Universiti Teknologi Malaysia Institutional Repository

Prediction of shear wave velocity in underground layers using particle swarm optimization

Upom, M. R. and Asmawisham Alel, M. N. and Ab. Kadir, M. A. and Yuzir, A. (2019) Prediction of shear wave velocity in underground layers using particle swarm optimization. In: 11th International Conference on Geotechnical Engineering in Tropical Regions, GEOTROPIKA 2019 and 1st International Conference on Highway and Transportation Engineering, ICHITRA 2019, 27-28 Feb 2019, Kuala Lumpur, Malaysia.


Official URL: https://dx.doi.org/10.1088/1757-899X/527/1/012012


Shear wave velocity (Vs) is considered a key soil parameter in the field of earthquake engineering. The time-averaged shear wave velocity in the upper 30 m (Vs30) layer of soil is used to classify seismic site class. In-situ Vs test is sometimes unsuitable to the project's need due to financial reasons, noisy environment on site or simply the lack of expertise. This paper attempts to develop a global prediction model for Vs using Standard Penetration Resistance (Nspt), depth (z) and soil type (s t) as the independent parameters. Two approaches to modelling would be taken; a multi-linear regression (MLR) model and an ensemble (EN-PSO) model. The EN-PSO model attempts to improve upon the accuracy of the MLR model prediction ability using the ensemble learning method. A dataset was compiled from literatures for this paper. 5 Base models were developed: MLR, Random Forest (RFR), Support Vector Machine (SVR), Artificial Neural Network (ANN) and k-Nearest Neighbor (KNN) which are combined into an ensemble model named EN-PSO. The weights for EN-SPO was then calculated using Particle Swarm Optimization (PSO). The performance of each models were then compared and it was shown that EN-PSO was the best in terms of: MAE (Mean Absolute Error) = 22.085, MAPE (Mean Absolute Percentage Error) = 9.1 %, RMSE (Root Mean Square Error) = 31.741 and R2 Coefficient of Determination) = 0.895. In addition, it was also shown that the EN-PSO model was able to improve upon the performance of the MLR model, which the most accurate among the Base models. Comparisons were also made between EN-PSO and other suggested Universal Vs correlations and EN-PSO was shown to outperform the other correlation based on prediction using a modified Test set. Three new empirical correlations as alternative for the EN-PSO model was also presented.

Item Type:Conference or Workshop Item (Lecture)
Uncontrolled Keywords:acoustic wave velocity, decision trees, earthquake engineering
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:90002
Deposited By: Narimah Nawil
Deposited On:29 Mar 2021 13:57
Last Modified:29 Mar 2021 13:57

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