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 Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1670 ◽  
Author(s):  
Lu Minh Le ◽  
Hai-Bang Ly ◽  
Binh Thai Pham ◽  
Vuong Minh Le ◽  
Tuan Anh Pham ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Buckling Load ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Steel Columns ◽  
Neuro Fuzzy ◽  
Hybrid Artificial Intelligence

This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

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 Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

2019 ◽  
Vol 9 (15) ◽  
pp. 3172 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Thanh-Hai Le ◽  
Cao-Thang Pham ◽  
Tien-Thinh Le ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Support Vector ◽  
Inference System ◽  
Stone Matrix Asphalt ◽  
Hybrid Artificial Intelligence ◽  
Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

Shear in Structural Stability: On the Engesser–Haringx Discord

2010 ◽  
Vol 77 (3) ◽  
Author(s):  
Johan Blaauwendraad
Keyword(s):  
Structural Engineering ◽  
Flexural Rigidity ◽  
Thought Experiment ◽  
Quantitative Information ◽  
Buckling Load ◽  
Structural Members ◽  
Buckling Loads ◽  
Critical Buckling Load ◽  
Helical Springs ◽  
Wide Range

Since Haringx introduced his stability hypothesis for the buckling prediction of helical springs over 60 years ago, discussion is on whether or not the older hypothesis of Engesser should be replaced in structural engineering for stability studies of shear-weak members. The accuracy and applicability of both theories for structures has been subject of study in the past by others, but quantitative information about the accuracy for structural members is not provided. This is the main subject of this paper. The second goal is to explain the experimental evidence that the critical buckling load of a sandwich beam-column surpasses the shear buckling load GAs, which is commonly not expected on basis of the Engesser hypothesis. The key difference between the two theories regards the relationship, which is adopted in the deformed state between the shear force in the beam and the compressive load. It is shown for a wide range of the ratio of shear and flexural rigidity to which extent the two theories agree and/or conflict with each other. The Haringx theory predicts critical buckling loads which are exceeding the value GAs, which is not possible in the Engesser approach. That sandwich columns have critical buckling loads larger than GAs does, however, not imply the preference of the Haringx hypothesis. This is illustrated by the introduction of the thought experiment of a compressed cable along the central axis of a beam-column in deriving governing differential equations and finding a solution for three different cases of increasing complexity: (i) a compressed member of either flexural or shear deformation, (ii) a compressed member of both flexural and shear deformations, and (iii) a compressed sandwich column. It appears that the Engesser hypothesis leads to a critical buckling load larger than GAs for layered cross section shapes and predicts the sandwich behavior very satisfactory, whereas the Haringx hypothesis then seriously overestimates the critical buckling load. The fact that the latter hypothesis is perfectly confirmed for helical springs (and elastomeric bearings) has no meaning for shear-weak members in structural engineering. Then, the Haringx hypothesis should be avoided. It is strongly recommended to investigate the stability of the structural members on the basis of the Engesser hypothesis.

Application of Modified Consistent Mode Imperfection Method in Stability Analysis of Single-Layer Reticulated Shells

2013 ◽  
Vol 477-478 ◽  
pp. 744-748 ◽  
Author(s):  
Sheng He ◽  
Jian Cai ◽  
Qi Qi Liu
Keyword(s):  
Stability Analysis ◽  
Single Layer ◽  
Buckling Load ◽  
Structure Stability ◽  
Geometric Imperfections ◽  
Stability Performance ◽  
Mode Method ◽  
Initial Geometric Imperfections ◽  
The Stability ◽  
Reticulated Shell Structure

Based on the probability reliability theory, this paper proposes a modified consistent mode imperfection method, which fits the integral stability analysis of single-layer reticulated shells with the initial geometric imperfections. Nearly 1000 elasto-plastic load-deflection overall processes of four different rise-to-span ratio for Kiewitt-8 single-layer reticulated shells are analyzed by using the random imperfection mode method, the consistent mode imperfection method and the modified consistent mode imperfection method respectively. The study shows that the random imperfection mode method can assess the influence of initial geometric imperfections on structure stability more scientifically, but the calculation is quite large. By using the consistent mode imperfection method, the buckling load is not sure to be the most unfavorable, and the degree of reliability couldnt be ensured effectively. The modified consistent mode imperfection method can gain the buckling load which meets the requirement of probability reliability with less calculation. It can also assess the stability performance of single-layer reticulated shell structure more reasonably and safely.

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