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

https://doi.org/10.3390/ma15134491
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1 of the 42 references listed on this page is in the Retraction Watch database as retracted (export 2026-10-28): 10.1007/s10845-016-1217-y (notice 2020-01-14).

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What this work says

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

inferredcites →https://doi.org/10.1002/suco.202100082openalex-snapshot
Residual strength of concrete subjected to fatigue based on machine learning technique
inferredcites →https://doi.org/10.1007/978-3-030-80312-4_22openalex-snapshot
Fatigue Life Prediction of Reinforced Concrete Using Artificial Neural Network
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Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network
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RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam
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Performance Analysis of Learning Rate Parameter on Prediction of Signal Power Loss for Network Optimization and Better Generalization
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inferredcites →https://doi.org/10.1016/j.camwa.2010.03.065openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compstruct.2020.112885openalex-snapshot
Predictive ANN models for varying filler content for cotton fiber/PVC composites based on experimental load displacement curves
inferredcites →https://doi.org/10.1016/j.conbuildmat.2018.03.276openalex-snapshot
Flexural fatigue behavior of ultra-lightweight cement composite and high strength lightweight 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.dyepig.2007.05.014openalex-snapshot
Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks
inferredcites →https://doi.org/10.1016/j.engstruct.2018.05.122openalex-snapshot
Remaining fatigue life assessment of in-service road bridge decks based upon artificial neural networks
inferredcites →https://doi.org/10.1016/j.heliyon.2018.e00938openalex-snapshot
State-of-the-art in artificial neural network applications: A survey
inferredcites →https://doi.org/10.1016/j.ijfatigue.2006.06.013openalex-snapshot
Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network
inferredcites →https://doi.org/10.1016/j.ijfatigue.2021.106356openalex-snapshot
A novel method of multiaxial fatigue life prediction based on deep learning
inferredcites →https://doi.org/10.1016/j.renene.2020.06.043openalex-snapshot
Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions
inferredcites →https://doi.org/10.1016/j.triboint.2019.105913openalex-snapshot
A machine learning approach to the prediction of fretting fatigue life
inferredcites →https://doi.org/10.1016/s0951-8339(96)00016-0openalex-snapshot
In-time fatigue monitoring using neural networks
inferredcites →https://doi.org/10.1061/(asce)0899-1561(2009)21:6(253)openalex-snapshot
Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks
inferredcites →https://doi.org/10.1080/24705314.2021.1892572openalex-snapshot
Predicting the compressive strength of a quaternary blend concrete using Bayesian regularized neural network
inferredcites →https://doi.org/10.1088/1742-6596/1187/5/052036openalex-snapshot
A Comparative Study of Customer Complaint Prediction Model of Time Series, Multiple Linear Regression and BP Neural Network
inferredcites →https://doi.org/10.1111/ffe.13343openalex-snapshot
Soft computing methods for fatigue life estimation: A review of the current state and future trends
inferredcites →https://doi.org/10.1111/ffe.13640openalex-snapshot
Fatigue modeling using neural networks: A comprehensive review
inferredcites →https://doi.org/10.1155/2019/5039097openalex-snapshot
Prediction of Ammunition Storage Reliability Based on Improved Ant Colony Algorithm and BP Neural Network
inferredcites →https://doi.org/10.1201/9781420050646.ptb6openalex-snapshot
Neural Networks
inferredcites →https://doi.org/10.12841/wood.1644-3985.144.13openalex-snapshot
PREDICTING THE PROPERTIES OF CORRUGATED BASE PAPERS USING MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS
inferredcites →https://doi.org/10.14256/jce.2438.2018openalex-snapshot
Prediction of concrete compressive strength through artificial neural networks
inferredcites →https://doi.org/10.14359/1870openalex-snapshot
Fatigue Life Distributions of Concrete for Various Stress Levels
inferredcites →https://doi.org/10.14359/6962openalex-snapshot
Fatigue Strength of Plain, Ordinary, and Lightweight Concrete
inferredcites →https://doi.org/10.1520/jte101907openalex-snapshot
Application of Artificial Neural Network for Fatigue Life Prediction under Interspersed Mode-I Spike Overload
inferredcites →https://doi.org/10.1680/macr.1996.48.177.361openalex-snapshot
Effects of loading frequency and stress reversal on fatigue life of plain concrete
inferredcites →https://doi.org/10.25073/jaec.202151.308openalex-snapshot
Assessment of Longstanding Effects of Fly Ash and Silica Fume on the Compressive Strength of Concrete Using Extreme Learning Machine and Artificial Neural Network
inferredcites →https://doi.org/10.25165/j.ijabe.20191202.4709openalex-snapshot
Prediction of protein content in rice using a near-infrared imaging system as diagnostic technique
inferredcites →https://doi.org/10.3151/jact.12.214openalex-snapshot
Probability Fatigue Models of Concrete Subjected to Splitting-Tensile Loads
inferredcites →https://doi.org/10.3151/jact.17.715openalex-snapshot
Fatigue Lifetime Prediction of Newly Constructed RC Road Bridge Decks
inferredcites →https://doi.org/10.32604/cmes.2022.020160openalex-snapshot
Analytical Models of Concrete Fatigue: A State-of-the-Art Review
inferredcites →https://doi.org/10.3389/fmats.2020.582635openalex-snapshot
Prediction of Rubber Fiber Concrete Strength Using Extreme Learning Machine
inferredcites →https://doi.org/10.3390/app9245534openalex-snapshot
Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete
inferredcites →https://doi.org/10.3390/foods10123016openalex-snapshot
Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters
inferredcites →https://doi.org/10.3390/ma12223787openalex-snapshot
ANN-Based Fatigue Strength of Concrete under Compression
inferredcites →https://doi.org/10.4028/www.scientific.net/amm.584-586.1054openalex-snapshot
Fatigue Performance of Concrete with Pre-Cracks in Tension-Compression Cycles
inferredcites →https://doi.org/10.5555/129449.129452openalex-snapshot
Interpreting neural-network connection weights

