On the public citation graph

Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods

https://doi.org/10.3390/ma15196740

0 signed · 75 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1016/j.cemconcomp.2021.104242openalex-snapshot

Mixture design methods for ultra-high-performance concrete - a review

inferredcites →https://doi.org/10.1016/j.cemconres.2021.106614openalex-snapshot

Estimation of constituent properties of concrete materials with an artificial neural network based method

inferredcites →https://doi.org/10.1016/j.conbuildmat.2021.122576openalex-snapshot

Automated bughole detection and quality performance assessment of concrete using image processing and deep convolutional neural networks

inferredcites →https://doi.org/10.1016/j.conbuildmat.2021.125970openalex-snapshot

Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete

inferredcites →https://doi.org/10.1016/j.conbuildmat.2022.126839openalex-snapshot

Prediction of mechanical and durability characteristics of concrete including slag and recycled aggregate concrete with artificial neural networks (ANNs)

inferredcites →https://doi.org/10.1016/j.jobe.2018.01.007openalex-snapshot

Compressive strength prediction of environmentally friendly concrete using artificial neural networks

inferredcites →https://doi.org/10.1016/j.jobe.2021.102497openalex-snapshot

Reliability analysis of strength models for short-concrete columns under concentric loading with FRP rebars through Artificial Neural Network

inferredcites →https://doi.org/10.1016/s0008-8846(00)00345-8openalex-snapshot

Prediction of compressive strength of concrete by neural networks

inferredcites →https://doi.org/10.1016/s0893-6080(05)80056-5openalex-snapshot

A scaled conjugate gradient algorithm for fast supervised learning

inferredcites →https://doi.org/10.1088/1742-6596/2124/1/012017openalex-snapshot

Selection of a rational formulation of lightened concretes on combined aggregates

inferredcites →https://doi.org/10.1088/1757-899x/537/2/022038openalex-snapshot

Durability prognostication of ferroconcrete structures on the basis of neural indistinct networks

inferredcites →https://doi.org/10.3390/app11167284openalex-snapshot

Optimization of Composition and Technological Factors for the Lightweight Fiber-Reinforced Concrete Production on a Combined Aggregate with an Increased Coefficient of Structural Quality

inferredcites →https://doi.org/10.3390/app11167700openalex-snapshot

Development of Crack Width Prediction Models for RC Beam-Column Joint Subjected to Lateral Cyclic Loading Using Machine Learning

inferredcites →https://doi.org/10.3390/app11199293openalex-snapshot

Influence of Composition and Technological Factors on Variatropic Efficiency and Constructive Quality Coefficients of Lightweight Vibro-Centrifuged Concrete with Alkalized Mixing Water

inferredcites →https://doi.org/10.3390/app112210544openalex-snapshot

Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete

inferredcites →https://doi.org/10.3390/app112211077openalex-snapshot

A Novel Artificial Neural Network to Predict Compressive Strength of Recycled Aggregate Concrete

inferredcites →https://doi.org/10.3390/app12010104openalex-snapshot

Improvement of Strength and Strain Characteristics of Lightweight Fiber Concrete by Electromagnetic Activation in a Vortex Layer Apparatus

inferredcites →https://doi.org/10.3390/buildings12010065openalex-snapshot

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

inferredcites →https://doi.org/10.3390/buildings12020132openalex-snapshot

Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms

inferredcites →https://doi.org/10.3390/cryst11101157openalex-snapshot

Applying Artificial Intelligence to Improve On-Site Non-Destructive Concrete Compressive Strength Tests

inferredcites →https://doi.org/10.3390/cryst12020259openalex-snapshot

Interpretable Machine Learning Models for Punching Shear Strength Estimation of FRP Reinforced Concrete Slabs

inferredcites →https://doi.org/10.3390/ma12091396openalex-snapshot

Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks

inferredcites →https://doi.org/10.3390/ma12172678openalex-snapshot

Prediction Model for Mechanical Properties of Lightweight Aggregate Concrete Using Artificial Neural Network

