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Predicting the Slump of Industrially Produced Concrete from Mixture Proportions: Insights from Statistical Analysis, Data Visualization and Machine Learning
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The 43 checked references that resolve
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Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models
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Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete
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Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods
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no DOI — not checkedref4
no DOI — not checkedImplementation of the use of hydration stabilizer admixtures at a ready-mix concrete plant, Case Stud
no DOI — not checkedMaterials discovery and design using machine learning
no DOI — not checkedPrediction of concrete compressive strength and slump by machine learning methods
no DOI — not checkedIn-situ concrete slump test incorporating deep learning and stereo vision
no DOI — not checkedVisualization of concrete slump flow using the kinect sensor
no DOI — not checkedDeep learning approach for estimating workability of self-compacting concrete from mixing image sequences
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no DOI — not checkedSupporting regularized logistic regression privately and efficiently
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no DOI — not checkedA comparison of MCC and CEN error measures in multi-class prediction
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