کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
6480856 1428772 2017 18 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
An investigation on effect of partial replacement of cement by waste marble slurry
ترجمه فارسی عنوان
تحقیق در مورد تأثیر جایگزینی سیمان توسط دوغاب سنگ مرمر زباله
کلمات کلیدی
دوغاب سنگ مرمر زباله، جایگزینی سیمان، روند هیدراتاسیون، خواص بتنی تازه و سخت شده، طول عمر شبکه های عصبی مصنوعی،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی عمران و سازه
چکیده انگلیسی


- This paper presents an experimental study on use of marble slurry as partial replacement of cement.
- Indigenous fabrication of equipment for percentage air content, Figg's air and water permeability and surface resistivity.
- Marble slurry replacement percentages kept as 10%, 15%, 20%, 25%.
- Experimental trials on under reinforced beams for flexure and bending.
- Compressive strength prediction model using Artificial Neural Networks.

In this study, waste marble slurry from Makrana region of Rajasthan in India is characterized for various physiochemical properties and used to replace cement partially by weight in concrete. Effects of marble slurry on hydration process, fresh and hardened concrete properties and durability properties using indigenously fabricated equipment are investigated. Effect of particle size of marble slurry on compressive strength and experimental trials on reinforced concrete with dried marble slurry are also conducted. No significant effect on characteristics of cement pastes is noted. Drying shrinkage is found to decrease and strength of mortar improves for a certain percentage replacement. Marble slurry is found to show filler effect by giving the concrete a denser and even structure. It is observed that the mechanical properties of concrete enhanced with incorporation of dried marble slurry for up to 15% replacement. The quality of concrete improves as per ultrasonic pulse velocity and durability tests. Reinforced concrete with marble slurry also shows promising results with increased bond strength. Finally, a compressive strength prediction model is developed using artificial neural network (ANN). The results for ANN are plotted as experimentally evaluated 28 days' compressive strength versus predicted compressive strength.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Construction and Building Materials - Volume 134, 1 March 2017, Pages 471-488
نویسندگان
, , ,