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Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave

Shear Wave Velocity (Vs) profile plays a crucial part in determining seismic site classification. However, field measurement incurs extra cost and time. Economic factors urge the need for a cheaper and faster alternative. Previous studies proposed the use of empirical equations, however there are gr...

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Main Author:	Upom, Mark Ruben
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/102258/1/MarkRubenUpomMSKA2021.pdf.pdf http://eprints.utm.my/id/eprint/102258/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145643
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id	my.utm.102258
record_format	eprints
spelling	my.utm.1022582023-08-13T06:17:12Z http://eprints.utm.my/id/eprint/102258/ Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave Upom, Mark Ruben TA Engineering (General). Civil engineering (General) Shear Wave Velocity (Vs) profile plays a crucial part in determining seismic site classification. However, field measurement incurs extra cost and time. Economic factors urge the need for a cheaper and faster alternative. Previous studies proposed the use of empirical equations, however there are growing evidence that Machine Learning (ML) methods may produce better results. Thus, this study was designed to develop a feasible method of predicting Vs value using ML Models. Due to the impact of weathering profile on seismic site classification, the results of this study are limited to sites with similar geological formation of the study area, which is composed of granitic rocks. The study utilized four types of ML algorithms to develop the predictive model. The ML algorithms used were Multi Linear Regression (MLR), Random Forest (RFR), Artificial Neural Network (ANN) and Support Vector Machine (SVR). The independent variables are Standard Penetration Resistance (Nspt) and depth of soil (Ds), while the dependent variable is Vs. Consequently, this study conducted a Multichannel Analysis of Surface Wave (MASW) survey to get the required dataset. Furthermore, this study verified the Vs profiles using Nspt data. In addition, the hyperparameters for the ML models were determined using Random Search and k-fold Cross Validation. On top of that, this study also used Coefficient of Determination (R ), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as the performance metrics for model selection. The best ML model was determined to be RFR based on the performance metrics (R = 0.9, MAE = 16.93 and RMSE = 19.79). It was then determined that the average percentage difference between the actual and predicted Vs30 was 10.7%. This study also presents the development of a software, pyMASW, for the processing of the raw seismic data. In conclusion, the RFR model can predict Vs30 values for seismic site classification with an accuracy of 89.3%. 2021 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/102258/1/MarkRubenUpomMSKA2021.pdf.pdf Upom, Mark Ruben (2021) Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145643
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Upom, Mark Ruben Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
description	Shear Wave Velocity (Vs) profile plays a crucial part in determining seismic site classification. However, field measurement incurs extra cost and time. Economic factors urge the need for a cheaper and faster alternative. Previous studies proposed the use of empirical equations, however there are growing evidence that Machine Learning (ML) methods may produce better results. Thus, this study was designed to develop a feasible method of predicting Vs value using ML Models. Due to the impact of weathering profile on seismic site classification, the results of this study are limited to sites with similar geological formation of the study area, which is composed of granitic rocks. The study utilized four types of ML algorithms to develop the predictive model. The ML algorithms used were Multi Linear Regression (MLR), Random Forest (RFR), Artificial Neural Network (ANN) and Support Vector Machine (SVR). The independent variables are Standard Penetration Resistance (Nspt) and depth of soil (Ds), while the dependent variable is Vs. Consequently, this study conducted a Multichannel Analysis of Surface Wave (MASW) survey to get the required dataset. Furthermore, this study verified the Vs profiles using Nspt data. In addition, the hyperparameters for the ML models were determined using Random Search and k-fold Cross Validation. On top of that, this study also used Coefficient of Determination (R ), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as the performance metrics for model selection. The best ML model was determined to be RFR based on the performance metrics (R = 0.9, MAE = 16.93 and RMSE = 19.79). It was then determined that the average percentage difference between the actual and predicted Vs30 was 10.7%. This study also presents the development of a software, pyMASW, for the processing of the raw seismic data. In conclusion, the RFR model can predict Vs30 values for seismic site classification with an accuracy of 89.3%.
format	Thesis
author	Upom, Mark Ruben
author_facet	Upom, Mark Ruben
author_sort	Upom, Mark Ruben
title	Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
title_short	Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
title_full	Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
title_fullStr	Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
title_full_unstemmed	Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
title_sort	machine learning shear wave velocity prediction based on multi-channel analysis of surface wave
publishDate	2021
url	http://eprints.utm.my/id/eprint/102258/1/MarkRubenUpomMSKA2021.pdf.pdf http://eprints.utm.my/id/eprint/102258/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145643
_version_	1775621960943796224
score	13.211869

Machine learning shear wave velocity prediction based on multi-channel analysis of surface wave

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