منصة إيفاد العلمية

Munir Altamai

إرسال رسالة

التخصص: هندسة مدنية

الجامعة: خليج السدرة

النقاط:

14.5

معامل الإنتاج البحثي

الخبرات العلمية

عضو هيئة تدريس
عميد كلية الهندسة
مدير ادارة الشوؤن الفنية والمشروعات
مدير مكتب تطوير وتجهيز المعامل

الأبحاث المنشورة

Correlation Between Different Properties of Recycled Aggregate and Recycled Aggregate Concrete

المجلة: AIP

سنة النشر: 2019

تاريخ النشر: 2019-08-30

The use of recycled aggregate (RA) in concrete has been increasing popularity over the last decades. Researches have been taken place in this field to develop information about strength properties of recycled aggregate concrete (RAC). In this research, three RA samples collected from different sources were used to produce different RAC mixes to investigate the correlation between RA and RAC properties. Two control mixes were made with two different water-to-cement (w/c) ratios (0.5 and 0.6). RA was placed in concrete by 0%, 50% and 100% to produce 12 different RAC mixes with the same w/c ratios used in the control mixes. During the mix deign it was focused on accurately compensating for the high-water absorption of RA to keep the effective w/c ratio constant for all concrete mixes. The main RA properties studied in this research were specific gravity, water absorption and porosity. The hardened properties investigated were the compressive strength, the flexural strength and the tensile strength. Typical relationships between RA properties and RAC characteristics were formulated. The results indicated strong correlation between RA properties and RAC characteristics. It should be highlighted that irrespective of RA source, all RAC mixes exhibit a very good workability similar to that of the normal concrete. The results also showed that adding extra quantities of water to the concrete mixes to compensate for the high-water absorption of RA will eliminate the effect of RA source on RAC properties since the strength properties of all RAC mixes were almost the same. It was concluded that the water absorption capacity of RA is the most important parameter that significantly affects RAC properties.

Development of Composite Mortars based On Lime, Blastfurnce Slag and Fly ash

المجلة: AIP

سنة النشر: 2019

تاريخ النشر: 2019-08-30

Slag and fly ash are the most commonly used industrial materials as a replacement for mortar which helped to reduce CO2 emissions. The activation of fly ash and slag with lime mortars have been studies in this paper. The parameters of the processes studies are: two kind of fly ash (Drax and Didcot), curing water pure and humidity room at 65%, and fly ash to-slag ratio (0/100, 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, 90/10 and 100/0). The main tests are flexural strength and compressive strength. Six samples were prepared for each percentage for both kinds of fly ash, and then curried for 56 days to be ready for test. The results showed that the influence of water pure curing on the development of strength is higher than the influence of humidity room. The samples cured in water showed higher strength than those cured in the humidity room. At 56 days, the highest compressive strength and flexural strength were obtained for samples having 30% of Didcot.

Tests of self-compacting concrete filled elliptical steel tube columns

المجلة: Thin-Walled Structures

سنة النشر: 2016

تاريخ النشر: 2016-09-21

This paper presents an experimental study into the axial compressive behaviour of self-compacting concrete filled elliptical steel tube columns. In total, ten specimens, including two empty columns, with various lengths, section sizes and concrete strengths were tested to failure. The experimental results indicated that the failure modes of the self-compacting concrete filled elliptical steel tube columns with large slenderness ratio were dominated by global buckling. Furthermore, the composite columns possessed higher critical axial compressive capacities compared with their hollow section companions due to the composite interaction. However, due to the large slenderness ratio of the test specimens, the change of compressive strength of concrete core did not show significant effect on the critical axial compressive capacity of concrete filled columns although the axial compressive capacity increased with the concrete grade increase. The comparison between the axial compressive load capacities obtained from experimental study and prediction using simple methods provided in Eurocode 4 for concrete-filled steel circular tube columns showed a reasonable agreement. The experimental results, analysis and comparison presented in this paper clearly support the application of self-compacting concrete filled elliptical steel tube columns in construction engineering practice.

