Hybrid BART-based models optimized by nature-inspired metaheuristics to predict ultimate axial capacity of CCFST columns

Luat, Nguyen-Vu; Shin, Jiuk; Lee, Kihak

doi:10.1007/s00366-020-01115-7

Cited by 22 publications

(7 citation statements)

References 64 publications

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“…The GA mimics the biological process of evolution through selection, crossover, and mutation [34]. This algorithm has received significant attention in recent studies [19], [35]- [38]. The DE algorithm was developed based on the GA and is a well-designed metaheuristic method for the global optimization of noncontinuous or nondifferentiable functions.…”

Section: Introductionmentioning

confidence: 99%

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Nguyen

Heo

et al. 2021

IEEE Access

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The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

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Section: Introductionmentioning

confidence: 99%

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Nguyen

Heo

et al. 2021

IEEE Access

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“…Due to the considerable accuracy improvements caused by the proposed model (from 10.3 to 87.9%), it was introduced as an effective tool for this purpose. Further similar applications of such algorithms can be found for invasive weed optimization (IWO) 59 , genetic algorithm (GA) 60 , and balancing composite motion optimization (BCMO) 61 .…”

Section: Introductionmentioning

confidence: 99%

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Liu,

Liang

2024

Sci Rep

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Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (Pu) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the Pu. In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the Pu pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in Pu prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the Pu of concrete-filled steel tube columns.

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“…However, the development of these models is based on pre-assumed equations, which makes them impracticable and unrealistic in terms of estimation perspective [ 17 , 18 ]. To tackle this problem, recently various artificial intelligence (AI) techniques, specifically machine learning methods, have been extensively used in the field of civil engineering) [ 19 , 20 , 21 ]. Researchers have used Artificial neural network (ANN) [ 22 ], Support vector machine (SVM), [ 23 ] random forest regression (RFR) [ 24 , 25 ], adaptive neuro-fuzzy interface system (ANFIS) [ 26 ], feed-forward neural network (FNN) [ 27 ], particle swarm optimization (PSO) [ 28 ], genetic programming (GP) [ 29 ], gene expression programming (GEP) [ 30 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

Khan

Zafar

et al. 2021

Materials

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The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.

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Hybrid BART-based models optimized by nature-inspired metaheuristics to predict ultimate axial capacity of CCFST columns

Cited by 22 publications

References 64 publications

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression

Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence

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