Applying fuzzy cognitive mapping to improve productivity of tunnel boring machines
Productivity of the Tunnel Boring Machines (TBM) is always a concern when bored tunnelling is used in tunnel construction projects. Low productivity would cause project delays, resulting in inefficient use of resources and unnecessary costs incurred. However, there are no efficient ways to determine...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78633 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Productivity of the Tunnel Boring Machines (TBM) is always a concern when bored tunnelling is used in tunnel construction projects. Low productivity would cause project delays, resulting in inefficient use of resources and unnecessary costs incurred. However, there are no efficient ways to determine the productivity of the TBM because current solutions are by means of multiple lab tests, which require time and resources. This report aims to analyse the main variables that affect the productivity of the TBM and predict the probability of each of the variables in affecting the productivity of the TBM using fuzzy cognitive mapping (FCM). 8 variables that are most commonly linked to TBM productivity are first identified through literature review and consultations with Land Transport Authority (LTA). Expert opinions on the influence of the 8 variables and TBM productivity on each other are then collected and aggregated. After which, the aggregated data is input for FCM modelling using an FCM software. Results from the software are then analysed in 2 different ways. By incorporating FCM into the study of the productivity of a TBM, this report aims to identify the crucial factors that play the most important role in increasing productivity tunnel construction. |
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