scholarly journals Bioconc-Based Green Concrete Quality Treatment for Mass Concrete’s Low Heat Concrete

2020 ◽  
Author(s):  
Makno Basoeki
Keyword(s):  
Green Concrete ◽  
Concrete Quality

Green Concrete made with Recycled Concrete Aggregates: Fresh and Hardened Properties

2018 ◽  
Author(s):  
Driton R. Kryeziu ◽  
Armend Muja ◽  
Fisnik Kadiu ◽  
Visar Krelani ◽  
Besian Sinani ◽  
...  
Keyword(s):  
Recycled Concrete ◽  
Green Concrete ◽  
Concrete Aggregates ◽  
Recycled Concrete Aggregates ◽  
Hardened Properties

Establishment of Dedicated Concrete Plant in Site and Efforts to Improve the Concrete Quality

2018 ◽  
Vol 56 (2) ◽  
pp. 144-149
Author(s):  
T. Mukohara ◽  
N. Kanasaki ◽  
K. Watanabe ◽  
K. Kobayashi
Keyword(s):  
Concrete Quality

Study Towards Green Concrete for Better Sustainable Construction

2019 ◽  
Author(s):  
Kaustav Das ◽  
Trina Dutta ◽  
Subhojit Chattaraj ◽  
Aditya Dasgupta ◽  
Tania Mondal ◽  
...  
Keyword(s):  
Sustainable Construction ◽  
Green Concrete

Research of Green Concrete

2012 ◽  
Vol 214 ◽  
pp. 353-357
Author(s):  
Shu Zhen Yang
Keyword(s):  
Building Material ◽  
Present Situation ◽  
Green Concrete ◽  
Existing Problems ◽  
The World

Concrete is the most widely used building material in the world, while with the large amount use of concrete, the pollution is becoming more and more serious. While green concrete can overcome ordinary concrete's shortcomings. This paper will introduce green concrete's features and analyze its prospect. What's more, according to the present situation of green concrete, points out some existing problems of green concrete, to these problems, put forward some suggestions to its development.

scholarly journals Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

2021 ◽  
Vol 13 (5) ◽  
pp. 2867
Author(s):  
Muhammad Izhar Shah ◽  
Muhammad Nasir Amin ◽  
Kaffayatullah Khan ◽  
Muhammad Sohaib Khan Niazi ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Predictive Models ◽  
Parametric Study ◽  
Cross Validation ◽  
Strength Properties ◽  
Fine Aggregate ◽  
Agricultural Industry ◽  
Green Concrete ◽  
Concrete Properties ◽  
Water To Cement Ratio ◽  
Sugarcane Bagasse Ash

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.

scholarly journals A Review on the Mechanical Properties of the Green Concrete

2021 ◽  
Vol 1055 (1) ◽  
pp. 012012
Author(s):  
T S Mukesh ◽  
K Sampathkumar ◽  
V Kisshore
Keyword(s):  
Mechanical Properties ◽  
Green Concrete

scholarly journals Reinforced Concrete Bridge Deck Model Considering Delayed Exposure to Chlorides

2019 ◽  
Author(s):  
Petr Konečný ◽  
Petr Lehner ◽  
David Pustka
Keyword(s):  
Reinforced Concrete ◽  
Bridge Deck ◽  
Concrete Bridge ◽  
Reinforced Concrete Bridge ◽  
Corrosion Initiation ◽  
Chloride Induced Corrosion ◽  
Concrete Quality ◽  
Concrete Type ◽  
Concrete Bridge Deck ◽  
Delayed Exposure

The paper is focused on the model of the effect of delayed chloride exposure on the chloride induced corrosion initiation on ideal reinforced concrete bridge. The Finite Element-based numerical model is applied. The effect of concrete quality is expressed in the form of time dependent diffusion coefficient in order to evaluate the effect of concrete type as well as the effect of aging. The influence of extended chloride exposure on the corrosion initiation is introduced.

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