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- Title
Hybrid Model: Teaching Learning-Based Optimization of Artificial Neural Network (TLBO-ANN) for the Prediction of Soil Permeability Coefficient.
- Authors
Bui, Quynh-Anh Thi; Al-Ansari, Nadhir; Le, Hiep Van; Prakash, Indra; Pham, Binh Thai
- Abstract
The permeability coefficient (k-value) of the soil is an important parameter used in the civil engineering design of roads, tunnels, dams, and other structures. However, the determination of k-value by experimental methods in the laboratory or the field is still costly and time-consuming. Moreover, it requires special equipment and special care in the collection of soil samples for laboratory study. Therefore, in this study, we have proposed machine learning (ML) hybrid model: teaching learning-based optimization of artificial neural network (TLBO-ANN) to predict the k-value of soil based on limited parameters (natural water content, void ratio, specific gravity, liquid limit, plastic limit, and clay content) which can be determined easily in the laboratory. Test results of 84 soil samples obtained from the Da Nang-Quang Ngai expressway project in Vietnam are used in the model development. Statistical indicators such as correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are used to validate and evaluate the accuracy of the model. The results show that the TLBO-ANN model is an effective tool in predicting correctly the k-value (R = 0.905) of soil for the consideration in the design of structures founded on the soil.
- Subjects
DA Nang (Vietnam); VIETNAM; ARTIFICIAL neural networks; SOIL permeability; TEACHING models; STANDARD deviations; ROAD construction; CLAY
- Publication
Mathematical Problems in Engineering, 2022, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2022/8938836