Laboratory Study of the Shear Behaviour of Natural Rough Rock Joints Infilled by Different Soils

Natural rock joints infilled with soil materials may show a reduced shear strength, which influences rock mass stability. The aim of this paper is to experimentally investigate the shear behaviour of infilled rock joints, taking into account joint surface characteristics and the properties of the joint and infill materials. A new model for predicting the shear strength of infilled joints is presented, on the basis of a series of tests carried out on natural rock joints with same surface roughness, with clay, sand and sandy-clay used as infill materials. All tests were carried out in a shear box apparatus under constant normal load (CNL) conditions. The empirical model was finally validated based on the experimental data from the literature. The results showed an acceptable confidence level for the model and reported that the new model successfully describes the observed shear behaviour of natural infilled rock joints

A Case Study of High School Chinese as a Foreign Language Blended Program

This qualitative case study investigated a Chinese as a foreign language program composed of three different instructional modes: synchronous instruction with videoconferencing technology, asynchronous instruction with online tutorials, and physically co-located face-to-face instruction. The study adopted Larry Cuban's multi-layered curriculum framework and investigated the four curriculum layers within the blended program: intended curriculum, taught curriculum, learned curriculum, and tested curriculum. This research utilized interviews, observation, and document analysis as the instruments in data collection. The participants consisted of one administrator, eight language teachers, four facilitators and twelve high school students. In addition to teaching site observation, the researcher also traveled to four remote school sites to observe how the curriculum was learned from the students' perspective. The results of the study indicated that although the intended curriculum reveals the administrator's ideal picture of blended learning design and defines what teachers should teach and what students should learn in each instructional delivery mode, the actual implementation process of blended learning is much more complex. The findings of the study showed that language teachers' specific operation of the daily lessons in a blended context and students' actual learning experiences at the remote sites can be influenced by many other variables; these variables lead the intended curriculum into different versions between the classes of the taught curriculum, learned curriculum and tested curriculum. Therefore, technology integration should not only be focused on the design of the external layer of the curriculum (the intended curriculum), but should also be focused on the implementation through the rest of the curriculum layers

Relationship between Texture and Uniaxial Compressive Strength of Rocks

Uniaxial compressive strength (UCS) is one of the most important parameters of rocks that is routinely used in rock engineering designs. This parameter is influenced greatly by textural properties of rocks; hence it is possible to estimate it from quantified texture coefficient (TC). In this paper, fourteen different types of rocks were experimentally studied to evaluate the effect of texture coefficient on UCS. Thin sections were first prepared, and then some digital photographs were taken from each section and were digitized in computer. Then, the texture coefficient for all samples were calculated. Subsequently, UCS of the samples were measured in laboratory. Finally, relationships between TC and UCS of rock samples were evaluated and related mathematical equations were presented. Results showed that the UCS has a power relationship with TC which can be utilized for future estimation purposes

Evaluation of rock slope stability conditions through discriminant analysis.

A methodology to predict the stability status of mine rock slopes is proposed. Two techniques of multivariate statistics are used: principal component analysis and discriminant analysis. Firstly, principal component analysis was applied in order to change the original qualitative variables into quantitative ones, as well as to reduce data dimensionality. Then, a boosting procedure was used to optimize the resulting function by the application of discriminant analysis in the principal components. In this research two analyses were performed. In the first analysis two conditions of slope stability were considered: stable and unstable. In the second analysis three conditions of slope stability were considered: stable, overall failure and failure in set of benches. A comprehensive geotechnical database consisting of 18 variables measured in 84 pit-walls all over the world was used to validate the methodology. The discriminant function was validated by two different procedures, internal and external validations. Internal validation presented an overall probability of success of 94.73% in the first analysis and 68.42% in the second analysis. In the second analysis the main source of errors was due to failure in set of benches. In external validation, the discriminant function was able to classify all slopes correctly, in analysis with two conditions of slope stability. In the external validation in the analysis with three conditions of slope stability, the discriminant function was able to classify six slopes correctly of a total of nine slopes. The proposed methodology provides a powerful tool for rock slope hazard assessment in open-pit mines

Face stability analysis of mechanized shield tunneling: An objective systems approach to the problem

peer reviewedThe stability of the tunnel face is one of the most critical issues having to be secured for a successful tunneling practice. It becomes more crucial for the tunneling in an urban environment and even more when large diameters are contemplated, where catastrophic and costly consequences can happen due to the excessive settlements and ground deformations. In this research, an objective systems methodology is incorporated into this problem for the first time, and through the application of machine learning, a Face Vulnerability Index (FVI) is presented to assess the stability conditions of tunnels. To this end, seven parameters that are important for tunnel face stability in the subsoil – including engineering geological, geotechnical and environmental factors – are employed for the FVI definition, and a comprehensive worldwide database of mechanized tunneling case histories is developed. The interaction matrix in the framework of the systems approach is then objectively coded by using the database and a deep learning technique (deep Artificial Neural Networks- ANN) capabilities. The results (FVI predictions) are compared with a number of well-known analytical methods and the actual applied face-support pressures. A good agreement between predictions and observations has been found that proves the field applicability of the new index to a great extent, which has led to the suggestion of design graphs for future applications

The Flotation System Optimization in Alborz-Sharghi Coal Washing Plant; A Laboratory Study

This paper tries to determine an optimum condition for the flotation operation of the Alborz-Sharghi coal washing plant. For this purpose, a series of comprehensive experiments have been conducted on representative samples from feed of the flotation system of the plant. Four operational variables such as the collector dosage (Fuel oil), the frother dosages (MIBC), the pulp density percent and the impeller speed were taken into account. After obtaining representative samples, 81 required experiments were designed using the orthogonal array (34) of Taguchi method. Three levels of the variables amount including low, base and high were considered for the experiments. The most obvious finding to emerge from this study was that the optimum flotation recovery (61.09 %) is obtained in the base level (L-2) of the collector dosage, the lowest level (L-1) of MIBC and the highest levels (L-3) of the pulp density and the impeller speed. The sensitivity analysis of the variables also indicated that the increase in the collector dosage causes to increase in the total recovery of the flotation and the coal quality. Besides, the largest effect on total recovery was clearly related to the pulp density levels. The increase in values of the pulp density causes to decrease in the recovery values

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance

Evaluation of rock slope stability conditions through discriminant analysis.

A methodology to predict the stability status of mine rock slopes is proposed. Two techniques of multivariate statistics are used: principal component analysis and discriminant analysis. Firstly, principal component analysis was applied in order to change the original qualitative variables into quantitative ones, as well as to reduce data dimensionality. Then, a boosting procedure was used to optimize the resulting function by the application of discriminant analysis in the principal components. In this research two analyses were performed. In the first analysis two conditions of slope stability were considered: stable and unstable. In the second analysis three conditions of slope stability were considered: stable, overall failure and failure in set of benches. A comprehensive geotechnical database consisting of 18 variables measured in 84 pit-walls all over the world was used to validate the methodology. The discriminant function was validated by two different procedures, internal and external validations. Internal validation presented an overall probability of success of 94.73% in the first analysis and 68.42% in the second analysis. In the second analysis the main source of errors was due to failure in set of benches. In external validation, the discriminant function was able to classify all slopes correctly, in analysis with two conditions of slope stability. In the external validation in the analysis with three conditions of slope stability, the discriminant function was able to classify six slopes correctly of a total of nine slopes. The proposed methodology provides a powerful tool for rock slope hazard assessment in open-pit mines