Engineering properties of the Bukit Timah Granitic residual soil in Singapore

Zhang, W. G.; Zhang, R. H.; Han, Liang; Goh, Alvina

doi:10.1016/j.undsp.2018.07.001

Cited by 35 publications

(7 citation statements)

References 9 publications

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“…Comparing the results of the Bayesian analysis with the empirically estimated parameter values, the zero correlation assumption appears to yield values that are better in line with the empirical estimates. In addition, parameter values identified with the zero correlation scheme are also more consistent and in line with laboratory and field test values reported by Leung et al, 40 Moon et al 41 and Zhang et al 42 on similar soil types. Figure 4 shows, for the case of non-zero correlation, the mean and the 95% bounds of the predicted wall deflection profiles of ID 09 and ID 04 at the final excavation stage, after each round of identification.…”

Section: Results Of Bayesian Model Updatingsupporting

confidence: 89%

“…Each vertical axis plots the values values of about 47-50 MPa contained within the second cluster appear to be too high based on the average measured SPT-N value of about 18 for this G (VI) silt layer. Furthermore, parameter values reported in the literature 40,41,20,42 suggest that this parameter should not exceed 35 MPa. The identified silt layer E ref 50 values of 47-50 MPa in the second cluster are thus likely to be erroneous.…”

Section: Results Of Edmfmentioning

confidence: 93%

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Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

Wang

Goh

Koh

et al. 2020

Num Anal Meth Geomechanics

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Summary Back analysis can provide engineers with important information for better decision‐making. Over the years, research on back analysis has focused mainly on optimisation techniques, while comparative studies of data‐interpretation methodologies have seldom been reported. This paper examines the use of three data‐interpretation methodologies on the performance of geotechnical back analysis. In general, there are two types of approaches for interpreting model predictions using field measurements, deterministic versus population‐based, both of which are considered in this study. The methodologies that are compared are (a) error‐domain model falsification (EDMF), (b) Bayesian model updating and (c) residual minimisation. Back analyses of an excavation case history in Singapore using the three methodologies indicate that each has strengths and limitations. Residual minimisation, though easy to implement, shows limited capabilities of interpreting measurement data with large uncertainty errors. EDMF provides robustness against incomplete information of the correlation structure. This is achieved at the expense of precision, as EDMF yields wider confidence intervals of the identified parameter values and predicted quantities compared with Bayesian model updating. In this regard, a modified EDMF implementation is proposed, which can improve upon the limitations of the traditional EDMF method, thus enhancing the quality of the identification outcomes.

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Section: Results Of Bayesian Model Updatingsupporting

confidence: 89%

Section: Results Of Edmfmentioning

confidence: 93%

Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

Wang

Goh

Koh

et al. 2020

Num Anal Meth Geomechanics

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show abstract

“…Proses geologi pada formasi berumur Kuarter mampu menghasilkan sifat kekakuan batuan lebih tinggi dibandingkan pada formasi berumur lebih muda Formasi Alluvial pantai. Kohesi antar butiran pada formasi berumur muda, juga dipengaruhi oleh saturasi air pada ruang pori yang belum termampatkan oleh kompresi (Zhang et al, 2019).…”

Section: Geologi Regionalunclassified

Rock Formation and Site Class in Bandar Lampung

Rustadi

Rananda

2020

JGE

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Soil characteristics are important impact for the scale of damage caused by earthquake vibrations. The impact caused by an earthquake in a location depends on the shear wave velocity (Vs). The study examined the effect of sedimentary rock formations on shear wave velocity and site class based on SPT tests in two rock formations in Bandar Lampung. The value of Vs, Vs30 and site class is determined based on the N-SPT value. Older rock formations of Quaternary age, have produced compaction at a shallow depth of 2 m from the surface, with Vs30 360 m/s and site class C. While the formation is younger, compactions are formed at a depth of 20 m, Vs30 200 m/s and site class D.

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“…Rainfall-induced landslides are the most common geohazards in the tropical and subtropical areas covered by granite residual soil, such as Brazil (Lacerda, 2007;Coutinho et al, 2019), Singapore (Rezaur et al, 2003;Rahardjo et al, 2008;Harianto et al, 2012;Zhai et al, 2016;Zhang et al, 2019), Malaysia (Rahman et al, 2018), Korea (Kim et al, 2004;Pham et al, 2019), the southern (Jiao et al, 40 2005;Luo et al, 2021;Liu et al, 2021;Liu et al, 2020a;Liu et al, 2020b) and southeastern China (Xia et al, 2019;Yao et al, 2021;Shu et al, 2021;Zhao et al, 2021). Guangxi is located in southeastern China, where granite is concentrated in the southeast, and landslides occur frequently (Liao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Failure mode of rainfall-induced landslide of granite residual soil, southeastern Guangxi province, China

Zhao

Liao

et al. 2021

Preprint

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Abstract. Granite residual soil landslides are widely distributed in southeastern Guangxi province, China. They are posing a huge threat to local communities and hindering social and economic development. To understand the failure mode of the landslide can provide a scientific basis for early warning and prevention. In this study, it conducted artificial flume model tests to investigate the failure mode of granite residual soil landslide. The macroscopic phenomena of landslides in the flume were summarized. The changes of soil moisture content along with pore water pressure were analyzed. And the differences and commonness in the initiation patterns of landslides were discussed. The results had four aspects. (1) There were significant similarities in the phenomenon of slope failures. In the beginning of the artificial rain, slopes were infiltrated, following by the slope toe soil softened and slipped. Another similar pattern was that continuous rainfall could cause soil crusts and runoff on the slope surface. Short-term low-lying areas and interlocking ditches would appear due to surface runoff and rainwater erosion. (2) The increase of initial dry density enhanced the permeability resistance of rainwater to the residual soil, which led to a delay in the response time of water content and pore water pressure, and a decrease in pore water pressure. Moreover, the fluctuation characteristics of pore water pressure may be related to the type of soil shear deformation. (3) The starting time of a landslide was delayed as the initial dry density and slope angle increased, but it was shortened due to the increase in rainfall intensity. Meanwhile, the initiation pattern changed from a sudden sliding type to a progressive failure type due to the increase of initial dry density. (4) The failure process of the granite residual soil landslide could be classified into five stages: rainwater infiltration, soil sliding at the slope toe, the occurrence of surface runoff and erosion, the formation of a steep free face, and the upper soil sliding. Above research results can provide valuable references for the prevention and warning of granite residual soil landslide in southeast Guangxi.

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Engineering properties of the Bukit Timah Granitic residual soil in Singapore

Cited by 35 publications

References 9 publications

Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

Rock Formation and Site Class in Bandar Lampung

Failure mode of rainfall-induced landslide of granite residual soil, southeastern Guangxi province, China

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