Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model
| Autor: | Amir Mosavi, Danial Mohammadzadeh S., Ehsan Nasseralshariati, Joseph H. M. Tah, Seyed-Farzan Kazemi |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
construction Computer Science - Machine Learning Coefficient of determination Mean squared error 0211 other engineering and technologies Machine Learning (stat.ML) 02 engineering and technology 010501 environmental sciences Atterberg limits 01 natural sciences Statistics - Applications lcsh:Technology Machine Learning (cs.LG) gene expression programming (GEP) soil engineering Statistics - Machine Learning big data Statistics General Materials Science Applications (stat.AP) Neural and Evolutionary Computing (cs.NE) 021101 geological & geomatics engineering 0105 earth and related environmental sciences Civil and Structural Engineering Mathematics Consolidation (soil) lcsh:T Computer Science - Neural and Evolutionary Computing deep learning fine-grained soils Building and Construction 68Q05 prediction data mining Geotechnical Engineering and Engineering Geology Computer Science Applications Void ratio Data point machine learning Gene expression programming infrastructures civil engineering Arithmetic mean soil compression index |
| Zdroj: | Infrastructures Volume 4 Issue 2 Infrastructures, Vol 4, Iss 2, p 26 (2019) |
| ISSN: | 2412-3811 |
| DOI: | 10.3390/infrastructures4020026 |
| Popis: | In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models. 8 figures, 5 tables, 12 pages |
| Databáze: | OpenAIRE |
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