A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Accuracy assessment of extreme learning machine in predicting soil compression coefficient
The compression coefficient (Cc) is an important soil mechanical parameter that represents soil compressibility in the process of consolidation. In this study, a machine learning derived model, namely extreme learning algorithm (ELM), was used to predict the Cc of soil. A total of 189 experimental results were used and randomly divided to construct the training and testing parts for the development and validation of ELM. Monte Carlo approach was applied to take into account the random sampling of samples constituting the training dataset. A number of 13 input parameters reflecting the experiment were used as the input variables to predict the output Cc. Several statistical criteria, such as mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R) and the Monte Carlo convergence estimator were used to assess the performance of ELM in predicting the Cc of soil. The results showed that ELM had a strong capacity to predict the Cc of soil, with the R value > 0.95. The convergence of results, as well as the capability of ELM were fully investigated to understand the advantage of using ELM as a predictor.
Accuracy assessment of extreme learning machine in predicting soil compression coefficient
The compression coefficient (Cc) is an important soil mechanical parameter that represents soil compressibility in the process of consolidation. In this study, a machine learning derived model, namely extreme learning algorithm (ELM), was used to predict the Cc of soil. A total of 189 experimental results were used and randomly divided to construct the training and testing parts for the development and validation of ELM. Monte Carlo approach was applied to take into account the random sampling of samples constituting the training dataset. A number of 13 input parameters reflecting the experiment were used as the input variables to predict the output Cc. Several statistical criteria, such as mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R) and the Monte Carlo convergence estimator were used to assess the performance of ELM in predicting the Cc of soil. The results showed that ELM had a strong capacity to predict the Cc of soil, with the R value > 0.95. The convergence of results, as well as the capability of ELM were fully investigated to understand the advantage of using ELM as a predictor.
Accuracy assessment of extreme learning machine in predicting soil compression coefficient
Ly, Hai-Bang (author) / Asteris, Panagiotis G. (author) / Pham, Thai Binh (author)
2020-06-26
doi:10.15625/0866-7187/42/3/14999
VIETNAM JOURNAL OF EARTH SCIENCES; Vol 42, No 3 (2020): SPECIAL ISSUE: DATA MINING IN EARTH SCIENCES; 228-336 ; 0866-7187
Article (Journal)
Electronic Resource
English
DDC:
690
Machine Learning Methods for Predicting Soil Compression Index
| Springer Verlag | 2024
| Taylor & Francis Verlag | 2016
Learning Curves: Accuracy in Predicting Future Performance
| Online Contents | 1997
Learning Curves: Accuracy in Predicting Future Performance
| British Library Online Contents | 1997