي فازي سلسله مراتبي مبتني بر قوانين فازي براي درنظرگرفتن عدم قطعيت‌ها، به‌دست‌آوردن ريسك كلي پروژه، و در عين حال رده‌بندي ريسك‌هاي پروژه بر مبناي بزرگي آن‌هاست. قابليت كاربرد مدل پيشنهادي با پياده‌كردن مدل در يك پروژه‌ي ساختماني در شهر تهران مورد بررسي قرار گرفته است. ارزيابي ريسك مطالعه‌ي موردي از ديدگاه كارفرما، مشاور، پيمانكار، و درنهايت با درنظرگرفتن همه‌ي ذينفعان پروژه صورت گرفته است. نتايج حاصل نشان‌دهنده‌ي قابليت به‌كارگيري مدل ارايه‌شده در تحليل ريسك‌هاي تاثيرگذار در پارامترهاي مهم هزينه، زمان، كيفيت، و ايمني در اجراي پروژه‌هاي ساختماني است.

Construction projects, due to their size and complexity and their unique and dynamic nature, makes them impossible to model accurately. These attributes reveal the enormous need in these projects for a proper method of risk assessment. Furthermore, the main uncertainties involved in these projects are resulted because of vagueness (e.g. in cost estimation, time estimation, etc.), so, traditional methods used in the context of risk assessment, which are mainly based on probability theory, are not satisfactory for these projects. The aim of this paper is to propose a methodology based on hierarchical fuzzy rule based expert systems to assess the total risk of construction projects and also rank the risks based on their magnitude. For this purpose, a suitable hierarchical risk breakdown structure (HRBS) for construction projects is used to develop a formal frame for qualitative risk assessment. The highest layer of HRBS is the total risk of the project and the lowest layer consists of the factors influencing each risk. Then, for relating the different layers of the HRBS and developing the expert fuzzy system, a method is used to extract the fuzzy rules based on expert opinion. Finally, this fuzzy system is programmed. The efficiency of the proposed model has been analyzed for assessing risks of a construction project in Tehran. Risk assessment in this case study has been undertaken from the client, contractor and consultant points of view, separately, and also after aggregation of their opinions. The results show that the presented methodology can be used to assess risks related to cost, time, quality and safety in construction projects.