Prediction of consolidation coefficient of soft soil using an artificial neural network models with biogeography-based optimization

Authors

Cai-jin WANG, Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 211189, ChinaFollow
Meng WU, Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 211189, China
Yang YANG, Jiangsu Province Transportation Engineering Construction Bureau, Nanjing, Jiangsu 210004, China
Guo-jun CAI, Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 211189, China; School of Civil Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, ChinaFollow
Song-yu LIU, Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 211189, China
Huan HE, Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 211189, China
Jian-xin CHANG, Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 211189, China

Abstract

The consolidation coefficient Cv of soft soil is an important parameter in geotechnical engineering. The artificial neural network (ANN) model is improved with the biogeography-based optimization algorithm (BBO). And the artificial neural network with biogeography-based optimization (ANN-BBO) model, trained and tested by using the subgrade soft soil data of the reconstruction and expansion project of Lianyungang–Huai’an expressway, has been adopted to calculate the soft soil consolidation coefficient. Using the correlation coefficient matrix and principal component analysis, eleven physical and mechanical parameters were statistically analyzed, seven of which were identified as input parameters of calculation model which was then trained and tested. The model was tested by correlation coefficient, root mean square error, and variance ratio, whose robustness was analyzed by using Monte Carlo simulation. The results show that the ANN-BBO model can be used to calculate the consolidation coefficient of soft soil, the correlation coefficient R2 = 0.947 1, the root mean square error RMSE = 0.165 7×10−3 cm2 /s, and the variance ratio VAF = 94.54%. The ANN-BBO model has significantly higher prediction accuracy and better robustness, compared with the ANN model.

Recommended Citation

WANG, Cai-jin; WU, Meng; YANG, Yang; CAI, Guo-jun; LIU, Song-yu; HE, Huan; and CHANG, Jian-xin (2023) "Prediction of consolidation coefficient of soft soil using an artificial neural network models with biogeography-based optimization," Rock and Soil Mechanics: Vol. 44: Iss. 10, Article 7.
DOI: 10.16285/j.rsm.2023.5209
Available at: https://rocksoilmech.researchcommons.org/journal/vol44/iss10/7