Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design

A Sharma, T Mukhopadhyay, SM Rangappa… - … Methods in Engineering, 2022 - Springer
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …

Influence of data splitting on performance of machine learning models in prediction of shear strength of soil

QH Nguyen, HB Ly, LS Ho, N Al-Ansari… - Mathematical …, 2021 - Wiley Online Library
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …

[HTML][HTML] Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

E Champa-Bujaico, P García-Díaz… - International Journal of …, 2022 - mdpi.com
Recently, the field of polymer nanocomposites has been an area of high scientific and
industrial attention due to noteworthy improvements attained in these materials, arising from …

[HTML][HTML] Soft computing ensemble models based on logistic regression for groundwater potential mapping

PT Nguyen, DH Ha, M Avand, A Jaafari, HD Nguyen… - Applied Sciences, 2020 - mdpi.com
Groundwater potential maps are one of the most important tools for the management of
groundwater storage resources. In this study, we proposed four ensemble soft computing …

[HTML][HTML] A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation

DV Dao, H Adeli, HB Ly, LM Le, VM Le, TT Le… - Sustainability, 2020 - mdpi.com
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI)
techniques, namely Gaussian Process Regression (GPR) with five different kernels …

Feature wise normalization: An effective way of normalizing data

D Singh, B Singh - Pattern Recognition, 2022 - Elsevier
This paper presents a novel Feature Wise Normalization approach for the effective
normalization of data. In this approach, each feature is normalized independently with one of …

Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive …

HB Ly, MH Nguyen, BT Pham - Neural Computing and Applications, 2021 - Springer
Foamed concrete (FC) shows advantageous applications in civil engineering, such as
reduction in dead loads, contribution to energy conservation, or decrease the construction …

[HTML][HTML] Groundwater potential mapping combining artificial neural network and real AdaBoost ensemble technique: the DakNong province case-study, Vietnam

PT Nguyen, DH Ha, A Jaafari, HD Nguyen… - International journal of …, 2020 - mdpi.com
The main aim of this study is to assess groundwater potential of the DakNong province,
Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates …

[HTML][HTML] A comparative study of kernel logistic regression, radial basis function classifier, multinomial naïve bayes, and logistic model tree for flash flood susceptibility …

BT Pham, TV Phong, HD Nguyen, C Qi, N Al-Ansari… - Water, 2020 - mdpi.com
Risk of flash floods is currently an important problem in many parts of Vietnam. In this study,
we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial …

[HTML][HTML] Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under …

HQ Nguyen, HB Ly, VQ Tran, TA Nguyen, TT Le… - Materials, 2020 - mdpi.com
Concrete filled steel tubes (CFSTs) show advantageous applications in the field of
construction, especially for a high axial load capacity. The challenge in using such structure …