Development of advanced artificial intelligence models for daily rainfall prediction
In this study, the main objective is to develop and compare several advanced Artificial
Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized …
Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized …
Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI)
techniques, namely Gaussian Process Regression (GPR) with five different kernels …
techniques, namely Gaussian Process Regression (GPR) with five different kernels …
Metaheuristic optimization of Levenberg–Marquardt-based artificial neural network using particle swarm optimization for prediction of foamed concrete compressive …
Foamed concrete (FC) shows advantageous applications in civil engineering, such as
reduction in dead loads, contribution to energy conservation, or decrease the construction …
reduction in dead loads, contribution to energy conservation, or decrease the construction …
A comparative study of kernel logistic regression, radial basis function classifier, multinomial naïve bayes, and logistic model tree for flash flood susceptibility map**
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 …
we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial …
RETRACTED ARTICLE: Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of …
Concrete is a very flexible composite material that is extensively employed in the building
industry. Steel slag is a waste material produced during steelmaking. It is formed during the …
industry. Steel slag is a waste material produced during steelmaking. It is formed during the …
[HTML][HTML] Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study
This research study utilizes four machine learning techniques, ie, Multi Expression
programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference …
programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference …
Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression
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 …
construction, especially for a high axial load capacity. The challenge in using such structure …
Prediction of pile axial bearing capacity using artificial neural network and random forest
Axial bearing capacity of piles is the most important parameter in pile foundation design. In
this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to …
this paper, artificial neural network (ANN) and random forest (RF) algorithms were utilized to …
Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity
Determination of pile bearing capacity is essential in pile foundation design. This study
focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network …
focused on the use of evolutionary algorithms to optimize Deep Learning Neural Network …