Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials

Investigation of the mechanical behaviour of metakaolin-based sandcrete mixtures

(2017) Panagiotis G. Asteris et al.   European Journal of Environmental and Civil Engineering

Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network

(2017) Tae-Keun Oh et al.   Journal of Advanced Concrete Technology

Self-compacting concrete strength prediction using surrogate models

(2017) Panagiotis G. Asteris et al.   NEURAL COMPUTING & APPLICATIONS

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

(2017) Faezehossadat Khademi et al.   Frontiers of Structural and Civil Engineering

Comparison of modelling using regression techniques and an artificial neural network for obtaining the static modulus of elasticity of Pinus radiata D. Don. timber by ultrasound

(2016) Alberto García-Iruela et al.   COMPOSITES PART B-ENGINEERING

Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

(2016) Hadi Mashhadban et al.   CONSTRUCTION AND BUILDING MATERIALS

Properties of sandcrete mixtures modified with metakaolin

(2016) Konstantinos G. Kolovos et al.   European Journal of Environmental and Civil Engineering

Prediction of self-compacting concrete strength using artificial neural networks

(2016) P.G. Asteris et al.   European Journal of Environmental and Civil Engineering

Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models

(2016) İbrahim Türkmen et al.   FIRE AND MATERIALS

Strength characteristics of cemented sand–bentonite mixtures with fiber and metakaolin additions

(2016) Bumjoo Kim et al.   MARINE GEORESOURCES & GEOTECHNOLOGY

Prediction of properties of self-compacting concrete containing fly ash using artificial neural network

(2016) Omar Belalia Douma et al.   NEURAL COMPUTING & APPLICATIONS

Anisotropic masonry failure criterion using artificial neural networks

(2016) Panagiotis G. Asteris et al.   NEURAL COMPUTING & APPLICATIONS

Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

(2016) Iman Mansouri et al.   NEURAL COMPUTING & APPLICATIONS

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

(2016) Md. Safiuddin et al.   Materials

Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches

(2015) Iman Mansouri et al.   COMPOSITES PART B-ENGINEERING

Spectral representation-based neural network assisted stochastic structural mechanics

(2015) Dimitris G. Giovanis et al.   ENGINEERING STRUCTURES

Using an Artificial Neural Network to Predict Mix Compositions of Steel Fiber-Reinforced Concrete

(2014) Merve Açikgenç et al.   ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING

Predicting strength development of RMSM using ultrasonic pulse velocity and artificial neural network

(2014) Nain Y. Sheen et al.   Computers and Concrete

Modeling of masonry failure surface under biaxial compressive stress using Neural Networks

(2014) Vagelis Plevris et al.   CONSTRUCTION AND BUILDING MATERIALS

Mechanical properties of soilcrete mixtures modified with metakaolin

(2013) K.G. Kolovos et al.   CONSTRUCTION AND BUILDING MATERIALS

Determination of compressive strength of concrete using Self Organization Feature Map (SOFM)

(2013) Mehdi Nikoo et al.   ENGINEERING WITH COMPUTERS

EVOLVING RBF NEURAL NETWORKS FOR ADAPTIVE SOFT-SENSOR DESIGN

(2013) ALEX ALEXANDRIDIS   International Journal of Neural Systems

Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete

(2009) Fatih Özcan et al.   ADVANCES IN ENGINEERING SOFTWARE

Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

(2008) Gregor Trtnik et al.   ULTRASONICS

Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic

(2007) İlker Bekir Topçu et al.   COMPUTATIONAL MATERIALS SCIENCE