Quasi-Non-Destructive Evaluation of Yield Strength Using Neural Networks
نویسندگان
چکیده
منابع مشابه
rodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Improving Non - Destructive Test Results Using Artificial Neural Networks
In the construction industry, non-destructive testing (NDT) methods are gaining more popularity for their ability to examine the in-situ component properties without damaging the structure. One of the most common NDTs for measuring the concrete compressive strength on site is the Rebound Hammer Test. Using the rebound value obtained from the test hammer, the concrete compressive strength can be...
متن کاملArtificial neural networks for non destructive testing of concrete structures
In this paper, the determination of defects in concrete structures using an ultrasound technique is discussed. A diagnostic model for concrete pillars by means of Multi Layer Perceptron neural networks is developed to locate and classify the defects. Finite Elements numerical techniques have been used to model a concrete pillar of specified size (i.e., rectangular cross section and 2 meters in ...
متن کاملImproving Non-Destructive Concrete Strength Tests Using Support Vector Machines
Non-destructive testing (NDT) methods are important alternatives when destructive tests are not feasible to examine the in situ concrete properties without damaging the structure. The rebound hammer test and the ultrasonic pulse velocity test are two popular NDT methods to examine the properties of concrete. The rebound of the hammer depends on the hardness of the test specimen and ultrasonic p...
متن کاملPrediction of Pervious Concrete Permeability and Compressive Strength Using Artificial Neural Networks
Pervious concrete is a concrete mixture prepared from cement, aggregates, water, little or no fines, and in some cases admixtures. The hydrological property of pervious concrete is the primary reason for its reappearance in construction. Much research has been conducted on plain concrete, but little attention has been paid to porous concrete, particularly to the analytical prediction modeling o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Artificial Neural Systems
سال: 2011
ISSN: 1687-7594,1687-7608
DOI: 10.1155/2011/607374