تعيين ظرفيت باربري شمع هاي كوبشي در خاك هاي ماسه اي

Determination of Beraing Capacity of Driven Piles in Sandy Soils by using Artificial Neural Network

اثر عواملي نظير ناهمساني محيط خاك در برگيرنده يك شمع، اجراي شمع، جنس شمع و شكل آن، تخمين صحيح ظرفيت باربري يك شمع را با مشكل همراه مي سازد. با آن كه آزمايش بارگذاري شمع مي تواند با وجود دقت بالا، به عنوان يك روش قابل اعتماد در مراحل مختلف تحليل و طراحي به كار رود، هزينه هاي زياد و زمان انجام طولاني را براي پروژه هاي عمراني تحميل مي-كند و همين مساله باعث ايجاد محدوديت هايي در انجام اين آزمايش مي شود. در اين ميان مدل‌سازي با استفاده از شبكه هاي عصبي مصنوعي روشي است كه بر اساس داده ها و اطلاعات قبلي بوده و نيازي به ساده‌سازي و استفاده از ضرايب اطمينان بالا ندارد. در اين مقاله، شبكه هاي عصبي مصنوعي براي پيش بيني ظرفيت باربري شمع هاي فلزي ته باز كوبيده شده در خاك هاي ماسه اي، به كار گرفته شده اند. از 4 پارامتر طول شمع، قطر شمع، مدول الاستيسيته خاك و زاويه اصطكاك داخلي آن به عنوان ورودي و از ظرفيت باربري شمع به عنوان خروجي استفاده شده است. چگونگي طراحي شبكه و عوامل موثر بر رفتار آن در مسيله مربوطه به صورت خلاصه بررسي شده است. در پايان با آناليز حساسيت بر روي ساختار بهينه مدل‌هاي معرفي شده، در خصوص چگونگي اثر 4 پارامتر ورودي بر خروجي شبكه بحث شده است.

Determining the bearing capacity of piles is an important issue that always Geotechnical engineers focus on. Effect of factors such as environmental dissonance of soil which contains a pile, pile implementation, pile gender and its shape make correct estimation of bearing capacity difficult. Pile load testing as a reliable method could be used in various stages of analysis, design and implementation of piles to determine theaxial bearing capacity of piles. On the other hand, pile load testing, despite high accuracy, imposes high cost and long duration for development projects and it causes limitations in this experiment. Thus acceptance of numerical analysis at geotechnical studies is increasing. The modeling using artificial neural networks is the method that is based on previous data and don’t need to simplify and improve the high reliability coefficient. In this study serious models of multi-layer perceptron neural network, one of the most commonly used neural networks, was used. Network design and factors influencing its behavior in this issue has been studied as a summary. In this study, artificial neural networks are used for prediction of bearing capacity of driven steel piles in sandy soil, in all models four parameters are used as input data which are length and diameter of the pile, the coefficient of elasticity and internal friction angle of soil and the bearing capacity of piles is used as output data. Models have reasonable success in predicting the bearing capacity of piles. In order to evaluation of networks, the different indices such as RMSE, MAE, MAXAE and SDAE were used. To increase the accuracy of predicting bearing capacity, for the network training stage the real tests that has been done at the geotechnical studies of dry dock area hormozgan by POR Consulting Engineers were used.Acording to (Because we) need of more data for training and testing network, several tests on pile bearing capacity, in smaller dimensions were performed in the laboratory. The sixty tests have been performed on piles with various length (35, 40, 45 and 50 cm), various diameters (20, 25 and 32 mm) and different relative compacted sandy beds (50, 60, 70, 75 and 80%). To perform these tests the device of pile bearing capacity, made in university of TarbiatModarres, was used. Models based on neural networks, unlike traditional models of behavior don’t explain effect of input parameters on output parameters. In this study, by the sensitivity analysis on the optimal structure of introduced models in each stage it has been somewhat trying to response this question.