مدل های جدید برای تعیین مدول الاستیسیته بتن با درنظرگیری تغییرات مقاومت فشاری | ||
| پژوهش های زیرساخت های عمرانی | ||
| دوره 8، شماره 1 - شماره پیاپی 14، شهریور 1401، صفحه 171-183 اصل مقاله (1.26 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22091/cer.2022.7871.1357 | ||
| نویسندگان | ||
| سیما آرامش* 1؛ پویان فخاریان2 | ||
| 1مربی، دپارتمان مهندسی عمران، دانشکده فنی و حرفه ای پسران سمنان، دانشگاه فنی و حرفه ای استان سمنان، سمنان، ایران | ||
| 2دانشکده مهندسی عمران، دانشگاه سمنان، سمنان، ایران | ||
| چکیده | ||
| مدول الاستیسیته بتن در مراحل آنالیز و طراحی سازه های بتنآرمه نقش کلیدی داشته است و اثرات تعیین کننده‌ای در تغییر شکل جانبی سازه دارد. در این مطالعه روابط جدیدی برای محاسبه مدول الاستیسیته بتن های با مقاومت معمولی و مقاومت بالا با استفاده از ترکیب شبکه عصبی چند جمله ای و الگوریتم بهینه سازی علف هرز مهاجم ارائه شده است. مشابه روابط آئین نامه‌های مرسوم، سعی شده است که مدلهای پیشنهادی به صورت ساده و براساس پارامتر مقاومت فشاری بتن تعیین شوند. برای بررسی صحت روابط پیشنهادی سعی شده است ارزیابی جامعی بین مقادیر آزمایشگاهی، نتایج مدل های ارائه شده در این مطالعه و روابط بیان شده توسط آئین نامه‌ها، دستورالعمل‌ها و محققین مختلف انجام گیرد. برای ارزیابی جامع از سه شاخص آماری: ضریب تشخیص، معیار خطای جذر میانگین مربعات و معیار میانگین درصد قدرمطلق خطا استفاده شده است. قابل ذکر است که درصد خطای نسبی رابطه‌های پیشنهادی برای بتن معمولی 9.02 درصد و برای بتن مقاومت بالا 3.8 درصد می باشد. نتایج حاکی از آن است که مدلهای پیشنهادی عملکرد بسیار مناسبی داشته و می توانند به عنوان ابزاری مناسب برای تعیین مدول الاستیسیته بتن های با مقاومت معمولی و بالا مورد استفاده قرار گیرند. | ||
| کلیدواژه‌ها | ||
| بتن؛ مدول الاستیسیته؛ مقاومت فشاری؛ شبکه عصبی چند جمله ای؛ الگوریتم بهینه سازی علف هرز | ||
| عنوان مقاله [English] | ||
| New Models for Determining Concrete Elastic Modulus Considering Variation in Values of Compressive Strength | ||
| نویسندگان [English] | ||
| Sima Aramesh1؛ Pouyan Fakharian2 | ||
| 1Faculty Member, Department of Civil Engineering, Faculty of Semnan, Technical and Vocational University (TVU), Semnan, Iran | ||
| 2Faculty of Civil Engineering, Semnan University, Semnan, Iran | ||
| چکیده [English] | ||
| Modulus of elasticity has played an essential role in the analysis and design of reinforced concrete structures and is a fundamental property required to calculate the lateral deformation of structures. This study proposes new models for predicting the elastic modulus of normal - and high-strength concrete using a hybrid polynomial neural network-invasive weed optimization algorithm (PNN-IWO). This paper attempts to estimate the elastic modulus concrete in terms of compressive strength in compliance with conventional building codes. To examine the validity of the proposed models, a comprehensive evaluation has been performed between the elastic modulus results predicted by PNN-IWO, experimental data, and those determined using buildings codes and various models. The assessment is performed in terms of coefficient of determination, root mean square error, and mean absolute error. It should be noted that the mean absolute error of the proposed model for normal- and high-strength concrete were calculated as 9.02%, 3.8%, respectively. The results demonstrate that the proposed models have a very strong potential to predict the elastic modulus of both normal- and high-strength concrete within the range of the considered compressive strength. | ||
| کلیدواژه‌ها [English] | ||
| Concrete, Elastic modulus, Compressive strength, Polynomial neural network, Invasive weed optimization algorithm | ||
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| مراجع | ||
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