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Deep Learning Approach for Estimating Workability of Self‐Compacting Concrete from Mixing Image Sequences

https://doi.org/10.1155/2018/6387930
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
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A comparative study of machine learning approaches for modeling concrete failure surfaces
inferredcites →https://doi.org/10.1016/j.autcon.2017.01.016openalex-snapshot
Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions
inferredcites →https://doi.org/10.1016/j.cemconcomp.2014.08.001openalex-snapshot
Rock-filled concrete, the new norm of SCC in hydraulic engineering in China
inferredcites →https://doi.org/10.1016/j.cemconres.2008.12.005openalex-snapshot
Concrete mixing kinetics by means of power measurement
inferredcites →https://doi.org/10.1016/j.conbuildmat.2013.12.008openalex-snapshot
Development of a mix design method for SCC based on the rheological characteristics of paste
inferredcites →https://doi.org/10.1016/j.conbuildmat.2014.09.054openalex-snapshot
Machine learning in concrete strength simulations: Multi-nation data analytics
inferredcites →https://doi.org/10.1016/j.conbuildmat.2015.09.058openalex-snapshot
CaPrM: Carbonation prediction model for reinforced concrete using machine learning methods
inferredcites →https://doi.org/10.1016/j.conbuildmat.2017.02.047openalex-snapshot
A method for real-time moisture estimation based on self-compacting concrete workability detected during the mixing process
inferredcites →https://doi.org/10.1016/j.cscm.2016.11.002openalex-snapshot
Modelling the strength of lightweight foamed concrete using support vector machine (SVM)
inferredcites →https://doi.org/10.1016/j.eswa.2017.05.039openalex-snapshot
Deep learning for biological image classification
inferredcites →https://doi.org/10.1016/j.powtec.2007.07.006openalex-snapshot
Assessment of the mixing efficiency of solid mixtures by means of image analysis
inferredcites →https://doi.org/10.1016/j.proeng.2017.01.418openalex-snapshot
Development of an Automatic Detector of Cracks in Concrete Using Machine Learning
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Support vector machine based reliability analysis of concrete dams
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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/iccv.2015.135openalex-snapshot
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
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Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1109/tpami.2016.2599174openalex-snapshot
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
inferredcites →https://doi.org/10.1111/mice.12297openalex-snapshot
Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network
inferredcites →https://doi.org/10.1145/1102351.1102422openalex-snapshot
The cross entropy method for classification
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
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Long Short-Term Memory
inferredcites →https://doi.org/10.12989/cac.2014.13.6.781openalex-snapshot
Method for estimating workability of self-compacting concrete using mixing process images
inferredcites →https://doi.org/10.1371/journal.pone.0029740openalex-snapshot
Color-to-Grayscale: Does the Method Matter in Image Recognition?
inferredcites →https://doi.org/10.1617/s11527-006-9187-8openalex-snapshot
Monitoring of concrete homogenisation with the power consumption curve
inferredcites →https://doi.org/10.21437/interspeech.2013-734openalex-snapshot
Investigations on hessian-free optimization for cross-entropy training of deep neural networks
inferredcites →https://doi.org/10.3151/jact.1.5openalex-snapshot
Self-Compacting Concrete
inferredcites →https://doi.org/10.48550/arxiv.1506.04214openalex-snapshot
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Predicting the Slump of Industrially Produced Concrete from Mixture Proportions: Insights from Statistical Analysis, Data Visualization and Machine Learning
inferred← citeshttps://doi.org/10.1002/cepa.2965openalex-snapshot
Concrete 4.0 ‐ Sustainable concrete construction with digital quality control
inferred← citeshttps://doi.org/10.1002/suco.202100091openalex-snapshot
A novel strength behavior prediction model of self‐compacting concrete with foundry sand and hybrid fibers
inferred← citeshttps://doi.org/10.1002/suco.70308openalex-snapshot
