标题
Classifying High Strength Concrete Mix Design Methods Using Decision Trees
作者
关键词
-
出版物
Materials
Volume 15, Issue 5, Pages 1950
出版商
MDPI AG
发表日期
2022-03-07
DOI
10.3390/ma15051950
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。- Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete
- (2022) Fadi Almohammed et al. Materials
- Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms
- (2022) Meijun Shang et al. Materials
- Investigating Trends and Costs Associated with Designing Concrete Mixes Using Different Methods by Computer Programs
- (2022) Mohammed Abdul Qader et al. Advances in Civil Engineering
- Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm
- (2021) Ayaz Ahmad et al. Materials
- Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach
- (2021) Hongwei Song et al. Materials
- Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning
- (2021) Xu Huang et al. Materials
- Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP
- (2021) Afnan Nafees et al. Materials
- Machine Learning Techniques in Concrete Mix Design
- (2019) Patryk Ziolkowski et al. Materials
- A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm
- (2019) Qinghua Han et al. CONSTRUCTION AND BUILDING MATERIALS
- Compressive strength prediction of recycled concrete based on deep learning
- (2018) Fangming Deng et al. CONSTRUCTION AND BUILDING MATERIALS
- A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete
- (2018) Dac-Khuong Bui et al. CONSTRUCTION AND BUILDING MATERIALS
- Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques
- (2016) Iman Mansouri et al. MATERIALS AND STRUCTURES
- Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model
- (2015) Ehsan Ghafari et al. JOURNAL OF MATERIALS IN CIVIL ENGINEERING
- Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction
- (2013) Halil Ibrahim Erdal ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks
- (2011) Rafat Siddique et al. ADVANCES IN ENGINEERING SOFTWARE
- Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques
- (2010) Jui-Sheng Chou et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Neural networks for predicting compressive strength of structural light weight concrete
- (2009) Marai M. Alshihri et al. CONSTRUCTION AND BUILDING MATERIALS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now