Strength prediction of concrete-filled steel tubular columns using Categorical Gradient Boosting algorithm

A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns

(2020) Sy Hung Mai et al.   ENGINEERING WITH COMPUTERS

Practical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concrete

(2020) Viet-Linh Tran et al.   THIN-WALLED STRUCTURES

Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns

(2020) Viet-Linh Tran et al.   THIN-WALLED STRUCTURES

Machine learning model for predicting structural response of RC slabs exposed to blast loading

(2020) M.K. Almustafa et al.   ENGINEERING STRUCTURES

Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams

(2020) Roya Solhmirzaei et al.   ENGINEERING STRUCTURES

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

(2020) Oladimeji Benedict Olalusi et al.   ENGINEERING STRUCTURES

Concrete-filled steel tubular columns: Test database, design and calibration

(2019) Son Thai et al.   JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

(2019) Qiubing Ren et al.   Applied Sciences-Basel

Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series

(2019) Matheus Henrique Dal Molin Ribeiro et al.   APPLIED SOFT COMPUTING

Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach

(2019) De-Cheng Feng et al.   CONSTRUCTION AND BUILDING MATERIALS

Behaviour and design of short high-strength steel welded box and concrete-filled tube (CFT) sections

(2017) M. Khan et al.   ENGINEERING STRUCTURES

Axial performance of short concrete filled steel tubes with high- and ultra-high- strength materials

(2017) Ming-Xiang Xiong et al.   ENGINEERING STRUCTURES

Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks

(2017) Yansheng Du et al.   Frontiers of Computer Science

ANN Model for Predicting the Compressive Strength of Circular Steel-Confined Concrete

(2016) M. Ahmadi et al.   International Journal of Civil Engineering

Many regression algorithms, one unified model: A review

(2015) Freek Stulp et al.   NEURAL NETWORKS

Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations

(2011) A.-L. Boulesteix et al.   BRIEFINGS IN BIOINFORMATICS