Hybridization of Differential Evolution and Adaptive-NetworkBased Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil
  • Manh Duc Nguyen1, Ha Nguyen Hai1, Nadhir Al-Ansari2,*, Mahdis Amiri3, Hai-Bang Ly4, Indra Prakash5, Binh Thai Pham4,*
1 University of Transport and Communications, Hanoi, 100000, Vietnam
2 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, 971 87, Sweden
3 Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, 4918943464, Iran
4 University of Transport Technology, Hanoi, 100000, Vietnam
5 DDG (R) Geological Survey of India, Gandhinagar, 382010, India
* Corresponding Authors: Nadhir Al-Ansari. Email: nadhir.alansari@ltu.se; Binh Thai Pham. Email: binhpt@utt.edu.vn
(This article belongs to this Special Issue:Computer Modelling in Disaster Prevention and Mitigation for Engineering Structures)
Received 04 May 2021; Accepted 12 July 2021 ; Published online 09 September 2021
Abstract
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.
Keywords
Compression coefficient; differential evolution; adaptive-network-based fuzzy inference system; machine learning; vietnam