Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete
出版年份 2018 全文链接
标题
Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete
作者
关键词
-
出版物
Applied Sciences-Basel
Volume 8, Issue 11, Pages 2171
出版商
MDPI AG
发表日期
2018-11-07
DOI
10.3390/app8112171
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Durability performance of high-performance concrete made with recycled aggregates, fly ash and densified silica fume
- (2018) D. Pedro et al. CEMENT & CONCRETE COMPOSITES
- Understanding variability in recycled aggregate concrete mechanical properties through numerical simulation and statistical evaluation
- (2018) Anuruddha Jayasuriya et al. CONSTRUCTION AND BUILDING MATERIALS
- Mechanical properties, durability, and life-cycle assessment of concrete building blocks incorporating recycled concrete aggregates
- (2018) Zhanggen Guo et al. JOURNAL OF CLEANER PRODUCTION
- Five-phase modelling for effective diffusion coefficient of chlorides in recycled concrete
- (2018) Zhi Hu et al. MAGAZINE OF CONCRETE RESEARCH
- Durability of crushed fine recycled aggregate concrete assessed by permeability-related properties
- (2018) Luís Evangelista et al. MAGAZINE OF CONCRETE RESEARCH
- Life cycle assessment of recycled concretes: A case study in southern Italy
- (2018) Francesco Colangelo et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks
- (2018) Chengyao Liang et al. Advances in Materials Science and Engineering
- Life cycle assessment of concrete made with high volume of recycled concrete aggregates and fly ash
- (2018) Rawaz Kurda et al. RESOURCES CONSERVATION AND RECYCLING
- A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks
- (2016) S. Chithra et al. CONSTRUCTION AND BUILDING MATERIALS
- Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks
- (2015) Mehdi Nikoo et al. Advances in Materials Science and Engineering
- Effects of the old cement mortar attached to the recycled aggregate surface on the bond characteristics between aggregate and cement mortar
- (2014) D.S. Seo et al. CONSTRUCTION AND BUILDING MATERIALS
- Properties and composition of recycled aggregates from construction and demolition waste suitable for concrete production
- (2014) R.V. Silva et al. CONSTRUCTION AND BUILDING MATERIALS
- Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete
- (2013) Z.H. Duan et al. CONSTRUCTION AND BUILDING MATERIALS
- Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
- (2012) Z.H. Duan et al. CONSTRUCTION AND BUILDING MATERIALS
- Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks
- (2011) Rafat Siddique et al. ADVANCES IN ENGINEERING SOFTWARE
- Recycled aggregate concrete (RAC) – comparative analysis of existing specifications
- (2010) P. Gonçalves et al. MAGAZINE OF CONCRETE RESEARCH
- Parameters for assessing recycled aggregate and their correlation
- (2009) Vivian W. Y. Tam et al. WASTE MANAGEMENT & RESEARCH
- Analysis of durability of high performance concrete using artificial neural networks
- (2008) R. Parichatprecha et al. CONSTRUCTION AND BUILDING MATERIALS
- Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic
- (2008) Mustafa Sarıdemir et al. CONSTRUCTION AND BUILDING MATERIALS
- Modeling and Analysis of Concrete Slump Using Artificial Neural Networks
- (2008) Ashu Jain et al. JOURNAL OF MATERIALS IN CIVIL ENGINEERING
- Mechanical properties of 5-year-old concrete prepared with recycled aggregates obtained from three different sources
- (2008) S.-C. Kou et al. MAGAZINE OF CONCRETE RESEARCH
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started