تعیین عوامل مؤثر بر نرخ شکست لوله‌ها در شبکه‌های توزیع آب با استفاده از تلفیق شبکه‌های عصبی مصنوعی و الگوریتم ژنتیک

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی آبیاری و زهکشی، پردیس ابوریحان، دانشگاه تهران

2 استادیار گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه شهرکرد

چکیده

در سامانه‌های توزیع آب حوادث ایجاد شده در لوله‌ها از اهمیت بالایی برخوردار است. در شکست لوله‌ها عوامل متعددی همچون سن، قطر، جنس، شرایط اجرا و نصب، شرایط بهره‌برداری و غیره دخیل بوده و بررسی‌ها نشان دهنده عدم جامعیت روابط ارائه شده در پیش‌بینی نرخ شکست لوله‌ها است. در این تحقیق، ساختاری با استفاده از ترکیب مدل‌های نوین بهینه‌سازی و شبیه‌سازی تدوین گردید. در این ساختار برای شبیه‌سازی نرخ شکست لوله‌ها از شبکه‌های عصبی مصنوعی استفاده شد. با توجه به عدم توانایی شبکه‌های عصبی در مشخص نمودن تأثیر هر متغیر مستقل بر متغیر وابسته، در این تحقیق برای تعیین پارامترهای ورودی مؤثر در نرخ شکست و همچنین مناسب‌ترین پارامترهای مرتبط با ساختار شبکه عصبی مصنوعی، از الگوریتم بهینه‌سازی ژنتیک با هدف ارائه ساختاری با کمترین میزان خطای شبیه‌سازی، استفاده گردید. متغیرهای تصمیم در این الگوریتم مشخصات شبکه عصبی و پارامترهای مؤثر بر نرخ شکست هستند. با اجرای ساختار پیشنهادی، علاوه بر پارامترهای مؤثر در شکست لوله، بهترین ساختار شبکه عصبی تعیین گردید. با استفاده از نتایج این تحقیق می‌توان مناسب‌ترین رابطه نرخ شکست لوله‌ها را با توجه به پارامترهای تأثیرگذار بر آن استخراج نمود. نتایج این تحقیق بیانگر آن است که روش ترکیبی پیشنهادی قادر است پارامترهای بهینه و مؤثر در نرخ شکست لوله‌ها را از میان عوامل متعدد مؤثر در شکست، استخراج کند و باعث ارتقای قابلیت و قدرت تعمیم شبکه عصبی گردد که این امر نشان دهنده کارایی بالای روش پیشنهادی در شبیه‌سازی روابط غیرخطی و پیچیده است.  

کلیدواژه‌ها


عنوان مقاله [English]

Determination of Effective Parameters in Pipe Failure Rate in Water Distribution System Using the Combination of Artificial Neural Networks and Genetic Algorithm

نویسندگان [English]

  • Jaber Soltani 1
  • Mahmoud Mohammad Rezapour Tabari 2
1 Assist. Prof. of Irrigation and Drainage, Abu Rayhan Pardis , Tehran University, Tehran
2 Assist. Prof. of Civil Eng., College of Eng., Shahrekord University, Shahrekord
چکیده [English]

In water supply systems, the accidents occurring in pipes are of the utmost importance and sensitivity. Failure of the pipes is not necessary with the end of their life and different factors namely age, diameter, material, stability and corrosion of soil and water, execution, installation and operational conditions such as hydraulic pressure are effective on it. At the same time studies show non comprehensiveness of presented relations in prediction of pipes failure rate. In this research, with regards to the available software and hardware, a structure was developed using combination of new optimization and simulation models. In this structure, theartificial neural networks were used for simulation of the pipes failure rate. Considering the point that  neural networks are always consider as a black box and unable to provide the effect of each independent variable on the dependent variable, on the other hand, they are prone to incorrect training. Therefore, in this study to determine the input parameters affecting failure rate and the most appropriate structure of artificial neural network (as well as bios vectors, layers adjusting weights and the number of neurons), Genetic algorithm has been used with the aim of presentation of a structure which has the minimum error rate of simulation.In this algorithm, decision variables and properties of neural networks are the parameters affecting the failure rate. By running the developed optimization model, in addition to the effective parameters on pipe failure, the best neural network structure for simulation pipe failure rate can be determined. The advantage of the proposed method is full coordination between the input parameters and network structure in prediction of the pipes failure rate. The results of this study can be used to find the most appropriate relationship failure rate pipes with regards to the effective parameters and take necessary actions for decision making lead to resolve problems due to it. The results of this research indicating that the proposed combination method is able to extract the optimal and effective parameters  on the pipes failure rate amongst the factors affecting failure rate, and also have been caused improve power capabilities and expansion of neural network structure that indicate high efficiency of the proposed method in simulation of nonlinear and complex relations.

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Genetic algorithm
  • Pipes Failure Rate
  • Water Distribution Systems
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