Catalogue Search | MBRL
Search Results Heading
Explore the vast range of titles available.
MBRLSearchResults
-
DisciplineDiscipline
-
Is Peer ReviewedIs Peer Reviewed
-
Item TypeItem Type
-
SubjectSubject
-
YearFrom:-To:
-
More FiltersMore FiltersSourceLanguage
Done
Filters
Reset
174
result(s) for
"الخواص الميكانيكية"
Sort by:
Improvement of Mechanical Properties by Waste Sawdust Ash Addition into Soil
2016
The objective of this paper is to utilize waste sawdust ash in a beneficial way for geotechnical purposes. The amount of sawdust generated every year constitutes up to 10-13% of the total volume of wood log. Such an enormous amount serves as waste material for landfill and is not utilized. Effective utilization of waste sawdust ash to enhance the engineering properties of soil could result in a solution of the landfill problem, which was the objective of this study. Permeability and direct shear tests were conducted to analyze the impact of waste sawdust ash on the properties of soil. After noticing the behavior, the optimum quantity of (12%) SDA was selected and further compaction and shrinkage limit tests on soil with 12% SDA were conducted. Dry density of the soil was improved by 7.8%, permeability was reduced by 71.8% and shrinkage limit was increased. Further, there was an increase of 22.14% in the friction angle with the addition of 12% sawdust ash and shear strength parameters were improved significantly. Overall, SDA had a positive effect on the geotechnical properties of the soil and it can be used as admixture in soil. This will not only solve the waste disposal problem, but will also improve the strength characteristics of soil.
Journal Article
Prediction of Mechanical Properties of Reactive Powder Concrete by Using Artificial Neural Network and Regression Technique after the Exposure to Fire Flame
2015
An experimental work was carried out to investigate some mechanical properties of Reactive Powder Concrete (RPC) which are particularly required as input data for structural design. These properties include compressive strength, flexural strength, tensile strength and static modulus of elasticity. A combined laboratory and modeling study was undertaken to develop a database of the estimation ability of the effects of exposure to real fire flame on the mechanical properties of reactive powder concrete using 2 different models: artificial neural network (ANN) and regression techniques. Experimental results were used in the estimation models. After being subjected to high temperatures from 200 to 500°C, the residual mechanical properties were determined, and RPC was considerably spalled under high temperature. Exposing to high temperatures from 200 to 400°C, mechanical properties were enhanced more or less, which can be attributed to further hydration of cementitious materials activated by elevated temperature. It was found that RPC can be used at elevated temperatures up to 300°C for heating times up to 1 hour, taking into consideration the loss of strength. Finally, prediction performances of reactive powder concrete single and multiple variable regression equations were developed, and ANN was compared. According to this comparison, best prediction performance which belongs to ANN was improved.
Journal Article