Prediction of drying shrinkage and compressive strength of self-compacting concrete using artificial neural networks

المجلة: Cement & Concrete Science 2

سنة النشر: 2014

تاريخ النشر: 2014-09-21

Self-compacting concrete (SCC) is characterised by the ability to flow under its own weight without vibration, pass through intricate geometrical configurations, and resist segregation. SCC mixtures are usually designed with high volumes of paste, large quantities of mineral fillers and high range water reducing admixtures. These modifications in composition affect the behaviour of concrete in its hardened state such as drying shrinkage that is considered as a major concern for concrete deterioration. The main aim of this study is to develop multi-layer back propagation artificial neural network (ANN) models for prediction of drying shrinkage strains and compressive strength of SCC. A comprehensive database has been collected from different sources in the literature and used to train and test the developed ANN. Different input neurons representing water binder ratio (W/B), cement content (C), fine aggregate (S), coarse aggregate (CA), superplasticizer (SP), fly ash (FA), ground granulated blast slag (GGBS), silica fume (SF), metakaolin (MK) and limestone (LS) were adopted, whereas the ANN outputs were the compressive strength and drying shrinkage strains. A parametric study was also conducted to study the trend of various SCC ingredients on the drying shrinkage strains and compressive strength of SCC.

Load Capacity Prediction of Simply Supported CFRP Deep Beams using Artificial Neural Networks

المجلة: AIP

سنة النشر: 2024

تاريخ النشر: 2024-08-15

Anticipating the load carrying capacity of deep beams with the aim of controlling the predominant shear failure led to the consideration of the behaviour of deep beams, which is dissimilar from the behavior of normal beams. Therefore, the usual design methods are not safe due to the fact that theoretical stresses are less than actual stresses. As a result, special design methods based on nonlinear stress distribution are used, which occur even in the elastic load range. Although CFRP has been widely used in reinforced concrete structures to increase load capacity, methods for reasonable estimation of shear strength for deep beams are still unavailable. This research mainly focuses on developing a multi-layer back propagation artificial neural network (ANN) model to predict the load capacity of simply supported deep beams reinforced with CFRP. Thorough experimental results have been compiled from several previous studies to build up a database that can be used to train and test the developed ANN model. Different input neurons were adopted including the shear span to depth ratio (a/d), the effective concrete compressive strength of concrete (f_c^'), the cross-sectional area of diagonal strut (Astrut) and the angle of the inclined strut (sinɸ). On the other hand, the ANN output was the ultimate load of simply supported deep beams. Parametric research is also carried out to determine the extent to which certain parameters employed in the created neural network model have an impact on the load capacity. The results showed that ANN can reasonably estimate the load capacity of simply supported CFRP deep beams reinforced with CFRP.

Load Capacity Prediction of Continuous Deep Beams using Artificial Neural Networks

المجلة: مجلة كلية الهندسة جامعة عمر المختار

سنة النشر: 2024

تاريخ النشر: 2024-07-06

Deep beams are categorized by the current design codes as a discontinuity region in which the strain distribution is nonlinear. Therefore, the classical theory of elasticity can only predict the behaviour of deep beams before cracking while the major redistribution of stresses starts after cracking. In this case, the usual design methods are not safe due to the fact that theoretical stresses are less than actual stresses. As a result, special design methods based on nonlinear stress distribution are used, which occur even in the elastic load range. Methods for accurate prediction of shear strength for continuous deep beams are still not accurate and need more validation. The main aim of this study is to develop a multi-layer back propagation artificial neural network (ANN) model to predict the load capacity of continuous deep beams. A comprehensive database has been collected from different sources in the literature and used to train and test the developed ANN model. Different input neurons were adopted including the shear span-to-depth ratio (a/d), the effective compressive strength of concrete ( ), the horizontal shear reinforcement ratio ( ) and the vertical reinforcement ratio ( ). On the other hand, the ANN output was the ultimate load of continuous deep beams. A parametric study is also conducted to quantify the degree of influence of some of the different parameters used in the developed neural network model. The results showed that ANN can reasonably predict the load capacity of continuous deep beams. Moreover, ANN results showed almost the same trend compared to the results predicted by ACI shear provisions and Model from Literature.

