Corrosion effect on bond behavior between rebar and concrete using Bayesian regularized feed-forward neural network

[1]  Tam T. Truong,et al.  Evaluation of residual flexural strength of corroded reinforced concrete beams using convolutional long short-term memory neural networks , 2022, Structures.

[2]  P. G. Asteris,et al.  Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete , 2022, Construction and Building Materials.

[3]  A. Bahrami,et al.  Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms , 2022, Materials.

[4]  T. Nguyen-Thoi,et al.  Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network , 2022, Soils and Foundations.

[5]  Huu‐Tai Thai,et al.  Machine learning for structural engineering: A state-of-the-art review , 2022, Structures.

[6]  D. Feng,et al.  A probabilistic bond strength model for corroded reinforced concrete based on weighted averaging of non-fine-tuned machine learning models , 2022, Construction and Building Materials.

[7]  Sajad Saraygord Afshari,et al.  Machine learning-based methods in structural reliability analysis: A review , 2021, Reliab. Eng. Syst. Saf..

[8]  De-Cheng Feng,et al.  A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams , 2021 .

[9]  Duc-Kien Thai,et al.  Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete , 2020, Neural Computing and Applications.

[10]  Duygu Bayram Kara,et al.  A Bayesian regularized feed-forward neural network model for conductivity prediction of PS/MWCNT nanocomposite film coatings , 2020, Appl. Soft Comput..

[11]  Moncef L. Nehdi,et al.  Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review , 2020, Archives of Computational Methods in Engineering.

[12]  Saipraneeth Gouravaraju,et al.  A Bayesian regularization-backpropagation neural network model for peeling computations , 2020, The Journal of Adhesion.

[13]  R. Xu,et al.  Stirrup effects on the bond properties of corroded reinforced concrete , 2020 .

[14]  Ali Akbarnezhad,et al.  Rebar corrosion detection, protection, and rehabilitation of reinforced concrete structures in coastal environments: A review , 2019, Construction and Building Materials.

[15]  Hongwei Lin,et al.  State-of-the-art review on the bond properties of corroded reinforcing steel bar , 2019, Construction and Building Materials.

[16]  M. Wevers,et al.  Assessing the bond behaviour of corroded smooth and ribbed rebars with acoustic emission monitoring , 2019, Cement and Concrete Research.

[17]  Nhat-Duc Hoang,et al.  Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model , 2019, Neural Computing and Applications.

[18]  Natarajan Sriraam,et al.  Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms , 2018, Expert Syst. Appl..

[19]  J. Dai,et al.  Prediction of the bond strength between non-uniformly corroded steel reinforcement and deteriorated concrete , 2018, Construction and Building Materials.

[20]  Tingwen Huang,et al.  A L-BFGS Based Learning Algorithm for Complex-Valued Feedforward Neural Networks , 2018, Neural Processing Letters.

[21]  Dario Coronelli,et al.  Engineering bond model for corroded reinforcement , 2018 .

[22]  H. Reinhardt,et al.  Bond strength evaluation of corroded steel bars via the surface crack width induced by reinforcement corrosion , 2017 .

[23]  Yafei Ma,et al.  Experimental investigation of corrosion effect on bond behavior between reinforcing bar and concrete , 2017 .

[24]  Panagiotis G. Asteris,et al.  Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials , 2017, Sensors.

[25]  Esko Sistonen,et al.  Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions , 2017 .

[26]  Václav Snásel,et al.  Metaheuristic design of feedforward neural networks: A review of two decades of research , 2017, Eng. Appl. Artif. Intell..

[27]  Damodar Reddy Edla,et al.  New Algebraic Activation Function for Multi-Layered Feed Forward Neural Networks , 2017 .

[28]  Esra Mete Güneyisi,et al.  Evaluation and modeling of ultimate bond strength of corroded reinforcement in reinforced concrete elements , 2016 .

[29]  Murat Kayri,et al.  Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data , 2016 .

[30]  Lei Zhu,et al.  Unsupervised neighborhood component analysis for clustering , 2015, Neurocomputing.

[31]  Francesco Tondolo,et al.  Bond behaviour with reinforcement corrosion , 2015 .

[32]  Michael Haist,et al.  Assessment of the sustainability potential of concrete and concrete structures considering their environmental impact, performance and lifetime , 2014 .

