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مقایسه آزمایش Break-off و مدول گسیختگی برای تعیین مقاومت بتن الیاف فولادی با استفاده از شبکه عصبی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 3، دوره 52، شماره 3، خرداد 1399، صفحه 565-580 اصل مقاله (1.79 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2019.14724.5727 | ||
| نویسندگان | ||
| سید حسین قاسم زاده موسوی نژاد* 1؛ بنیامین گنجه خسروی2؛ جواد رزاقی لنگرودی3 | ||
| 1استادیار دانشگاه گیلان | ||
| 2دانشجو دکتری سازه-دانشگاه گیلان | ||
| 3عضو هیئت علمی دانشگاه گیلان | ||
| چکیده | ||
| آزمایش مدول گسیختگی بتن جهت تعیین مقاومت در برابر شکست ناشی از خمش و محاسبه لنگر ترک خوردگی نقش مهمی دارد. به منظور تعیین این پارامتر طبق استانداردASTM - C78- نیاز به برش از سازه موجود یا ساخت نمونه‌های با مقطع مربعی 150 میلی متر و طول 700 میلی متر می‌باشد. از آنجائی که تهیه نمونه با ابعاد فوق در محل دشوار می‌باشد و عمال باعث تخریب سازه می‌شود، استفاده از آزمایش نیمه مخرب با خسارت جزئی بر عضو، مدنظر قرار گرفته است. در این مطالعه آزمایش نیمه مخرب Break- off برای ارزیابی مقاومت بتن حاوی الیاف فولادی در محل به عنوان جایگزین آزمایش مدول گسیختگی مدنظر قرار گرفته است. جهت فراهم کردن یک پایگاه آماری کامل و جامع، 24 طرح اختالط انتخاب گردید. سپس، عوامل تأثیرگذار بر خصوصیات بتن حاوی الیاف فولادی، نتایج آزمایش Break- off و مدول گسیختگی مورد ارزیابی قرار گرفت. نتایج نشان می‌دهد که مقادیر متوسط مقاومت مدول گسیختگی و Break- off با افزایش مقدار سیمان افزایش می‌یابد. همچنین این مقادیر برای سنگدانه بزرگتر، بیشتر می‌باشند. همچنین وجود الیاف فولادی مانع گسترش و توسعه ریزترک‌های داخلی بتن شده و باعث افزایش مقاومت خمشی می‌شود که منجربه افزایش مقاومت Break- off و مدول گسیختگی می‌شود. همچنین، در این مقاله جهت پیش‌بینی مقاومت خمشی آزمایش مدول گسیختگی در سنین مختلف از مدل‌سازی عددی شبکه عصبی استفاده شده است. شاخص‌های مختلف آماری برای مقایسه عملکرد مدل در نظر گرفته شده است. نتایج نشان می‌دهد که مدل شبکه عصبی ابزار قدرتمندی جهت پیش بینی مقاومت فشاری بتن می‌باشد. | ||
| کلیدواژه‌ها | ||
| آزمایش break-off؛ مدول گسیختگی؛ آزمایش نیمه مخرب؛ الیاف فولادی؛ شبکه عصبی | ||
| موضوعات | ||
| تکنولوژی بتن؛ سازه بتنی | ||
| عنوان مقاله [English] | ||
| Comparison of Break-off and Flexural Strength Test Results for Determining Strength of SFRC Using Neural Network Model | ||
| نویسندگان [English] | ||
| hossein ghasem zadeh1؛ benyamin ganjeh khosravi2؛ Javad Razzaghi Langroudi3 | ||
| 1guilan university | ||
| 2PhD. Student, Civil Engineering, Faculty of Civil , Engineering, Guilan University | ||
| 3Assistant Professor, Faculty of Civil Engineering, Guilan University | ||
| چکیده [English] | ||
| Flexural strength test has a significant effect on the determination of failure strengths and cracking moment. According to ASTM-C78, the size and shape of used specimens were cubes by size (700*150*150 mm). Here, the efficiency of the non-destructive Break-Off (BO) and flexural strength tests was investigated for assessing the in-place compressive strength of steel fiber reinforced concrete (SFRC). In order to provide a thorough and comprehensive database, 24 mixtures were designed with various cement content, maximum aggregate size, steel fibre volume fractions and the constant water/ cement ratio of 0.4 for all mixtures. Then, effective parameters of SFRC and Break-Off and flexural strength test results were evaluated. The studies showed that volumetric percentage and features of steel fibers had a significant influence on concrete properties as well as Break-Off and flexural strength test results. According to the experimental results it could be generally concluded that the influencing factors, namely, SFRC properties due to presence of steel fibers and non-destructive tests significantly affect the results as follows: Generally, for a constant W/C ratio, it can be concluded that raising the cement content increases the mean values of Break-Off strength and Flexural strength. It can be stated that increasing the size of the aggregate causes an increase in strength. Also, the steel fibers restrain the development of internal micro-cracks in the concrete and thus contribute to the increase in bending strength, which causes improving Break-Off and flexural strengths. In addition, the conventional numerical regression model was developed in this study. Statistical indices were used to compare the efficiency and accuracy of the model. The result of this study confirmed the accuracy of the artificial neural network models in the determination of the compressive strength of concrete. | ||
| کلیدواژه‌ها [English] | ||
| Break-off test, Flexural strength test, Steel Fiber, Non-destructive, Neural network model | ||
| مراجع | ||
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