inferredcites →https://doi.org/10.3390/ma13112445openalex-snapshot

Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

inferredcites →https://doi.org/10.3390/ma14154222openalex-snapshot

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

inferredcites →https://doi.org/10.3390/ma14164518openalex-snapshot

Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach

inferredcites →https://doi.org/10.3390/ma14195637openalex-snapshot

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

inferredcites →https://doi.org/10.3390/ma14206172openalex-snapshot

Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm

inferredcites →https://doi.org/10.3390/ma14216311openalex-snapshot

Deep-Learning-Based Segmentation of Fresh or Young Concrete Sections from Images of Construction Sites

inferredcites →https://doi.org/10.3390/ma14237172openalex-snapshot

Artificial Neural Network (ANN) and Finite Element (FEM) Models for GFRP-Reinforced Concrete Columns under Axial Compression

inferredcites →https://doi.org/10.3390/ma14237347openalex-snapshot

Nanomodification of Lightweight Fiber Reinforced Concrete with Micro Silica and Its Influence on the Constructive Quality Coefficient

inferredcites →https://doi.org/10.3390/ma14247531openalex-snapshot

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

inferredcites →https://doi.org/10.3390/ma14247669openalex-snapshot

A Predictive Mimicker of Fracture Behavior in Fiber Reinforced Concrete Using Machine Learning

inferredcites →https://doi.org/10.3390/ma15041477openalex-snapshot

Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam

inferredcites →https://doi.org/10.3390/ma15051662openalex-snapshot

Prediction of Compressive Strength of Partially Saturated Concrete Using Machine Learning Methods

inferredcites →https://doi.org/10.3390/ma15134450openalex-snapshot

Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete

inferredcites →https://doi.org/10.3390/ma15134491openalex-snapshot

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

inferredcites →https://doi.org/10.3390/ma15155208openalex-snapshot

Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques

inferredcites →https://doi.org/10.3390/ma15155435openalex-snapshot

Application of Machine Learning Techniques for Predicting Compressive, Splitting Tensile, and Flexural Strengths of Concrete with Metakaolin

inferredcites →https://doi.org/10.3390/math10132245openalex-snapshot

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

inferredcites →https://doi.org/10.3390/modelling2030019openalex-snapshot

Numerical Simulation-Based Damage Identification in Concrete

inferredcites →https://doi.org/10.3390/polym13193389openalex-snapshot

Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

inferredcites →https://doi.org/10.3390/polym14040682openalex-snapshot

Quantitative and Qualitative Aspects of Composite Action of Concrete and Dispersion-Reinforcing Fiber

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4570550openalex-extracted

inferred← citeshttps://doi.org/10.1002/eng2.70781openalex-snapshot

A Multi‐Fiber Data‐Driven Framework for Predicting Impact Resistance of Fiber‐Reinforced Concrete Using Hybrid ANN Optimization

inferred← citeshttps://doi.org/10.1002/gch2.202500235openalex-snapshot

Smart and Sustainable Regeneration of Fouled Desalination Membranes Using Artificial Intelligence

inferred← citeshttps://doi.org/10.1007/s11831-025-10325-5openalex-snapshot

Ensembles of Graph and Physics-Informed Machine Learning for Scientific Modeling in Materials Science: A Review

inferred← citeshttps://doi.org/10.1007/s42107-023-00726-xopenalex-snapshot

Efficient compressive strength prediction of concrete incorporating industrial wastes using deep neural network

inferred← citeshttps://doi.org/10.1016/j.actamat.2025.121292openalex-snapshot

A comprehensive Ai-driven framework for predictive design of grained materials: Case study on nanoglass

inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2023.134646openalex-snapshot

The prediction and evaluation of recycled polypropylene fiber and aggregate incorporated foam concrete using Artificial Neural Networks

inferred← citeshttps://doi.org/10.1016/j.cscm.2023.e02367openalex-snapshot

Lightweight expanded-clay fiber concrete with improved characteristics reinforced with short natural fibers