Bridging the gap in concrete slump prediction: Scalable, generalized, and deployable machine learning approach
inferred← citeshttps://doi.org/10.1007/978-3-032-07738-7_1openalex-snapshot
Recent Advences of Artificial Intelligence in Civil Engineering
inferred← citeshttps://doi.org/10.1007/s00521-021-05836-8openalex-snapshot
Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC
inferred← citeshttps://doi.org/10.1007/s11831-025-10284-xopenalex-snapshot
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review
inferred← citeshttps://doi.org/10.1007/s13369-022-06586-yopenalex-snapshot
Prediction of Strength of Plain and Blended Cement Concretes Cured Under Hot Weather Using Quadratic Regression and ANN Tools
inferred← citeshttps://doi.org/10.1007/s41064-024-00303-0openalex-snapshot
Fresh Concrete Properties from Stereoscopic Image Sequences
inferred← citeshttps://doi.org/10.1016/j.autcon.2020.103432openalex-snapshot
In-situ concrete slump test incorporating deep learning and stereo vision
inferred← citeshttps://doi.org/10.1016/j.cemconres.2026.108336openalex-snapshot
Online rheological perception of fresh cementitious material toward intelligent construction: An energy transfer analysis approach
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.132647openalex-snapshot
Framework for rapid characterization of fresh properties of cementitious materials using point cloud and machine learning
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.134809openalex-snapshot
Deep Concrete Flow: Deep learning based characterisation of fresh concrete properties from open-channel flow using spatio-temporal flow fields
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.137709openalex-snapshot
Non-contact viscosity detection for cement slurry using laser scanning of wave motion under rod impact
inferred← citeshttps://doi.org/10.1016/j.cscm.2022.e00955openalex-snapshot
Soft computing approaches towards tensile strength estimation of GFRP rebars subjected to alkaline-concrete environment
inferred← citeshttps://doi.org/10.1016/j.cscm.2025.e04442openalex-snapshot
An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks via image recognition
inferred← citeshttps://doi.org/10.1016/j.dibe.2026.101022openalex-snapshot
Toward intelligent construction: AI-driven autonomous monitoring of fresh concrete flow behaviour
inferred← citeshttps://doi.org/10.1016/j.jobe.2022.104997openalex-snapshot
Predicting the slump of industrially produced concrete using machine learning: A multiclass classification approach
inferred← citeshttps://doi.org/10.1016/j.jobe.2026.116891openalex-snapshot
Rapid rheological characterization of fresh cement paste via surface gravity wave analysis: Concept and validation
inferred← citeshttps://doi.org/10.1016/j.jobe.2026.117542openalex-snapshot
Online prediction of fresh concrete workability via machine learning-based assessment of mixing power curves
inferred← citeshttps://doi.org/10.1016/j.measurement.2021.110073openalex-snapshot
Estimating workability of concrete with different strength grades based on deep learning
inferred← citeshttps://doi.org/10.1061/jmcee7.mteng-22587openalex-snapshot
Review of Research Methods for Real-Time Detection of Fresh Concrete Properties in the Mixing State Based on Machine Vision
inferred← citeshttps://doi.org/10.13189/cea.2023.110515openalex-snapshot
Self-Compacting Concrete (SCC) Strength Prediction via Optimized Duple-Deep-Learning Model and Distance Ranked Fire-Hawk Optimizer (DRFO)
inferred← citeshttps://doi.org/10.14801/jkiit.2022.20.1.159openalex-snapshot
Recognition and Analysis of Ready-Mixed Concrete Slump using Image Processing and Deep-Learning
inferred← citeshttps://doi.org/10.3390/app10238543openalex-snapshot
Estimating Slump Flow and Compressive Strength of Self-Compacting Concrete Using Emotional Neural Networks
inferred← citeshttps://doi.org/10.3390/s24144472openalex-snapshot
Estimating the Workability of Concrete with a Stereovision Camera during Mixing
inferred← citeshttps://doi.org/10.3390/su17178005openalex-snapshot
AI-Powered Advanced Technologies for a Sustainable Built Environment: A Systematic Review on Emerging Challenges
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Concrete flow transformer: predicting fresh concrete properties from concrete flow using vision transformers
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IMAGE-BASED DEEP LEARNING FOR RHEOLOGY DETERMINATION OF BINGHAM FLUIDS
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