Some instability problems were found on natural or engineered slopes mostly lying on Subang claystones. The instability problems included excessive erosion, slumps and rock falls. The field performance surveys of the problems suggested that the claystones physically weather rapidly so that the rock properties they exhibit during excavation often change to properties with a more characteristic of soil. Such a phenomenon is generally known as a slaking process. In order to gain better understanding about the slaking of Subang claystones, a series of experimental laboratory studies were carried out involving a modified slaking index test. Claystone samples used in this study were obtained from their exposures along the Northern West Java area of Indonesia. Petrographic analysis was correspondingly performed to identify mineral and texture/fabric, and in turn, to determine the inherent factors of the rocks which might affect the slaking process. The study results indicated that the claystones were characterized by high to very high slaking properties having a maximum slaking index (I s ) of 57.4% and a mean I s of 43.8%. Major dispersion slaking on sample surfaces and high cracking in response to excessive swelling were recognized as main slaking modes within the claystones. All samples lose progressively less material through the five wet-dry cycles of a slaking index test, indicating a decelerated slaking rate. It was evident that the main inherent factors controlling the slaking process were expandable clay mineral smectite, non-clay mineral pyrite and soluble mineral calcite. Moreover, a quite important of inherent bonding material and stress release energy in the slaking characteristics of the claystones was revealed by a closure phase of an initial hairline crack during unloading.

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A landslide susceptibility mapping is essential for landslide hazard mitigation to reduce the associated risk. This paper aims to present the results of the landslide susceptibility modeling in the Citarik sub-watershed using three bivariate statistical-based methods, i.e., frequency ratio (FR), information value (IV), and weight of evidence (WoE). The main objective of this study is to evaluate the significance of the threshold of the area under curve (AUC) value in parameter selection. In this study, 118 landslide pixels were compiled from Google Earth images, unmanned aircraft vehicle (UAV) aerial photos taken just after the landslide, official landslide reports, and field observation. Thirteen landslide causative factors were prepared in Geographic Information System (GIS) environment, derived from various satellite images and maps. The landslide data were divided into two groups, 70% of data as training data and the rest as test data. Two scenarios that involve a different number of parameters were compared to explain the threshold of the AUC value in parameter selection and model accuracy. The result of this study shows that the AUC value threshold of 0.6 for parameter selection cannot be applied in all cases, and the performance of both two scenarios was excellent in assessing landslide susceptibility in this study area. Those three landslide susceptibility zonation maps of the best scenario showed that the sub-watershed's northern, northeastern, south-eastern, and southern parts were under high to very high susceptibility to landslides, including the Cimanggung area where a recent deadly double landslide occurred.

In order to gain insights into the detailed physical disintegration characteristics of various types of mudrock, a series of static slaking exposure and immersion tests were carefully carried out in this study. The intent of this paper is to exhaustively describe and discuss the results of the tests, including slaking mechanism, mode and rate/intensity. In general, it can be obviously identified that there are significant different susceptibilities of each mudrock tested to slake-disintegration. These differences can not only be identified from the results of slaking exposure test but also from slaking immersion test. It seems that there is also an agreement in both testing results, which show that the most resistant mudrock to slaking was Ikeshima shales, and was comparatively, followed by Ombilin siltstones, Tanjung Enim mudstones-claystones, and Subang claystones as the worst slaking characteristic. The detailed differences in fundamental characteristics of physical disintegration characteristics will further widely be discussed in this paper. SariDalam upaya untuk mengetahui secara mendalam sifat disintegrasi fisik berbagai jenis batulumpur, serangkaian pengujian dengan penyingkapan disintegrasi statis dan perendaman secara seksama dilakukan pada studi ini. Maksud penulisan makalah ini adalah untuk mendeskripsikan dan membahas hasil pengujian, termasuk mekanisme disintegrasi, cara (model), dan tingkat/intensitasnya. Pada umumnya dengan mudah dapat diidentifikasi bahwa ada berbagai macam kerentanan yang signifikan pada batulumpur yang diuji terhadap disintegrasi fisik. Keragaman ini tidak saja dapat diidentifikasi dari hasil uji penyingkapan disintegrasi, tapi juga dari uji rendam pelapukan. Tampaknya bahwa ada kesesuaian di antara kedua uji tersebut yang mengungkapkan bahwa batulumpur yang paling resisten terhadap disintegrasi adalah serpih Ikeshima, dan secara komparatif diikuti oleh batulanau Ombilin, batulumpur-batulempung Tanjung Enim, dan batulempung Subang yang mempunyai sifat paling buruk. Perbedaan fundamental terperinci dalam sifat disintegrasi fisik lebih lanjut akan dibahas dalam makalah ini.Kata kunci: disintegrasi, batulumpur, singkapan, perendaman, uji disintegrasi

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Sigi Biromaru is an area prone to landslides. This study aims to apply the statistical method of Weight of Evidence (WoE) in landslide susceptibility mapping using Geographic Information Systems (GIS). The 265 landslides that occurred 2009-2019 were randomly divided into two groups, 70% of the data were used as training dataset for susceptibility modelling and 30% of the data were used as test data for validation of the susceptibility model. Twenty-one parameters were tested for their influence on landslides. Based on the Area Under Curve (AUC), parameters that significant controlling the landslides are slope gradient, elevation, aspect, flow direction, peak ground acceleration, clay content (<0,002 mm), land cover, terrain ruggedness index (TRI), river density, soil type, lineament density, lithology, rainfall and stream power index (SPI) respectively. The validation results show that the AUC success rate is 0,811 using the training dataset and AUC prediction rate is 0,756 using the test dataset. These results indicate that the WoE method produces a good landslide susceptibility map in the Sigi Biromaru area.