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110933openalex-snapshot

Artificial intelligence approaches in predicting the mechanical properties of natural fiber-reinforced concrete: A comprehensive review

inferred← citeshttps://doi.org/10.1016/j.microc.2026.117756openalex-snapshot

Experimental study on the mechanical properties of fiber-reinforced manufactured sand concrete

inferred← citeshttps://doi.org/10.1051/e3sconf/202451504015openalex-snapshot

Use of artificial neural networks for solving the problem of residual resource estimation of hoisting cranes

inferred← citeshttps://doi.org/10.1109/fet68771.2026.11601405openalex-snapshot

A Comparative Study of Artificial Neural Network Architectures for Predicting the Compressive Strength of Jute Fiber Reinforced Concrete

inferred← citeshttps://doi.org/10.1155/2024/7844854openalex-snapshot

Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

inferred← citeshttps://doi.org/10.1590/s1678-86212025000100800openalex-snapshot

Strength prediction of mortar reinforced with babassu coconut fiber using artificial neural networks

inferred← citeshttps://doi.org/10.23947/2541-9129-2024-8-2-68-77openalex-snapshot

Comparative Analysis of the Performance of Artificial Neural Networks in Assessing the Technical Condition of Steel Ropes

inferred← citeshttps://doi.org/10.23947/2687-1653-2024-24-1-36-47openalex-snapshot

Prediction of Rheological Parameters of Polymers by Machine Learning Methods

inferred← citeshttps://doi.org/10.32604/cmes.2025.066276openalex-snapshot

A Comprehensive Review on Bridging the Research Gap in AI-Driven Material Simulation for FRP Composites

inferred← citeshttps://doi.org/10.3390/a18120744openalex-snapshot

Compressive Strength of Geopolymer Concrete Prediction Using Machine Learning Methods

inferred← citeshttps://doi.org/10.3390/app122110864openalex-snapshot

Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression

inferred← citeshttps://doi.org/10.3390/app13031904openalex-snapshot

Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network

inferred← citeshttps://doi.org/10.3390/app13095413openalex-snapshot

Discovery and Classification of Defects on Facing Brick Specimens Using a Convolutional Neural Network

inferred← citeshttps://doi.org/10.3390/biomimetics8030309openalex-snapshot

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms

inferred← citeshttps://doi.org/10.3390/buildings14020377openalex-snapshot

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

inferred← citeshttps://doi.org/10.3390/buildings14051198openalex-snapshot

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods

inferred← citeshttps://doi.org/10.3390/buildings15142442openalex-snapshot

Porosity Analysis and Thermal Conductivity Prediction of Non-Autoclaved Aerated Concrete Using Convolutional Neural Network and Numerical Modeling

inferred← citeshttps://doi.org/10.3390/buildings16193926openalex-snapshot

Prediction of Ultra-High-Performance Concrete (UHPC) Compressive Strength in Structural Applications Using Machine Learning: A Comprehensive Evaluation via Multiple Random Seeds and Hyperparameter Optimization

inferred← citeshttps://doi.org/10.3390/jcs7080327openalex-snapshot

Method for Concrete Structure Analysis by Microscopy of Hardened Cement Paste and Crack Segmentation Using a Convolutional Neural Network

inferred← citeshttps://doi.org/10.3390/jcs7090364openalex-snapshot

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

inferred← citeshttps://doi.org/10.3390/jcs8080287openalex-snapshot

Analysis of Models to Predict Mechanical Properties of High-Performance and Ultra-High-Performance Concrete Using Machine Learning

inferred← citeshttps://doi.org/10.3390/ma16237459openalex-snapshot

Evaluation of Durability Performance for Chloride Ingress Considering Long-Term Aged GGBFS and FA Concrete and Analysis of the Relationship between Concrete Mixture Characteristic and Passed Charge Using Machine Learning Algorithm

inferred← citeshttps://doi.org/10.3390/ma19122641openalex-snapshot

Data-Driven Modeling of Friction in Drawbead Test Through Advanced Machine Learning

inferred← citeshttps://doi.org/10.7717/peerj-cs.3725openalex-snapshot

Deep learning-based modeling of sulfur-extended asphalt (SEA) properties: a novel computational framework

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.