Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Abambres, Miguel; Rajana, Komal; Tsavdaridis, Konstantinos Daniel; Ribeiro, Tiago

doi:10.3390/computers8010002

Cited by 50 publications

(29 citation statements)

References 43 publications

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“…In addition, Cascardi predicted the strength of a concrete column, and proceeded to the wall shear strength [33,34]. Abambres et al carried out the load prediction of an I-Section steel beam [35]. ANNs have been employed extensively in recent building load prediction studies owing to their advantages and improved algorithm.…”

Section: Modeling Of An Artificial Neural Network (Ann) Based On Usermentioning

confidence: 99%

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

2019

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The evaluation of building energy consumption is heavily based on building characteristics and thus often deviates from the true consumption. Consequently, user-based estimation of building energy consumption is necessary because the actual consumption is greatly affected by user characteristics and activities. This work aims to examine the variation in energy consumption as a function of user activities within the same building, and to employ an artificial neural network (ANN) to predict user-based energy consumption. The study exploited the actual 24-h schedules of 5240 single-person households and computed the respective energy consumption using EnergyPlus V 8.8.0 software. The calculated values were clustered according to gender, age, occupation, income, educational level, and occupancy period and the difference among them was analyzed. The simulation results showed that for single-person households in Korea, females used more energy than males did, and the difference increased with age. Furthermore, unemployed and low-income individuals consumed more energy whereas consumption was inversely proportional to the educational level. Energy consumption increased with the occupancy period. Based on the simulation results and six user characteristics, the ANN model indicated a correlation between user characteristics and energy usage. This study analyzed the differences in energy usage depending on user activity and characteristics that affect building energy consumption.

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Section: Modeling Of An Artificial Neural Network (Ann) Based On Usermentioning

confidence: 99%

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

2019

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“…Consequently, the FNN model exhibits one hidden layer and seven neurons in the hidden layer. The activation function for the hidden layer was chosen as a sigmoid function, whereas the activation function for the output layer was a linear Materials 2020, 13, 1205 9 of 25 function [115]. The cost function was chosen such as the mean square error function [116].…”

Section: Optimization Of Weight Parameters Of Fnn Using the Iwo Technmentioning

confidence: 99%

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

Nguyen

Tran

et al. 2020

Materials

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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 lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P u ) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P u was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of P u , with a value of R 2 of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R 2 , respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P u in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.Materials 2020, 13, 1205 2 of 25 larger energy absorption capacity [7], convenient construction [11], economy of materials [12][13][14], and excellent seismic and refractory performance [15]. In particular, this type of structure can reduce the environmental burden by removing formwork [16], reusing steel pipes, and using high quality concrete with recycled aggregate [17]. The characteristics of CFST are that the steel material is located far from the central axis so the rigidity of the column is very large, and thus it also contributes to increasing the moment of inertia of the structure [5,18]. The ideal form of concrete core works against the compressive load and hinders the local buckling state of the steel pipe. Therefore, the CFST structures are often used in locations subject to large compressive loads [9,15,19]. The CFST columns are mainly divided into square columns, round columns, and rectangular columns, based on different cross-sectional forms [15]. In particular, the square and rectangular CFST columns have the advantage of easy connection and reliable work with other structural members such as beams, walls, and panels [20]. Compared with square CFST columns, rectangular columns have irregular bending stiffness along different axes, so this type of column is suitable for the mechanical behavior of members including arch ribs, pillars, abutments, and piers, and other structural members under load actions vary greatly from vertical to horizontal [6]. Because the scope of application of rectangular CFST columns is quite wide and this column is mainly subjected to compression, the main purpose of the paper is to analyze and evaluate the ultimate bearing capacity of...

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“…The data used in this work was extracted from a validated finite element (FE) model, previously introduced in the literature by Abambres et al [31]. The goodness of using data from a validated FE solver instead of experiments for training ML models has been proved in various investigations such as Mallela and Upadhyay [32] and Sadovský and Soares [33].…”

Section: Description Of Cellular Beams and Selection Of Variables Formentioning

confidence: 99%

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

et al. 2019

Applied Sciences

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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.

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Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Cited by 50 publications

References 43 publications

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

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

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