دانلود و نمایش مقالات مرتبط با Masonry::صفحه 1
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نتیجه جستجو - Masonry

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Determination of the interfacial cohesive material law for SRG composites bonded to a masonry substrate
تعیین قانون ماده چسبندگی میان سطحی برای کامپوزیت های SRG که به یک بستر سنگ تراشی متصل می شوند-2020
Fiber reinforced cementitious matrix (FRCM) composites, also known as textile reinforced matrix (TRM) composites, are a suitable alternative to fiber reinforced polymer (FRP) composites to strengthen reinforced concrete and masonry structures. In the toolbox of FRCMs, a recentlydeveloped composite that employs high-strength steel fibers embedded in a hydraulic mortar is particular appealing for applications on historical masonry constructions. This type of composite is known as steel reinforced grout (SRG). In this paper, an extensive experimental work is presented. Single-lap shear tests are performed to study the debonding of SRG strips from a masonry substrate, which is the critical failure mode for strengthening applications. For SRGs, debonding typically occurs at the fiber-matrix interface. A large scatter of the experimental results is observed, which is related to the variability of hydraulic mortars and their ability to impregnate the fibers. Although strain gauges can be applied directly to the fibers to obtain the experimental strain profile along the fibers, because of the presence of the matrix these measurements are complex and in some cases not reliable. Thus, indirect method based on the global response of the test is proposed to obtain the interfacial properties.
Keywords: SRG | Debonding | Cohesive Material Law | Masonry
مقاله انگلیسی
2 یک مدل پرتوی پیشرفته برای تجزیه و تحلیل دیوارهای ساختمانی (بنایی)
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 27
پس ‌زمینه: برخی انواعی از ساختمان‌های بنایی (به عنوان مثال، برج‌ها یا دیوارها با دهانه ها) را می توان به طور منطقی از طریق تیر و یا چارچوب ساده مورد مطالعه قرار داد. برای این ساختارها، مکانیزم‌های برشی اغلب نقش مهمی در القای خرابی (شکست) و فروریختگی (سقوط) ایفا می‌کنند.
هدف: این مقاله یک مدل پرتو غنی برای مطالعه پاسخ داخل صفحه دیوارها ارائه می‌کند. در ابتدا برای ستون‌های بنایی، برج‌ها و سازه‌های ساده بنایی به طور کلی، این مدل به منظور دستیابی به مکانیزم‌های شکست برشی، به علاوه مکانیزم‌های خمشی، اصلاح می‌شود.
روش‌ها: شروع با یک مدل تنش - بعدی، یک دامنه قدرت در سطح تنش محوری و مماسی، اضافه شده ‌است، که با محدود کردن مولفه برشی تنش با توجه به هر جهت ممکن و تنش فشاری اصلی تعریف شده ‌است.
نتایج: این مدل، که در المان محدود (FEM) و با کد محاسباتی MADY اجرا شد، زمان‌های محاسباتی کوتاهی در مطالعه پاسخ صفحات منفرد و همچنین دیوارها با دهانه فراهم می‌آورد.
نتیجه‌گیری: مقایسه با نتایج تجربی حاصل از ادبیات و برخی نتایج عددی از مدل‌های ۲ بعدی اصلاح ‌شده کارایی و دقت پیش‌بینی‌های مدل را از نظر واکنش کلی و محلی نشان می‌دهد.
کلمات کلیدی: بنایی (مصالح ساختمانی) | پانل‌ها | غیرخطی | المان محدود (اجزای محدود) | مدل پرتو | قالب معادل | رفتار درون صفحه ای
مقاله ترجمه شده
3 Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning
طبقه بندی حالت های شکست در هواپیما برای قاب های بتونی مسلح با استفاده از یادگیری ماشین-2019
The failure modes of reinforced concrete frame structures with masonry infill panels have strong implications to their overall seismic performance. This paper explores a data-driven approach to classifying the in-plane failure modes of infill frames by employing machine learning methods. To this end, an experimental database consisting of 114 infill frame specimens is constructed. Six machine learning algorithms are implemented and evaluated for failure-mode classification using nine structural parameters as input variables. The validation results indicate that most of the models are able to achieve more than 80% prediction accuracy, with the highest accuracy of 85.7% achieved by the Adaptive Boosting and Support Vector Machine algorithms.
