Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153680
Title: Grouting in rock caverns
Authors: Amirul Hazim Salleh
Keywords: Engineering::Civil engineering::Geotechnical
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Amirul Hazim Salleh (2021). Grouting in rock caverns. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153680
Project: GE-41AB
Abstract: Land-scarce countries, such as Singapore, are looking at different, innovative ways to create more space. One such way is to build underground spaces, such as rock caverns, and use them as storage spaces so that more land can be freed up. With the construction of rock caverns, grouting has been employed to control the inflow of groundwater to maintain the hydraulic gradient around the cavern, prevent construction delays and unsafe working environment. An accurate volume of grout is needed to ensure economical use, however current practices are largely still empirical, practical and do not follow any analytical models. This study proposes the use of Artificial Neural Networks (ANN), a data-mining process already commonly used in other geotechnical problems. Parameters such as the rock mass quality (Q value), water inflow rate and ingress pressure were used as inputs to establish four ANN models. The coefficient of correlation, or R values, of each model were analysed, and it was found that the model with Q value and water inflow rate as inputs had the most optimal performance and thus, the most influence on predicting grout volume. Limitations in data size and model parameters were identified and improvements to address both were recommended for future work to further improve model accuracy.
URI: https://hdl.handle.net/10356/153680
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:CEE Student Reports (FYP/IA/PA/PI)

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