پیش بینی طول توسعه میله تقویت کننده
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پیش بینی طول توسعه میله تقویت کننده

عنوان فارسی مقاله: پیش بینی طول توسعه میله تقویت کننده با استفاده از گسترش آشفتگی چند جمله ای
عنوان انگلیسی مقاله: Predicting reinforcing bar development length using polynomial chaos expansions
مجله/کنفرانس: سازه های مهندسی – Engineering Structures
رشته های تحصیلی مرتبط: مهندسی عمران
گرایش های تحصیلی مرتبط: سازه
کلمات کلیدی فارسی: گسترش آشفتگی چند جمله ای، مدل، پیش بینی، میلگرد، طول توسعه، تنش پیوند، پوشش بتونی، استحکام فشاری، کد طراحی
کلمات کلیدی انگلیسی: Polynomial chaos expansion، Model، Prediction، Rebar، Development length، Bond stress، Concrete cover، Compressive strength، Design code
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.engstruct.2019.06.012
دانشگاه: Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 3.604 در سال 2018
شاخص H_index: 114 در سال 2019
شاخص SJR: 1.628 در سال 2018
شناسه ISSN: 0141-0296
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 12
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E12444
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. Experimental database


3. Methodology and model development


4. Model application, results and discussion


5. Parametric study


6. Summary and concluding remarks


References

نمونه متن انگلیسی مقاله

Abstract


The bond stress of a reinforcing bar in a cementitious matrix varies along the bar length and is difficult to quantify. Thus, design code provisions refer to the concept of bar development length and rely on statistical analysis of rebar-pull-out test results. In the present study, a novel data-driven predictive model based on Polynomial Chaos Expansions (PCE) was developed to predict the reinforcing bar development length using 534 experimental results of simple pull-out tests on short unit bar lengths. The predictive capability of PCE was compared to that of other data-driven models, namely the Response Surface Method (RSM) and Artificial Neural Networks (ANN). Moreover, predictions of the PCE, RSM and ANN were further compared with calculations of three commonly used design code formulas (i.e., ACI 318-14, ACI 408R-03, and Eurocode 2) and predictions of two existing empirical models (i.e. Model Code 2010 and Hwang et al. model). A parametric study was conducted to explore the sensitivity of the proposed model to influential input parameters. It was found that the Polynomial Chaos Expansions model offers a powerful predictive tool for reinforcing bar bond strength. The model was able to capture trends that differ from that of existing models that assume unrealistic uniform bond stress along the rebar. This flexible and data intensive model for predicting rebar bond stress and full embedment length could offer an intelligent platform for accommodating new bar materials, new test data, and calibrating existing design provisions to keep design codes relevant.


Introduction


Reinforced concrete technology has been undergoing transformative changes in recent decades. For instance, various types of reinforcing steel rebar are being used, including carbon steel with various deformed shapes, galvanized, epoxy-coated, stainless, and high-strength steel rebar [1,2]. Moreover, new composite material bars with different engineering properties and surface texture have emerged as contenders for concrete reinforcement, including carbon, aramid, glass and basalt rebar. Likewise, the cementitious matrices in which various rebar can be embedded have become diverse. Indeed, different types of concrete have become mainstream with inherently different bond to and compatibility with reinforcing rebar. These include conventional concrete, mass concrete, cellular concrete, fiber-reinforced concrete, highstrength concrete, reactive powder concrete, ultrahigh-performance concrete, polymer-modified concrete, lightweight concrete, pervious concrete, shotcrete, rubberized concrete, roller-compacted concrete, self-consolidating and anti-washout concrete, concrete using calcium aluminate or calcium sulfo-aluminate cements, just to name the most common types. Moreover, sustainability considerations have brought about eco-efficient concrete types such as geo-polymer concrete, concrete using alkali-activated binders, and other alternative cementitious matrices.

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