Keywords: Masonry infills | Failure mode | Reinforced concrete frames | Seismic performance | Machine learning
مقاله انگلیسی
4 Comparative evaluation of MFP and RBF neural networks’ ability for instant estimation of r/c buildings’ seismic damage level
ارزیابی مقایسه ای توانایی شبکه های عصبی MFP و RBF در برآورد فوری میزان آسیب لرزه ای ساختمانهای r / c-2019
The problem of the seismic damage prediction of reinforced concrete (r/c) buildings utilizing two types of Artificial Neural Networks (ANN) is investigated in the present paper. More specifically, the problem is formulated and solved in terms of the Function Approximation problem as well as of the Pattern Recognition problem using Multilayer Feedforward Perceptron Networks (MFP) and Radial-Basis Function (RBF) networks. The required training data-sets are created by means of Nonlinear Time History Analyses of 90 r/c buildings which are subjected to 65 earthquakes. The selected buildings differ in total height, in structural system, in structural eccentricity as well as the existence or not of masonry infills. The seismic damage index which is used to describe the seismic damage state is the Maximum Interstorey Drift Ratio. The influence of the parameters which are used for the configuration and the training of MFP and RBF networks on the reliability of their predictions is also investigated. The generalization ability of the best configured ANNs is examined by means of two categories of seismic scenarios. The most significant conclusion that turned out is that the trained ANNs can reliably and rapidly classify the r/c buildings into pre-defined damage classes provided they are appropriately configured.
Keywords: Artificial neural networks | MFP networks | RBF networks | Seismic damage prediction | Structural vulnerability assessment | Reinforced concrete buildings
مقاله انگلیسی
5 Automatic damage detection of historic masonry buildings based on mobile deep learning
تشخیص خودکار آسیب ساختمانهای سنگ تراشی بر اساس یادگیری عمیق سیار-2019
Vision-based manual inspection technology for identifying and assessing superficial damage of historic buildings is time- and labor-consuming. To overcome these limits, this paper proposed a novel automatic damage detection technique using Faster R-CNN model based on ResNet101 framework to detect two categories of damage (efflorescence and spalling) for historic masonry structures. 33 different cases were studied, and the best case shown an average precision (AP) of 0.999 and 0.900 for efflorescence and spalling damage respectively, with a 0.950 mean AP. Moreover, an Internet Protocol (IP) webcam damage detection system combined with workstation was developed to detect the damage in real-time, and an automatic damage detection system based on smartphones was developed, which can realize real-time damage detection of brick masonry buildings. In addition, two on-site experiments were carried out on real masonry buildings to verify the feasibility and effectiveness of the system. Consequently, it was demonstrated that the proposed method was considerably automatic, efficient, and reliable for damage detection of historic masonry buildings and, ultimately, contributing to the management and protection of historic buildings.
Keywords: Automatic damage detection | Historic masonry buildings | Deep learning | Mobile detection
مقاله انگلیسی
6 Principles of the roof cut short-arm beam mining method (110 method) and its mining-induced stress distribution
اصول سقف روش معاينه پرتو برش کوتاه دست (روش 110) و توزیع تنش ناشی از معادن آن-2018
Since the 1960s, mining science and technology in China has experienced two technical innovations, i.e. the ‘‘Masonry Beam Theory (MBT)” and ‘‘Transfer Rock Beam Theory (TRBT)”. Based on those theories, the conventional mining method (being called the 121 mining method) was established, consisting of exca vating two tunnels with a pillar left for mining a working panel. However, with increasing mining depth, engineering geological disasters in the underground caverns have been frequently encountered. In addi tion, the use of the coal-pillar mining results in a large amount of coal resources unexploited. In order to address the problems above, the ‘‘Roof Cut Short-Arm Beam Theory (RCSBT), being called the 110 mining method)” was proposed by He Manchao in 2008. The 110 mining method features the mining of one coal seam panel, excavating necessarily only one roadway tunnel and leaving no pillars. Realization of the 110 mining method includes the following steps: (1) directional pre-splitting roof cutting, (2) supporting the roof by using high Constant Resistance Large Deformation bolt/cable (CRLD), and (3) blocking gangue by hydraulic props. This paper presents an overview of the principles, techniques and application of the 110 mining method. Special emphasis is placed on the numerical simulation of the geostress distribution found in the mining panel using the 110 method compared to that of the 121 method. In addition, the stress distribution on the ‘‘short beam” left by the roof cutting when performing the 110 method was also investigated using both numerical simulation and theoretical formulation.
Keywords: Mining innovation ، 121 mining method ، Cutting cantilever beam theory ، Non-pillar mining ، 110 mining method
مقاله انگلیسی
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