Research Output
Prediction of pressuremeter modulus (EM) using GMDH neural network: a case study of Kenny Hill Formation
Assessment of the Twin-Tunnel Interaction Mechanism in Kenny Hill Formation Using Contraction Ratio Method
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Publication
Soil investigation (SI) work is a process of collecting subsurface ground profile information in evaluating soil engineering properties for a construction project. The standard penetration method (SPT) is widely accepted worldwide as a feasible and relatively inexpensive method over other field tests. Pressuremeter test (PMT), on the other hand, is costly and more popular in situ method in interpreting soil deformation behavior. Considering SPT is available in almost site investigation works for all sizes of project, it was tempting to establish the correlation between SPT and PMT results, specifically in local geologic setting. The study was conducted in Kenny Hill Formation, Kuala Lumpur, Malaysia. The correlation established in this study is between pressuremeter modulus (EM) and SPT blow count (N60). In addition to in situ methods, the physical properties of soil extruded from boreholes were tested in geotechnical laboratories to determine soil properties, such as particle size distribution, liquid limit, and plastic limit. These parameters need to be considered during prediction of EM. Group method of data handling (GMDH) neural network has been used to achieve this objective. The mean absolute error (MAE) results show that the GMDH neural networks produce values of 8.04 and 8.59 for training and testing. The root mean square error (RMSE) yields 10.61 and 10.84 for training and testing, respectively. Further, the results of the coefficients of determination (R2) are 0.794 and 0.726 for training and testing demonstrates a good correlation exists between predicted and measured values. Based on the GMDH results, N60, sand, and clay are required input variables for determination of EM.
Publication
Volume loss during tunneling excavation leads to ground deformation, which can damage adjacent surfaces or subsurface structures. Thus, tunnel design with proper estimation of ground deformation and realistic geotechnical simulation is essential for large-scale urban underground construction. In this study, subsurface characterization of the tunnel excavation section in Kenny Hill Formation (KHF) was conducted to develop 3D ground model and tunnel-filtered models and obtain ground sections through the spatial interpolation of borehole data using the Inverse Distance Weighting (IDW) method. Six greenfield ground sections were selected by using the tunnel-filtered model’s configuration of tunnels and available tunneling-induced ground movement data. Conceptual models for finite element modeling were developed based on soil profiles, and the corresponding soil parameters were determined from ground sections. The strength and stiffness parameters of the Hardening Soil (HS) model were established using data from site investigation, in situ and laboratory tests, and empirical correlations with standard penetration test. The effectiveness of empirical correlations was determined through back analysis of twin-tunnel excavation in 2D finite element analysis using the contraction method and verified with monitored ground movement data. The numerical back-analyzed results of twin-tunnel excavation simulation using HS parameters obtained from a selected empirical correlation showed good agreement with construction-monitored ground movements. The application range of the values of contraction ratio was from 0.3 to 0.95%.