Figure
Rock mass classification RMR system (Bieniawski, 1989) A. CLASSIFICATION PARAMETERS AND THEIR RATINGS
Source publication
During the feasibility and preliminary design stages of a project, when very little detailed information on the rock mass and its geomechanic characteristics is not available, the use of a Rock Mass Classification Scheme (RMCS) can be of considerable benefit. Various parameters were used in order to identify the RMCS. The parameter comprised of Roc...
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Actualmente, existen pocos estudios que aplican métodos de simulación geoestadística para modelar las propiedades geomecánicas de los macizos rocosos, y la mayoría de estos estudios pasan por alto la anisotropía de los datos geotécnicos. Por lo tanto, el objetivo del presente estudio es desarrollar un modelo geotécnico en 3D mediante el Kriging ord...
Citations
... Geologically, the Kundasang area consists of several types of lithology, such as the Crocker Formation (Late Eocene to Early Miocene age) and the Trusmadi Formation (Paleocene to Eocene age), granite intrusion, and several recent Quaternary alluvial materials which are still being deposited (Tongkul, 1987, Roslee et al., 2008. The Trusmadi Formation, in general, exhibits two distinct structural orientations -NW-SE and NE-SW (Tongkul, 2007, Roslee et al., 2020.-and is characterized by the presence of dark-colored argillaceous rocks, siltstone, and thin-bedded turbidite in a well-stratified sequence (Yusoff et al., 2016, Roslee et al., 2020. Jacobson (1970) divided the Trusmadi Formation into four main lithological units: argillaceous rocks, interbedded sequences (turbidites), cataclasites, and massive sandstones. ...
... Geologically, the Kundasang area consists of several types of lithology, such as the Crocker Formation (Late Eocene to Early Miocene age) and the Trusmadi Formation (Paleocene to Eocene age), granite intrusion, and several recent Quaternary alluvial materials which are still being deposited (Tongkul, 1987, Roslee et al., 2008. The Trusmadi Formation, in general, exhibits two distinct structural orientations -NW-SE and NE-SW (Tongkul, 2007, Roslee et al., 2020.-and is characterized by the presence of dark-colored argillaceous rocks, siltstone, and thin-bedded turbidite in a well-stratified sequence (Yusoff et al., 2016, Roslee et al., 2020. Jacobson (1970) divided the Trusmadi Formation into four main lithological units: argillaceous rocks, interbedded sequences (turbidites), cataclasites, and massive sandstones. ...
Background
Mapping and monitoring the state of activity of landslides is crucial for effective landslide management and risk assessment. This study presents a novel approach using vegetation anomalies indicator (VAI) derived from high-resolution remotely sensed data for landslide state of activity mapping. The study focuses on the Kundasang area in Sabah, Malaysia, known for its tectonic activity. High-resolution remotely sensed data were utilized to assist in the manual inventory process of landslide activities and to generate VAIs as input for modeling.
Results
The landslide inventory process identified active, dormant, and relict landslides. The resulting inventory map was divided into training (70%) and validation (30%) datasets for modeling purposes. Seven main VAIs, including canopy gap, mature woody vegetation, primary forest, Root Strength Index (RSI), and water-loving tree, were produced and used as the input for the classification process using Support Vector Machine (SVM) and Artificial Neural Network (ANN) methods. The result showed that SVM outperforms ANN for both deep-seated and shallow rotational landslides, with an overall accuracy of 68.6% and 80.7%, respectively. Furthermore, an evaluation of SVM revealed that the radial basis function (RBF) kernel yielded the highest accuracies, whereas ANN performed best with a hyperbolic tangent (tanh) activation function.
Conclusion
The accurate classification of landslide state of activity using VAI provides several benefits, including the ability to map and classify landslide activity in forested areas, characterize vegetation characteristics specific to each activity state, and enable continuous monitoring in areas where field monitoring is impractical. This research opens new possibilities for more accurate landslide activity mapping and monitoring, thereby improving disaster risk reduction and management in tectonically active regions.