What points at this work

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

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Machine learning-augmented finite element modeling for fracture mechanics: A systematic review and future research directions
inferred← citeshttps://doi.org/10.1007/978-3-031-82907-9_34openalex-snapshot
Comprehensive Review of Fatigue Life Prediction of Plain Concrete Using Machine Learning and Finite Element Methods
inferred← citeshttps://doi.org/10.1007/s13369-024-08776-2openalex-snapshot
Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation
inferred← citeshttps://doi.org/10.1007/s40997-026-00963-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41062-026-02629-7openalex-snapshot
Concrete temperature prediction model for arch dam construction based on a parrot optimizer-back propagation neural network
inferred← citeshttps://doi.org/10.1016/j.conbuildmat.2024.138962openalex-snapshot
A comprehensive review of fatigue of cementitious materials: Characterisation, modelling and mechanisms
inferred← citeshttps://doi.org/10.1016/j.cscm.2024.e03613openalex-snapshot
Evaluation and prediction of fatigue life for ceramsite lightweight concrete considering the effects of ceramsite aggregate size and content
inferred← citeshttps://doi.org/10.1016/j.cscm.2026.e06051openalex-snapshot
Model and test verification for concrete fatigue failure of the steel-concrete hybrid wind turbine tower
inferred← citeshttps://doi.org/10.1016/j.ijfatigue.2024.108599openalex-snapshot
A stacking ensemble model for predicting the flexural fatigue life of fiber-reinforced concrete
inferred← citeshttps://doi.org/10.1016/j.jfranklin.2024.01.011openalex-snapshot
Iterative learning control with parameter estimation for non-repetitive time-varying systems
inferred← citeshttps://doi.org/10.1016/j.jobe.2025.114683openalex-snapshot
Probabilistic fatigue life and damage mechanisms of recycled coarse aggregate concrete under flexural loading
inferred← citeshttps://doi.org/10.1016/j.ress.2025.112049openalex-snapshot
A novel multiaxial fatigue life prediction method based on multi-scale physical neural network
inferred← citeshttps://doi.org/10.1016/j.tafmec.2023.103867openalex-snapshot
Fatigue behaviour of plain and reinforced concrete: A systematic review
inferred← citeshttps://doi.org/10.1061/jpeodx.pveng-1997openalex-snapshot
Application of Machine-Learning Models on Analysis and Prediction of the Fatigue Life of Pavement Quality Concrete Made with Recycled Concrete Aggregate
inferred← citeshttps://doi.org/10.1088/1742-6596/3154/1/012021openalex-snapshot
Evaluating Regression Algorithms for Accurate Fatigue Strength Prediction in Structural Steels
inferred← citeshttps://doi.org/10.1111/ffe.14557openalex-snapshot
A Physics‐Guided Neural Network for Probabilistic Fatigue Life Prediction Under Multiple Overload Effects
inferred← citeshttps://doi.org/10.1111/ffe.70012openalex-snapshot
Physics‐Informed Neural Network Model for Predicting the Fatigue Life of Natural Rubber Under Ambient Temperature Effects
inferred← citeshttps://doi.org/10.1155/2023/5525182openalex-snapshot
Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based‐BP Neural Network
inferred← citeshttps://doi.org/10.1590/1517-7076-rmat-2025-0072openalex-snapshot
Research on fatigue performance and life prediction of steel-concrete composite structural materials
inferred← citeshttps://doi.org/10.3390/app14198721openalex-snapshot
Equivalent Fatigue Constitutive Model Based on Fatigue Damage Evolution of Concrete
inferred← citeshttps://doi.org/10.3390/chemosensors11070377openalex-snapshot
Determination of Elemental Composition and Content in Stream Sediments by Laser-Induced Breakdown Spectroscopy
inferred← citeshttps://doi.org/10.3390/gels12030215openalex-snapshot
Prediction of Impact Resistance of Nano-SiO2 and Hybrid Fiber Modified Geopolymer Gel Concrete in Marine Wet–Thermal and Chloride Salt Environment
inferred← citeshttps://doi.org/10.3390/ma15196740openalex-snapshot
Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods
inferred← citeshttps://doi.org/10.3390/ma16020831openalex-snapshot
A Review on Nonlocal Theories in Fatigue Assessment of Solids
inferred← citeshttps://doi.org/10.3390/ma16196447openalex-snapshot
Tensile Fatigue Properties of Ordinary Plain Concrete and Reinforced Concrete under Flexural Loading
inferred← citeshttps://doi.org/10.3390/ma17010029openalex-snapshot
A Metamodel-Based Multi-Scale Reliability Analysis of FRP Truss Structures under Hybrid Uncertainties
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