Dissolution-enlarged fractures imaging using electrical resistivity tomography (ERT)

In recent years the electrical imaging techniques have been largely applied to geotechnical and environmental investigations. These techniques have proven to be the best geophysical methods for site investigations in karst terrain, particularly when the overburden soil is clay-dominated. Karst is terrain with a special landscape and distinctive hydrological system developed by dissolution of rocks, particularly carbonate rocks such as limestone and dolomite, made by enlarging fractures into underground conduits that can enlarge into caverns, and in some cases collapse to form sinkholes. Bedding planes, joints, and faults are the principal structural guides for underground flow and dissolution in almost all karstified rocks. Despite the important role of fractures in karst development, the geometry of dissolution-enlarged fractures remain poorly unknown. These features are characterized by an strong contrast with the surrounding formations in terms of physical properties, such as electrical resistivity. Electrical resistivity tomography (ERT) was used as the primary geophysical tool to image the subsurface in a karst terrain in Greene County, Missouri. Pattern, orientation and density of the joint sets were interpreted from ERT data in the investigation site. The Multi-channel Analysis of Surface Wave (MASW) method and coring were employed to validate the interpretation results. Two sets of orthogonal visually prominent joints have been identified in the investigation site: north-south trending joint sets and west-east trending joint sets. However, most of the visually prominent joint sets are associated with either cultural features that concentrate runoff, natural surface drainage features or natural surface drainage --Abstract, page iii

Measuring the Effectiveness of Mining Shovels

Electric and hydraulic shovels are the dominant loading machinery in surface mining operations. Despite their critical role in production and their high capital and operating costs, no reliable and comprehensive quantitative performance metric is available. In this paper, a stochastic shovel effectiveness (SSE) measure is proposed for the purpose of quantifying the performance effectiveness of these shovels. The SSE is based on the widely used method of overall equipment effectiveness (OEE) in the manufacturing industry. The OEE measures the performance effectiveness of equipment by multiplying its mechanical availability, utilization and production quality. In manufacturing processes, quality rate is the ratio of the total number of products minus the number of defective products - equivalent to the number of acceptable products - to the total number of products. The SSE similarly uses the mechanical-availability and utilization terms, but instead of quality rate it uses a new parameter named bucket rate. The variability or randomness of the input data, that is, availability, utilization and bucket rate, are further incorporated into the SSE, and a final stochastic SSE distribution is derived in the form of a probability density function. One hydraulic and one electric shovel in a surface mining operation were selected to test the validity of the proposed method. The SSE scores for the two shovels, operating continuously for one year, were derived and compared. As with the OEE, the three-parameter SSE method yielded more representative results for overall performance measurement than a single-parameter approach. Using Monte Carlo simulation, a three-parameter Weibull and a normal distribution were derived for quantifying the overall effectiveness of hydraulic and electric shovels, respectively. As a decision aid, the proposed methodology promises to render a more informative tool than traditional metrics for mine equipment maintenance and management

Work breakdown structure (WBS) development for underground construction

A work breakdown structure (WBS) can prove to be pivotal to successful project management planning. There are few published studies about the methodologies or tools to develop the appropriate WBS for a project, and those that are available are limited to the specific areas of construction such as apartment-building construction and boiler manufacturing. This research has an emphasis on developing a methodology with higher generalizability, which has the capability to be customized to complex underground projects. To address this issue, a new methodology that employs hierarchical neural networks to develop the WBS of complex underground projects is presented. This methodology has been applied to several tunnel case studies and it has been shown that for a real project, the model is able to generate the WBS and its activities that are comparable to those generated by a project planner. Consequently, it is concluded that these modeling methods have the capacity to significantly improve the WBSs for complex underground projects and improve key project tasks, such as workload planning, cost estimating and scheduling