[33]  Chung-Ho Huang Effects of Rust and Scale of Reinforcing Bars on the Bond Performance of Reinforcement Concrete , 2014 .

[34]  J. Ožbolt,et al.  Modeling pull-out resistance of corroded reinforcement in concrete:Coupled three-dimensional finite element model , 2014 .

[35]  B. Pradhan,et al.  Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks , 2012 .

[36]  Özgür Eren,et al.  An experimental study on the bond strength between reinforcement bars and concrete as a function of concrete cover, strength and corrosion level , 2012 .

[37]  Ashraf F. Ashour,et al.  Corrosion of steel reinforcement in concrete of different compressive strengths , 2011 .

[38]  Ashish Chaturvedi,et al.  Gradient Descent Feed Forward Neural Networks for Forecasting the Trajectories , 2011 .

[39]  Vagelis G. Papadakis,et al.  Consequences of steel corrosion on the ductility properties of reinforcement bar , 2008 .

[40]  Lan Chung,et al.  Bond strength prediction for reinforced concrete members with highly corroded reinforcing bars , 2008 .

[41]  Luisa Berto,et al.  Numerical modelling of bond behaviour in RC structures affected by reinforcement corrosion , 2008 .

[42]  Sankaran Mahadevan,et al.  Chloride-induced reinforcement corrosion and concrete cracking simulation , 2008 .

[43]  A. K. Ghosh,et al.  Suggested Empirical Models for Corrosion-Induced Bond Degradation in Reinforced Concrete , 2008 .

[44]  Kapilesh Bhargava,et al.  Corrosion-induced bond strength degradation in reinforced concrete—Analytical and empirical models , 2007 .

[45]  G. Batis,et al.  Corrosion of steel reinforcement due to atmospheric pollution , 2005 .

[46]  Geoffrey E. Hinton,et al.  Neighbourhood Components Analysis , 2004, NIPS.

[47]  K. Lundgren,et al.  Corrosion influence on bond in reinforced concrete , 2004 .

[48]  K. Lundgren,et al.  Modelling the effect of corrosion on bond in reinforced concrete , 2002 .

[49]  G. Arliguie,et al.  Mechanical behaviour of corroded reinforced concrete beams—Part 1: Experimental study of corroded beams , 2000 .

[50]  Arnaud Castel,et al.  Mechanical behaviour of corroded reinforced concrete beams—Part 2: Bond and notch effects , 2000 .

[51]  P. Balaguru,et al.  BOND BEHAVIOR OF CORRODED REINFORCEMENT BARS , 2000 .

[52]  Dan M. Frangopol,et al.  Reliability of Reinforced Concrete Girders Under Corrosion Attack , 1997 .

[53]  M Maage,et al.  SERVICE LIFE PREDICTION OF EXISTING CONCRETE STRUCTURES EXPOSED TO MARINE ENVIRONMENT , 1996 .

[54]  Rasheeduzzafar,et al.  EFFECT OF REINFORCEMENT CORROSION ON BOND STRENGTH , 1996 .

[55]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[56]  M. Georgiopoulos,et al.  Feed-forward neural networks , 1994, IEEE Potentials.

[57]  M. F. Møller,et al.  Efficient Training of Feed-Forward Neural Networks , 1993 .

[58]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[59]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[60]  C. Fu,et al.  Bond degradation of non-uniformly corroded steel rebars in concrete , 2021 .

[61]  K. Lundgren Modelling bond between corroded reinforcement and concrete , 2014 .

[62]  Dave Winkler,et al.  Bayesian Regularization of Neural Networks , 2009, Artificial Neural Networks.

[63]  Zhaolong Yu,et al.  Test study on bond behavior of corroded steel bars and concrete , 2002 .

[64]  J. Rodriguez,et al.  Corrosion of Reinforcement and Service Life of Concrete Structures , 1996 .

[65]  Patrick van der Smagt Minimisation methods for training feedforward neural networks , 1994, Neural Networks.

[66]  Farid U. Dowla,et al.  Backpropagation Learning for Multilayer Feed-Forward Neural Networks Using the Conjugate Gradient Method , 1991, Int. J. Neural Syst..

[67]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..