A NOVEL APPROACH TO FORECASTING SHORT UNIVARIATE TUNNEL SETTLEMENT DATA THROUGH MULTI-TASK MULTI-CHANNEL TRAINING OF ADJACENT TUNNEL POINTS
LIM JIA RONG
LIM JIA RONG
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Abstract
With the increased density of urban environments and increased connectivity globally,
tunnel constructions have increased tremendously, and settlement monitoring and
forecasting is crucial to reduce loss of life and resources. There are three methods of
tunnel settlement forecasting, empirical, numerical and intelligent methods. Empirical
and numerical methods are restricted by the need for assumptions for the model to work
and do not perform well when used for different soil types. Intelligent methods were
proved to have great potential in predicting tunnel settlement as they do not require
complex understanding of the mathematical or physical mechanisms of the whole
environment.
Tunnel settlement data collection is difficult and it requires significant resources and
time to collect sufficient datasets for intelligent methods. Hence a significant number of
datasets are univariate time series data collected from measuring the vertical
displacement along the longitudinal axis of the tunnel, making it difficult for deep
learning models to be implemented. This study looked into the relationship between
short univariate datasets collected from this scenario and found that the data from
adjacent points are linearly correlated.
Hence, a multi-task multi-channel method based on nested long short term memory was
proposed to exploit the inter-point relationship and overcome the shortness of the data.
However, the experiment was unable to prove the superiority of the proposed method
over the baseline models and hence a recommendation for future work into using
different machine learning methods, wavelet types for transformation and number of
adjacent points to batch for multi-task prediction is proposed.
Keywords
2020-2021, Building, Bachelor's, BACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT), Yan Ke, Research Subject Categories::NATURAL SCIENCES::Earth sciences::Exogenous earth sciences::Physical geography, Research Subject Categories::TECHNOLOGY::Civil engineering and architecture::Geoengineering and mining engineering::Geoengineering
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Date
2021-06-01
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Dissertation