Research Output
Correlation Between SPT and PMT for Sandy Silt: A Case Study from Kuala Lumpur, Malaysia
Prediction of pressuremeter modulus (E M) using GMDH neural network: a case study of Kenny Hill Formation
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Publication
In this study, the pressuremeter modulus (EM) and the unload–reload modulus (Eur) consisted of a wide range of data were correlated to blow counts (N60) using a maximum of 50 blows/300 mm and the extrapolated N60 of 300 blows/300 mm. A 3D model and statistical analysis were used to provide relevant justifications for the selection of this extrapolation method, considering that N60 was limited to 50 blows. In generating a 3D model, the N60 profile was developed using the inverse distance weighting method for predicting unsampled data between boreholes. Correlations were established for the sandy silt soil type that was observed as the dominant lithology in the Klang Valley Mass Rapid Transit line 1 project in Kuala Lumpur, Malaysia. A total 52 pressuremeter test and standard penetration test data pairs were obtained at depths ranging from 6 to 41.7 m within the Kenny Hill Formation (KHF) and the contact zone between the KHF and the limestone formation. This contact zone has shown distinct geological features with the characteristic of a lower N60 value underlying stiff strata. According to the EM/Eur ratio, the maximum value of 7 indicated that this zone is unpredicted in initial and unload–reload stiffness compared to the non-contact zone (the KHF only), with a maximum ratio of 3. Therefore, separate correlations were established to distinguish these zones. Strong correlations between N60 and EM were identified by splitting these zones. The proposed correlation was then compared with the previous research.
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.