Contribution of two artificial intelligence techniques in predicting the secondary compression index of fine-grained soi
- PDF / 1,389,114 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 87 Downloads / 193 Views
TECHNICAL PAPER
Contribution of two artificial intelligence techniques in predicting the secondary compression index of fine‑grained soils Mohammed el Amin Bourouis1 · Abdeldjalil Zadjaoui1 · Abdelkader Djedid2 Received: 14 March 2020 / Accepted: 30 July 2020 © Springer Nature Switzerland AG 2020
Abstract Fine soils have the particularity of producing very slow settlement over time, particularly secondary settlement, also known as creep. The coefficient Cα that characterizes the creep phenomenon seems difficult to evaluate in the laboratory and in situ. Two approaches are proposed in this article for a better and faster prediction of that coefficient. The first approach is based on machine learning using multi-gene genetic programming, and the second one uses hybridization of particle swarm optimization algorithms and artificial neural networks. A regression analysis allowed identifying the determinant parameters to be used in the calculations. A database from several sites, and containing 203 samples, was utilized. The findings showed that a good agreement exists between the predicted and measured values. This also indicates that these two techniques can be quite interesting for engineers when they have to design works on compressible soils. Keywords Creep · Secondary compression · Genetic programming · Optimization · Neural networks · Particulate swarm Abbreviations MGGP Multi-gene genetic programming NN-PSO Neural network particle swarm optimization PSO Particle swarm optimization QPSO Quantum particle swarm optimization ANN Artificial neural network RMSE Root-mean-square error MAE Mean absolute error
Introduction Clayey fine soils have the particularity of producing a settlement that can be divided into two phases, a primary settlement and a secondary settlement. The first one is more or less rapid and is due to the expulsion of water from the voids of soil. The second one is much slower; it is caused by the deformation of solid particles and takes several years * Mohammed el Amin Bourouis [email protected] 1
Department of Civil Engineering, Faculty of Technology, Aboubekr Belkaid University, BP 230 Chetouane, 13000 Tlemcen, Algeria
Department of Architecture, Faculty of Technology, Aboubekr Belkaid University, BP 230 Chetouane, 13000 Tlemcen, Algeria
2
to stabilize. This phenomenon, known as creep, is difficult to study because it raises many difficulties that are mainly related to the complexity of the structure of fine soils and to their behavior as well. The most frequently used parameter to characterize this creep is the coefficient of secondary compression, generally noted Cα (Fig. 1). This index, which is a key parameter for most viscoplastic behavior models applied in the engineering practice [19, 40, 42], is generally obtained by means of conventional oedometric tests. In this article, two novel approaches are suggested to predict this secondary compression index: One is based on multi-gene genetic programming and the other one on hybridization of artificial neural net
Data Loading...
