Moment Capacity and Strength of Reinforced Concrete Members Using Stress- Strain Diagrams of Concrete and Steel
Publication Date
Sun Feb 03 2019
Journal Name
Journal Of The College Of Education For Women
Publication Date
Tue Mar 01 2022
Journal Name
Results In Engineering
(17)
Publication Date
Wed Jul 01 2020
Journal Name
Journal Of Environmental Engineering
(28)
(24)
Publication Date
Tue May 16 2023
Journal Name
Journal Of Engineering
Statistical Model for Predicting the Optimum Gypsum Content in Concrete
Optimum SO3 content (O.G.C)
total effective SO3 content
28-day age model
late age model
all age model
The problem of internal sulfate attack in concrete is widespread in Iraq and neighboring countries.This is because of the high sulfate content usually present in sand and gravel used in it. In the present study the total effective sulfate in concrete was used to calculate the optimum SO3 content. Regression models were developed based on linear regression analysis to predict the optimum SO3 content usually referred as (O.G.C) in concrete. The data is separated to 155 for the development of the models and 37 for checking the models. Eight models were built for 28-days age. Then a late age (greater than 28-days) model was developed based on the predicted optimum SO3 content of 28-days and late age. Eight developed models were built for all
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Publication Date
Mon Nov 24 2025
Journal Name
Scientific Reports
Bearing capacity improvement of medium clay soil using sodium silicate sand columns
Bearing capacity
Clay
Raft foundation
Sand column
Sodium silicate
Publication Date
Tue Jun 02 2026
Journal Name
Russian Physics Journal
Publication Date
Sat Sep 01 2018
Journal Name
2018 11th International Conference On Developments In Esystems Engineering (dese)
(5)
Publication Date
Mon Jun 15 2026
Journal Name
Journal Of Physical Education
Publication Date
Tue Mar 16 2021
Journal Name
International Journal For Computational Methods In Engineering Science And Mechanics
(6)
(1)
Publication Date
Fri Feb 21 2025
Journal Name
Applied System Innovation
Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
rutting
asphalt concrete
machine learning
model
uniaxial load
Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R
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(1)