Numerical investigations of rock bridge effect on open pit slope stability

Abstract: persistent joint networks in numerical models, and the critical profiles of an open pit mine were analysed. Parallel deterministic networks of infinite and finite lengths, ubiquitous joint network model and Veneziano joint network model were used in order to simulate the rock fractures. Materials were modelled based on the generalised Hoek―Brown and equivalent MohreCoulomb failure criteria. The parallel deterministic infinite and the ubiquitous joint network models produced lower safety factors. The introduction of rock bridges along discontinuity planes in the parallel deterministic network and Veneziano joint network models significantly contributed to the stability and strain distribution, which should be considered in stability analysis of rock mass in open pit by rock slope practitioners. The results show the significance of joints in hard rock behaviour and the joints should be included in order to attain practical and realistic simulations

Smart slope monitoring through the use of fibre optic sensors

Abstract: This study demonstrates the use of fibre optic instrumentation such as an Optical Time-Domain Reflectometer as well as Fibre Bragg Grating sensors on a small-scale physical 1-g model to monitor potential slope movement. The scope is to improve current knowledge in the field of slope monitoring through the im- plementation of optic fibre sensors. Single-mode and multi-mode hetero-core optic fibre displacement sensors were created and directly embedded into layers of coarse-grained soil. By inducing critical slope conditions in the small-scale model through the course of several experiments we were able to identify localised failure zones and quantify signal attenuation. Using a calibrated source, it was possible to indirectly estimate microstrain and investigate spatial resolution of the sensing cable. Laboratory testing of the sensors and the sensing system allowed for further development of sensor integration techniques

An Introduced Methodology for Estimating Landslide Hazard for Seismic andRainfall Induced Landslides in a Geographical Information System Environment

The demand for estimating landslide hazard has evolved during the last decade. Landslides are characterised among the most severe natural hazards, which can cause casualties, fatalities, harm or detriment in natural and man-made environment. In the first part of this paper the results of the research conducted on slope deformation due to seismic loading are presented. According to field observations deformation and displacement of natural and man-made slopes in strong earthquakes are common phenomena, even though they are associated to moderate magnitude seismic events. These permanent displacements are due to seismic loading, and are produced because the material, through which acceleration pulses have to travel before reaching the ground surface, has a finite strength, and stresses induced by strong earthquakes may overcome this strength limit and bring about failure. Many methods were developed in order to assess the earthquake induced ground displacements due to seismic energy flow. We applied the simplified Newmark’s model, in order to study the problem of slope stability estimation and induced permanent deformations. In the current paper, the outcome of the studies attached to slope stability estimation under static and dynamic conditions considering the factors controlling safety conditions is introduced. These principal factors were first introduced to an artificial neural network and the estimated factor of safety and displacement were subsequently implemented in a geographical information system. A software tool was developed in order to produce landslide hazard maps due to static and dynamic loading, implementing failure criteria. In the second part, the results of the investigation of slope hydrology conditions in slope stability are presented. In these cases the factor of safety decreases due to prolonged precipitation and eventually the slope may fail. A parametric study of the effect of suction zone in slope stability of unsaturated soils is examined. This study focuses on slope behaviour under rainfall conditions

Experimental Study of Sinkhole Propagation Induced by a Leaking Pipe Using Fibre Bragg Grating Sensors

Sinkhole formation caused by leaking pipes in karst soluble rocks is a significant concern, leading to infrastructure damage and safety risks. In this paper, an experiment was conducted to investigate sinkhole formation in dense sand induced by a leaking pipe. Fibre Bragg grating (FBG) sensors were used to record the strain. A balloon was gradually deflated within a bed of wet silica sand to create an underground cavity. Eighteen FBG sensors, with a wavelength range between 1550 nm and 1560 nm, were embedded horizontally and vertically in the physical model at different levels to monitor deformation at various locations. A leaking pipe was installed to induce the collapse of the formed arch above the cavity. The strain measurements suggested the following four phases in the sinkhole formation process: (1) cavity formation, (2) progressive weathering and erosion, (3) catastrophic collapse, and (4) subsequent equilibrium conditions. The results showed differences in the strain signatures and distributions between the horizontal and vertical measurements. During the critical phase of the sinkhole collapse, the horizontal measurements primarily showed tension, while the vertical measurements indicated compression. This investigation demonstrates the effectiveness of FBGs as advanced monitoring tools for sinkhole precursor identification. The study also suggests using FBGs in geotechnical monitoring applications to improve the understanding and mitigation of sinkholes and related geohazards.</jats:p

Investigation of the stability of a fly ash pond facility using 2D and 3D slope stability analysis

A numerical investigation of the effect of pore pressure regime on the safety factor and the critical failure mechanism is presented for fly ash storage facility. Pore pressures’ measurements from standpipe piezometers and pore pressure estimated from seepage analysis are used to compare the factor of safety for a fly ash slope. This was applied for considering static and seismic scenarios. A probabilistic approach was applied to account for the uncertainties resulting from the limited data available and support a qualitative risk assessment evaluation. Slope stability analysis is conducted in two and three dimensions, adopting the limit equilibrium analysis approach, and also a finite element seepage analysis, to assess the stability of the slope. The two-dimensional cross-sections were extruded to three-dimensional models to estimate the factor of safety and associated shear failure. The results from the performed analysis suggest an increase in safety factor values of 5%

Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety and reliability. Before the widespread usage of computers, slope stability analysis was conducted through semi analytical methods, or stability charts. Presently, engineers have developed many computational tools to perform slope stability analysis more efficiently. The challenge associated with furthering slope stability methods is to create a reliable design solution to perform reliable estimations involving a number of geometric and mechanical variables. The objective of this study was to investigate the application of tree-based models, including decision tree (DT), random forest (RF), and AdaBoost, in slope stability classification under seismic loading conditions. The input variables used in the modelling were slope height, slope inclination, cohesion, friction angle, and peak ground acceleration to classify safe slopes and unsafe slopes. The training data for the developed computational intelligence models resulted from a series of slope stability analyses performed using a standard geotechnical engineering software commonly used in geotechnical engineering practice. Upon construction of the tree-based models, the model assessment was performed through the use and calculation of accuracy, F1-score, recall, and precision indices. All tree-based models could efficiently classify the slope stability status, with the AdaBoost model providing the highest performance for the classification of slope stability for both model development and model assessment parts. The proposed AdaBoost model can be used as a screening tool during the stage of feasibility studies of related infrastructure projects, to classify slopes according to their expected status of stability under seismic loading conditions

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique

Data for: Intelligent Open-pit Slope Stability Index

The coded database of open-pit slopes employed to develop and test the IOMSS

Data for: Intelligent Open-pit Slope Stability Index

Results of SOM trainin