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ỨNG DỤNG PHẦN MỀM REVIT TRONG CÔNG NGHỆ BIM ĐỂ HỖ TRỢ VIỆC LẬP DỰ TOÁN CÔNG TRÌNH XÂY DỰNG DD&CN

Optimizing the architecture of the artificial neural network by genetic algorithm to improve the predictability of pile bearing capacity based on CPT results

Journal of Science and Transport Technology - UTT

Application of Artificial Intelligence to Determined Unconfined Compressive Strength of Cement-Stabilized Soil in Vietnam

Applied Sciences

Application of Random Forest model to determine unconfined compressive strength of the soil-cement mixture

Cigos 2021 conference

Improving deep neural network using hyper-parameters tuning in predicting the bearing capacity of shallow foundations

Computer Science and Information Engineering

Application of Ensemble Learning Using Weight Voting Protocol in the Prediction of Pile Bearing Capacity

Mathematical Problems in Engineering

Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil

Mathematical Problems in Engineering

Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity

PLOS ONE

Prediction of pile axial bearing capacity using artificial neural network and random forest

Applied Sciences

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Sustainability

Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials