Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning

Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear

(2020) Oladimeji B. Olalusi et al.   ADVANCES IN ENGINEERING SOFTWARE

Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures

(2020) Oladimeji B. Olalusi et al.   Applied Sciences-Basel

Database of Shear Experiments on Steel Fiber Reinforced Concrete Beams without Stirrups

(2019) Eva Lantsoght   Materials

Gaussian Process-Based Response Surface Method for Slope Reliability Analysis

(2019) Bin Hu et al.   Advances in Civil Engineering

Model uncertainties and bias in SHEAR strength predictions of slender stirrup reinforced concrete beams

(2019) Oladimeji B. Olalusi et al.   Structural Concrete

Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression

(2019) Junfei Zhang et al.   CONSTRUCTION AND BUILDING MATERIALS

How do steel fibers improve the shear capacity of reinforced concrete beams without stirrups?

(2019) Eva O.L. Lantsoght   COMPOSITES PART B-ENGINEERING

A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm

(2019) Qinghua Han et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models

(2019) Rachel Cook et al.   JOURNAL OF MATERIALS IN CIVIL ENGINEERING

Assessment of reliability of EN 1992‐1‐1 variable strut inclination method of shear design provisions for stirrup failure

(2019) Oladimeji B. Olalusi et al.   Structural Concrete

New empirical approach for determining nominal shear capacity of steel fiber reinforced concrete beams

(2019) Masoud Ahmadi et al.   CONSTRUCTION AND BUILDING MATERIALS

Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

(2019) Yuantian Sun et al.   Advances in Civil Engineering

Reduced order modeling for nonlinear structural analysis using Gaussian process regression

(2018) Mengwu Guo et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Dual potential capacity model for reinforced concrete short and deep beams subjected to shear

(2018) Deuck Hang Lee et al.   Structural Concrete

Present State of Eurocode 2 Variable Strut Inclination Method for Shear Design and Possible Improvement

(2018) Oladimeji Benedict Olalusi   Structures

Optimal classification trees

(2017) Dimitris Bertsimas et al.   MACHINE LEARNING

A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis

(2017) Guoshao Su et al.   STRUCTURAL SAFETY

Shear Strength Prediction in Reinforced Concrete Deep Beams Using Nature-Inspired Metaheuristic Support Vector Regression

(2016) Jui-Sheng Chou et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

Modeling shear strength of medium- to ultra-high-strength steel fiber-reinforced concrete beams using artificial neural network

(2016) Khandaker M. A. Hossain et al.   NEURAL COMPUTING & APPLICATIONS

Assessment of model uncertainties for structural resistance

(2016) Milan Holický et al.   PROBABILISTIC ENGINEERING MECHANICS

Influence of Fiber Properties on Shear Failure of Steel Fiber Reinforced Beams Without Web Reinforcement: ANN Modeling

(2016) Yassir M. Abbas et al.   Latin American Journal of Solids and Structures

Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model

(2016) Nhat-Duc Hoang et al.   Advances in Civil Engineering

Development of prediction models for shear strength of SFRCB using a machine learning approach

(2015) Masoud Sarveghadi et al.   NEURAL COMPUTING & APPLICATIONS

A Gaussian process-based response surface method for structural reliability analysis

(2015) Guoshao Su et al.   STRUCTURAL ENGINEERING AND MECHANICS

Shear strength of Steel Fiber Reinforced Concrete (SFRC) slender beams

(2014) Guray Arslan   KSCE Journal of Civil Engineering

Generalization of shear truss model to the case of SFRC beams with stirrups

(2013) Piero Colajanni et al.   Computers and Concrete

Nonlinear modeling of shear strength of SFRC beams using linear genetic programming

(2013) A.H. Gandomi et al.   STRUCTURAL ENGINEERING AND MECHANICS

Principal Component and Multiple Regression Analysis for Steel Fiber Reinforced Concrete (SFRC) Beams

(2013) Mohammad S. Islam et al.   International Journal of Concrete Structures and Materials

Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming

(2012) Ilker Fatih Kara   NEURAL COMPUTING & APPLICATIONS

Predicting the shear strength of steel fiber reinforced concrete beams

(2011) Emma Slater et al.   CONSTRUCTION AND BUILDING MATERIALS

Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash

(2010) Mustafa Sarıdemir   CONSTRUCTION AND BUILDING MATERIALS

Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming

(2010) Harun Tanyildizi et al.   CONSTRUCTION AND BUILDING MATERIALS

Simple Plastic Model for Shear Critical SFRC Beams

(2009) Nino Spinella et al.   JOURNAL OF STRUCTURAL ENGINEERING

Locating acoustic emission sources in complex structures using Gaussian processes

(2009) J. Hensman et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Experimental and finite element analysis on the steel fiber-reinforced concrete (SFRC) beams ultimate behavior

(2008) D. Mehmet Özcan et al.   CONSTRUCTION AND BUILDING MATERIALS

Modelling damping ratio and shear modulus of sand–mica mixtures using neural networks

(2008) Ali Firat Cabalar et al.   ENGINEERING GEOLOGY

Support vector machine applied to settlement of shallow foundations on cohesionless soils

(2007) Pijush Samui   COMPUTERS AND GEOTECHNICS

Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming

(2007) Erdogan Ozbay et al.   CONSTRUCTION AND BUILDING MATERIALS