scholarly journals Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

2019 ◽  
Vol 9 (24) ◽  
pp. 5458 ◽  
Author(s):  
Hai-Bang Ly ◽  
Tien-Thinh Le ◽  
Lu Minh Le ◽  
Van Quan Tran ◽  
Vuong Minh Le ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Optimization Techniques ◽  
Buckling Load ◽  
Steel Beams ◽  
Beam Length ◽  
Inference System ◽  
Critical Buckling Load ◽  
Flange Thickness ◽  
Hybrid Machine

The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.

scholarly journals Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections

2019 ◽  
Vol 9 (11) ◽  
pp. 2258 ◽  
Author(s):  
Hai-Bang Ly ◽  
Lu Minh Le ◽  
Huan Thanh Duong ◽  
Thong Chung Nguyen ◽  
Tuan Anh Pham ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mean Squared Error ◽  
Optimization Technique ◽  
Buckling Load ◽  
Structural Members ◽  
Geometric Imperfections ◽  
Inference System ◽  
Critical Buckling Load ◽  
Initial Geometric Imperfections ◽  
Hybrid Artificial Intelligence

The main aim of this study is to develop different hybrid artificial intelligence (AI) approaches, such as an adaptive neuro-fuzzy inference system (ANFIS) and two ANFISs optimized by metaheuristic techniques, namely simulated annealing (SA) and biogeography-based optimization (BBO) for predicting the critical buckling load of structural members under compression, taking into account the influence of initial geometric imperfections. With this aim, the existing results of compression tests on steel columns were collected and used as a dataset. Eleven input parameters, representing the slenderness ratios and initial geometric imperfections, were considered. The predicted target was the critical buckling load of columns. Statistical criteria, namely the correlation coefficient (R), the root mean squared error (RMSE), and the mean absolute error (MAE) were used to evaluate and validate the three proposed AI models. The results showed that SA and BBO were able to improve the prediction performance of the original ANFIS. Excellent results using the BBO optimization technique were achieved (i.e., an increase in R by 7.15%, RMSE by 40.48%, and MAE by 38.45%), and those using the SA technique were not much different (i.e., an increase in R by 5.03%, RMSE by 26.68%, and MAE by 20.40%). Finally, sensitivity analysis was performed, and the most important imperfections affecting column buckling capacity was found to be the initial in-plane loading eccentricity at the top and bottom ends of the columns. The methodology and the developed AI models herein could pave the way to establishing an advanced approach to forecasting damages of columns under compression.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Prediction of Stock Index of Tata Steel using Hybrid Machine Learning based Optimization Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 3186-3193
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Prediction Models ◽  
Market Price ◽  
Optimization Techniques ◽  
Stock Index ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stock Price Prediction ◽  
Hybrid Machine

The trend of stock price prediction has always been in the focal point of analytical activity in financial domain for both the researchers and investors. Prediction with accuracy is very essential for improved investment decisions that imbibe minimum risk factors. Due to this, majority of investors depend upon that intelligent trading system which generates better forecasting results. As forecasting stock market price with high accuracy is quite a challenging task for the analysts, machine learning has been adopted as one of the popular techniques to predict future trends. Even if there are many recognized analytical time series analysis that are categorized either under soft computing or under conventional statistical techniques like fuzzy logic, artificial neural networks and genetic algorithms, researchers have been looking for more appropriate techniques which can exhibit improved results. In this paper, we developed different hybrid machine learning based prediction models and compared their efficiency. Dimension reduction techniques such as orthogonal forward selection (OFS) and kernel principal component analysis (KPCA) are used separately with support vector regression (SVR) and teaching learning based optimization (TLBO) to predict the stock price of Tata Steel. The performance of both the proposed approach is evaluated with 4143days daily transactional data of Tata steels stocks price, which was collected from Bombay Stock Exchange (BSE). We compared the results of both OFS-SVR-TLBO and KPCA-SVR-TLBO hybrid models and concludes that by incorporating KPCA is more practicable and performs better results than OFS

scholarly journals COVID-19 Pandemic Prediction for Hungary: A Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Gergo Pinter ◽  
Imre Felde ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Practical Hybrid Machine Learning Approach for Estimation of Ultimate Load of Elliptical Concrete-Filled Steel Tubular Columns under Axial Loading

2020 ◽  
Vol 2020 ◽  
pp. 1-19 ◽  
Author(s):  
Tien-Thinh Le
Keyword(s):  
Machine Learning ◽  
Load Capacity ◽  
Axial Loading ◽  
Steel Tube ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Elliptical Cross ◽  
Real Coded Genetic Algorithm ◽  
Hybrid Machine ◽  
Cfst Columns

In this study, a hybrid machine learning (ML) technique was proposed to predict the bearing capacity of elliptical CFST columns under axial load. The proposed model was Adaptive Neurofuzzy Inference System (ANFIS) combined with Real Coded Genetic Algorithm (RCGA), denoted as RCGA-ANFIS. The evaluation of the model was performed using the coefficient of determination (R2) and root mean square error (RMSE). The results showed that the RCGA-ANFIS (R2 = 0.974) was more reliable and effective than conventional gradient descent (GD) technique (R2 = 0.952). The accuracy of the present work was found superior to the results published in the literature (R2 = 0.776 or 0.768) when predicting the load capacity of elliptical CFST columns. Finally, sensitivity analysis showed that the thickness of the steel tube and the minor axis length of the elliptical cross section were the most influential parameters. For practical application, a Graphical User Interface (GUI) was developed in MATLAB for researchers and engineers and to support the teaching and interpretation of the axial behavior of CFST columns.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Gergo Pinter ◽  
Imre Felde ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

AbstractSeveral epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

2020 ◽  
Author(s):  
Gergo Pinter ◽  
Imre Felde ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Future Research ◽  
Complex Nature ◽  
Learning Approach ◽  
Epidemiological Models ◽  
Inference System ◽  
Proper Actions ◽  
Machine Learning Approach ◽  
Hybrid Machine

Abstract Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

scholarly journals Machine Learning for Air Transport Planning and Management

2021 ◽  
Author(s):  
Graham Wild ◽  
Glenn Baxter ◽  
Panarat Srisaeng ◽  
Steven Richardson
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Traditional Approach ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Air Transport ◽  
Civil Aviation ◽  
Support Vector ◽  
Inference System

In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error.

Sign in / Sign up

Export Citation Format

Share Document