TY - GEN
T1 - Prediction of vegetation-induced soil suction using numerical modelling and AI
AU - Priya, M. Indu
AU - Garg, Ankit
AU - Sreedeep, S.
AU - Sarmah, Ajit
AU - Daud, Nik Norsyahariati Nik
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - Study of soil suction is important in design and implementation of slope stability and erosion control measures. In order to conduct a realistic analysis of performance of sustainable green infrastructure, it is essential to address the uncertainties in suction induced by vegetation due to variability in their leaf and root characteristics, evapotranspiration (ET) and initial conditions of the soil. The objective of this study is to investigate the combined influence leaf area index (LAI), root depth, ET rate and initial suction of soil on root water uptake-induced soil suction. A parametric numerical study was performed with 480 simulations using HYDRUS to carry out the finite element analysis. The study was done on completely decomposed granite (CDG) soil and vegetation species used was Schefflera heptaphylla. It was observed that although if independently considered, vegetation with higher LAI provided greater mechanical stability, when combined with higher ET rates or initial suction, the suction induced may lead to wilting of the vegetation. Artificial intelligence technique such as Artificial neural network (ANN) was used to predict matric suction at any given depth using the results obtained from the numerical simulations. Performance of the best model indicated that ANN was able to successfully predict the vegetation-induced matric suction.
AB - Study of soil suction is important in design and implementation of slope stability and erosion control measures. In order to conduct a realistic analysis of performance of sustainable green infrastructure, it is essential to address the uncertainties in suction induced by vegetation due to variability in their leaf and root characteristics, evapotranspiration (ET) and initial conditions of the soil. The objective of this study is to investigate the combined influence leaf area index (LAI), root depth, ET rate and initial suction of soil on root water uptake-induced soil suction. A parametric numerical study was performed with 480 simulations using HYDRUS to carry out the finite element analysis. The study was done on completely decomposed granite (CDG) soil and vegetation species used was Schefflera heptaphylla. It was observed that although if independently considered, vegetation with higher LAI provided greater mechanical stability, when combined with higher ET rates or initial suction, the suction induced may lead to wilting of the vegetation. Artificial intelligence technique such as Artificial neural network (ANN) was used to predict matric suction at any given depth using the results obtained from the numerical simulations. Performance of the best model indicated that ANN was able to successfully predict the vegetation-induced matric suction.
KW - Artificial intelligence
KW - Numerical modelling
KW - Soil matric suction
UR - http://www.scopus.com/inward/record.url?scp=85060620437&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-2224-2_43
DO - 10.1007/978-981-13-2224-2_43
M3 - Conference Proceeding
AN - SCOPUS:85060620437
SN - 9789811322235
T3 - Environmental Science and Engineering
SP - 351
EP - 358
BT - Proceedings of the 8th International Congress on Environmental Geotechnics Volume 2 - Towards a Sustainable Geoenvironment
A2 - Bouazza, Abdelmalek
A2 - Zhan, Liangtong
A2 - Chen, Yunmin
PB - Springer Berlin Heidelberg
T2 - 8th International Congress on Environmental Geotechnics, ICEG 2018
Y2 - 28 October 2018 through 1 November 2018
ER -