Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
出版年份 2022 全文链接
标题
Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
作者
关键词
Machine learning, Concrete strength, Missing data, Data imputation, SHAP
出版物
CEMENT & CONCRETE COMPOSITES
Volume 128, Issue -, Pages 104414
出版商
Elsevier BV
发表日期
2022-01-22
DOI
10.1016/j.cemconcomp.2022.104414
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- A new method for exploring gene–gene and gene–environment interactions in GWAS with tree ensemble methods and SHAP values
- (2021) Pål V. Johnsen et al. BMC BIOINFORMATICS
- Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach
- (2020) Luchun Yan et al. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
- Toward intelligent construction: Prediction of mechanical properties of manufactured-sand concrete using tree-based models
- (2020) Junfei Zhang et al. JOURNAL OF CLEANER PRODUCTION
- Explainable artificial intelligence model to predict acute critical illness from electronic health records
- (2020) Simon Meyer Lauritsen et al. Nature Communications
- Elucidating the Behavior of Nanophotonic Structures through Explainable Machine Learning Algorithms
- (2020) Christopher Yeung et al. ACS Photonics
- Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach
- (2020) Sujith Mangalathu et al. ENGINEERING STRUCTURES
- Role of Limestone Powder in Early-Age Cement Paste Considering Fineness Effects
- (2020) Qiang Ren et al. JOURNAL OF MATERIALS IN CIVIL ENGINEERING
- A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)
- (2020) Furqan Farooq et al. Applied Sciences-Basel
- Efficient machine learning models for prediction of concrete strengths
- (2020) Hoang Nguyen et al. CONSTRUCTION AND BUILDING MATERIALS
- Machine learning-guided synthesis of advanced inorganic materials
- (2020) Bijun Tang et al. Materials Today
- Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning
- (2020) Gideon A. Lyngdoh et al. Scientific Reports
- Multi-level diffusion model for manufactured sand mortar considering particle shape and limestone powder effects
- (2019) Qiang Ren et al. CONSTRUCTION AND BUILDING 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
- Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models
- (2019) Rachel Cook et al. JOURNAL OF MATERIALS IN CIVIL ENGINEERING
- Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
- (2019) De-Cheng Feng et al. CONSTRUCTION AND BUILDING MATERIALS
- Computational design optimization of concrete mixtures: A review
- (2018) M.A. DeRousseau et al. CEMENT AND CONCRETE RESEARCH
- 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
- Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods
- (2018) Benjamin A. Young et al. CEMENT AND CONCRETE RESEARCH
- Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
- (2018) Scott M. Lundberg et al. Nature Biomedical Engineering
- Cements in the 21st century: Challenges, perspectives, and opportunities
- (2017) Joseph J. Biernacki et al. JOURNAL OF THE AMERICAN CERAMIC SOCIETY
- Experimental study on long-term compressive strength of concrete with manufactured sand
- (2016) Xinxin Ding et al. CONSTRUCTION AND BUILDING MATERIALS
- Direct Carbonation of Ca(OH)2 Using Liquid and Supercritical CO2: Implications for Carbon-Neutral Cementation
- (2015) Kirk Vance et al. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
- Assessment of concrete compressive strength prediction models
- (2015) Fayez Moutassem et al. KSCE Journal of Civil Engineering
- Grand Challenges in Structural Materials
- (2015) John L. Provis Frontiers in Materials
- Effect of partial and total replacement of siliceous river sand with limestone crushed sand on the durability of mortars exposed to chemical solutions
- (2013) M. Bederina et al. CONSTRUCTION AND BUILDING MATERIALS
- Properties of self-compacting mortar made with various types of sand
- (2012) Benchaa Benabed et al. CEMENT & CONCRETE COMPOSITES
- Embodied carbon dioxide in concrete: Variation with common mix design parameters
- (2012) Phil Purnell et al. CEMENT AND CONCRETE RESEARCH
- Influence of porosity on compressive and tensile strength of cement mortar
- (2012) Xudong Chen et al. CONSTRUCTION AND BUILDING MATERIALS
- Influence of aggregates grading and water/cement ratio in workability and hardened properties of mortars
- (2011) Vladimir G. Haach et al. CONSTRUCTION AND BUILDING MATERIALS
- Influence of manufactured sand characteristics on strength and abrasion resistance of pavement cement concrete
- (2011) Beixing Li et al. CONSTRUCTION AND BUILDING MATERIALS
- A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks
- (2010) Marek Słoński COMPUTERS & STRUCTURES
- Rheological and mechanical properties of mortars prepared with natural and manufactured sands
- (2008) D.D. Cortes et al. CEMENT AND CONCRETE RESEARCH
- Strength and durability of concrete incorporating crushed limestone sand
- (2008) B. Menadi et al. CONSTRUCTION AND BUILDING MATERIALS
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