تصميم خلطات الخرسانة ذاتية الدمك باستخدام شبكة االعصاب االصطناعية

المجلة: مجلة كلية الهندسة جامعة عمر المختار

سنة النشر: 2018

تاريخ النشر: 2018-11-21

الخرسانة ذاتية الدمك أصبحت شائعة االستخدام في مجاالت الهندسة اإلنشائية في معظم دول العالم خصوصا عندما يكون استخدام الخرسانة االعتيادية غير ممكن كأن يكون المجال غير متسع الستخدام أدوات الدمك كما في األعمدة المركبة الطويلة و العتبات العميقة والمنشآت المائية وغيرها. ورغم هذا االنتشار الواسع الذي يتزايد يوما بعد يوم فإنه ال توجد حتى اآلن مواصفات قياسية معتمدة لتصميم خلطات هذا النوع من الخرسانة كما هو متوفر في حالة الخرسانة االعتيادية. باإلضافة إلى ذلك ورغم تواجد بعض الطرق لتصميم خلطات الخرسانة ذاتية الدمك إال أن بعض هذه الطرق من الصعب جدا تتبعها النها لم يتم نشرها بكافة تفاصيلها في حين أن البعض االخر يحتاج إلى إستخدام برامج خاصة بها وهذه البرامج عالية التكلفة ويصعب الحصول على رخصة لالشتراك بها. لذا فإن معظم البحاث في هذا المجال اتجهوا إلى استخدام البرامج العلمية البسيطة والمتوفرة لتصمبم الخلطات للخرسانة ذاتية الدمك ومن هذه البرامج شبكة األعصاب االصطناعية )ANN). شبكة األعصاب االصطناعية اجتاحت العديد من المجاالت الهندسية منذ أواخر الثمانينات وازداد استخدامها مؤخرا في الهندسة المدنية في عدة مجاالت مثل الدراسات البيئية والطرق والتربة وغيرها. في هذه الورقة سيتم استخدام شبكة األعصاب االصطناعية لتصميم الخلطات للخرسانة ذاتية الدمك للحصول على المقاومة المطلوبة بطريقة مبسطة وبدقة عالية جدا بعيدا عن الطرق المعقدة األخرى. هذا البرنامج يعمل بطريقة المدخالت و الهدف بحيت يتم تغذية البرنامج بعدد من العينات وبعد ذلك يتم تشغيل البرنامج عدة مرات من أجل الحصول علي عالقة خطية تكون قريبة من الهدف. في هذا البحث تم تجميع عدد 240 عينة من دراسات سابقة الستخدامها في تطوير البرنامج. عدد المتغيرات التي تم استخدامها في تطوير البرنامج ثمانية متغيرات و هي االسمنت, الغبار, الخبث, نسبة الماء الى المواد الرابطة, المضافات, كمية الرمل, كمية الركام الخشن و المطاط و الهدف كان مقاومة انضغاط الخرسانة. تم تشغيل البرنامج و كانت النتائج قريبة جدا إلي الهدف و نسبة الخطا كانت أقل من %10 . و للتحقق من صحة البرنامج تم استخدامه لدراسة العالقة بين مقاومة الخرسانة و كال من االسمنت, نسبة الماء الى المواد الرابطة وكمية الركام و كانت كل العالقات متطابقة مع المتوقع مما يدل علي إمكانية استخدام شبكة االعصاب االصطناعية في تصميم الخرسانة ذاتية الدمك.