STUDI KOMPARASI KLASIFIKASI MASSA BATUAN SMR, Q-SLOPE, DAN RHRS UNTUK ANALISIS KESTABILAN LERENG PADA AREA JALAN AKSES PEMBANGUNAN LOWER DAM CISOKAN, KABUPATEN BANDUNG BARAT

• Main Author: Matakupan, Zapheline
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/87040
• Institution: Institut Teknologi Bandung

The Cisokan Lower Dam Access Road is the only access road to the Cisokan hydroelectric power construction site, so the level of safety including slope stability needs to be considered. Therefore, this research was conducted to analyze slope stability at several data acquisition points in the road area by comparing several rock mass classification methods. The results of this research also provide an evaluation for slope reinforcement in the study area. In this research, a scanline survey was conducted to determine the discontinuities' orientation, spacing, condition, and number of planes, as well as groundwater conditions and slope geometry. Physical and mechanical properties of rocks were obtained from laboratory testing results. Secondary data in the form of some rock mechanical properties and seismic coefficients were also used. The rock mass classification methods applied and compared were Slope Mass Rating (SMR), a development of Rock Mass Rating (RMR), and Q-Slope, a development of Q-System. Slope stability analysis in the form of landslide type and Safety Factor (FK) value determination was carried out based on kinematic and limit equilibrium analysis with variations in pseudostatic conditions and water content levels. Based on the SMR classification, the slopes in the research area are classified as unstable-partially stable, while based on the Q-Slope classification, the slopes in the research area are classified as stable. Slope stability analysis shows that the slope most prone to collapse is TAR3-3 with a toppling failure, an FK value in dry and static conditions of 1.20, and a Probability of Failure (PK) of 60%. The research results showed that the SMR classification is more effective to use because it projects the worst possibilities so that mitigation against landslide disasters can be more efficient.