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

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...

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Bibliographic Details
Published in:Computer modeling in engineering & sciences 2022, Vol.130 (1), p.149-166
Main Authors: Duc Nguyen, Manh, Nguyen Hai, Ha, Al-Ansari, Nadhir, Amiri, Mahdis, Ly, Hai-Bang, Prakash, Indra, Thai Pham, Binh
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bridges
Civil engineering
Clay soils
Correlation coefficients
Datasets
Density
Error reduction
Estimation
Evolutionary algorithms
Evolutionary computation
Inference
Liquid limits
Machine learning
Mathematical models
Mean square errors
Moisture content
Natural resources
Neural networks
Optimization techniques
Parameters
Plastic limit
Root-mean-square errors
Shear strength
Soil compressibility
Soils
Specific gravity
Statistical methods
Support vector machines
Void ratio
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Summary: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.
ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2022.017355