A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

Samui, Pijush; Hoang, Nhat‐Duc; Nhu, Viet-Ha; Nguyen, My-Linh Thi; Ngo, Phuong Thao Thi; Bui, Dieu Tien

doi:10.3390/app9224912

Cited by 15 publications

(6 citation statements)

References 57 publications

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“…Bui et al [22] indicated that the hybrid model of Particle Swarm Optimization based Multi-Layer Perceptron (PSO-MLP) has the most accurate prediction of Cc in comparison with the single models of SVM, random forest, and Gaussian process, backpropagation neural network, and radial basis function. Another comparative study between hybrid models and single models also found that a hybrid model of ABC-LM-ANN (Artificial Bee Colony-Levenberg-Marquardt-Artificial Neural Network) could give a better performance compared to other benchmark approaches in predicting Cc for a housing construction project [23]. Moayedi et al [24] also confirmed that a hybrid model of League Championship optimization Algorithm (LCA) and ANFIS outperformed the single model of ANFIS.…”

Section: Introductionmentioning

confidence: 87%

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Nguyen¹,

Hai²,

Al‐Ansari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using statistical indices: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). In addition, two popular ML models namely Reduced Error Pruning Trees (REPTree) and Decision Stump (Dstump) were used for comparison. Results showed that the performance of the novel model ANFIS-DE is the best (R = 0.825, MAE = 0.064 and RMSE = 0.094) in comparison to other models such as REPTree (R = 0.7802, MAE = 0.068 and RMSE = 0.0988) and Dstump (R = 0.7325, MAE = 0.0785 and RMSE = 0.1036). Therefore, the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc, which can be employed in the design and construction of civil engineering structures.

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Section: Introductionmentioning

confidence: 87%

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Nguyen¹,

Hai²,

Al‐Ansari³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…Details for mastering the art of GT are presented in the aforementioned articles for the interested readers. By using necessary conditions detailed in the above-mentioned papers, the variance of the noise is specified by the bias of the regression between γ(k) and δ(k), where 1 ≤ k ≤ p. This variance is dubbed Γ. γ(k) and δ(k) are presented in Equations ( 9) and (10):…”

Section: Overview Of Gamma Test (Gt)mentioning

confidence: 99%

Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Benbouras

Petrişor

2021

Applied Sciences

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Several attempts have been made for estimating the vital swelling index parameter conducted by the expensive and time-consuming Oedometer test. However, they have only focused on the neuron network neglecting other advanced methods that could have increased the predictive capability of models. In order to overcome this limitation, the current study aims to elaborate an alternative model for estimating the swelling index from geotechnical physical parameters. The reliability of the approach is tested through several advanced machine learning methods like Extreme Learning Machine, Deep Neural Network, Support Vector Regression, Random Forest, LASSO regression, Partial Least Square Regression, Ridge Regression, Kernel Ridge, Stepwise Regression, Least Square Regression, and genetic Programing. These methods have been applied for modeling samples consisting of 875 Oedometer tests. Firstly, principal component analysis, Gamma test, and forward selection are utilized to reduce the input variable numbers. Afterward, the advanced techniques have been applied for modeling the proposed optimal inputs, and their accuracy models were evaluated through six statistical indicators and using K-fold cross validation approach. The comparative study shows the efficiency of FS-RF model. This elaborated model provided the most appropriate prediction, closest to the experimental values compared with other models and formulae proposed by the previous studies.

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“…In a related context, recently intense application of soft computing techniques (simple and hybrid) has been found in civil engineering for modeling various aspects such as prediction of safety factor values of retaining walls [29], prediction of the critical buckling load of structural members under compression [30], estimation of surface treatment effects on the tribological performance of steel tools [31], modeling the Marshall stability of stone matrix asphalt materials [32], estimation of soil compression coefficient [33], rock tensile strength prediction [34], and forecasting pile settlement [35].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

et al. 2020

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A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

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A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project

Cited by 15 publications

References 57 publications

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils

Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

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