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By Bora Okumusoglu, Devon K. V. Mothersille

"AnchorTest is an innovative software package, specifically developed for tablets, that permits ground anchor test data assessment and management in real time. The solution has two components: a front-end tablet app runs on the Apple iPad platform, and a back-end Cloudbased system running on Amazon Web Services with an online cross-platform website, which can be accessed from any PC, tablet or even mobile smartphone. This tool provides full data analysis and data management capabilities and portability to create greater efficiency in the anchor testing process. AnchorTest is programmed to accommodate the major international anchor testing codes and incorporates an intuitive user interface, which is revolutionary compared to conventional engineering software designs. The use of this tool creates an environment where the testing of ground anchors has effectively become a paperless exercise with greater efficiency and access to instantaneous feedback on the performance of anchors in relation to established acceptance criteria.Historically, project personnel record and analyze the results from anchor load tests using spreadsheets and a laptop computer and/or on predesigned paperbased test sheets (usually as a backup of the testing). In addition, the processing of the test results is not typically performed in real time as the testing progresses — the calculations and analysis are performed either afterward while still on site (if a field office is available) or after returning to the office. Undoubtedly, on larger projects containing numerous anchors where multiple anchor tests may be performed at the same time, processing, analyzing and accepting the load test data and results can be especially time consuming, which could cause delays to the construction schedule.DevelopmentThe need for a more automated and digitized approach to record, process and analyze the data from anchor load tests was highlighted at a deep basement project for a mixed-use development complex in Moscow, Russia. The diaphragm wall supporting the structure was approximately 1,970 ft (600 m) in perimeter length and about 148 ft (45 m) deep, with an excavated depth of about 82 ft (25 m), which required six levels of single bore multiple anchors (SBMAs). This project provided a challenge to recording, processing and signing off on the data generated from some 3,600 3-unit SBMAs. More specifically, for this project, three hydraulically synchronized stressing jacks were used to stress and proof test each 3-unit SBMA, which resulted in nearly 10,800 data plots of the load-extension tests being generated. This project served as the main impetus in the conceptual design and optimization of AnchorTest."

Jan 1, 2017

By X. Wu

Tunnel Boring Machines (TBMs) are widely used for tunneling in urban areas because of their high advance rate, precise as-built profile and minimal impact to buildings and traffic on the ground. For a tunnel built by a TBM, there are strict requirements on alignment control and safety. To meet these requirements, it is important to monitor the position of the TBM. In the current practice, the TBM position data, including advance distance and line and level deviations, are reported manually to the managers on a daily basis. To interpret and analyze the position data, the managers also need to refer to various construction drawings. This practice is untimely, inefficient, and error prone for decision making. To improve the current practice, an automatic approach for real-time as-built tunnel product modeling and visualization is proposed, based on TBM position and orientation data autonomously sourced from the Virtual Laser Target Board (VLTB) TBM Guidance System developed at University of Alberta. Related information of the tunnel project, such as the as-designed alignment, soil layers, the ground, existing utility lines, is also visualized together with the model of the as-built tunnel. The modeling algorithm is presented step by step. Its application in a real-life drainage tunnel project is discussed and limitations are also identified.

Aug 1, 2013

By Antonio Nieto

According to the analysis of surface powered haulage accidents involving off-highway haul trucks in openpit mining, the U.S. Mine Safety and Health Administration (MSHA), reported 1,300 fatal accidents from 1990 to 2005. Of these, 163 were considered off-highway truck haulage accidents, and at least 23 involved collisions of an off-highway truck with a neighboring vehicle or a worker on foot (Nieto and Dagdelen, 2006). In general, an average of 18 nonfatal accidents and three fatal accidents occurred in openpit mining per year when equipment backed over the edge of an embankment, stockpile or dump point (Fessak, et al., 2003). The specific problems that need to be solved can be determined by carefully studying the accidents involving haulage trucks (Marrara, et al., 2004). Solutions to haulage truck safety must consider the human factors affecting each task even when engineering solutions seem most appropriate. In surface haulage, human performance becomes a critical issue because of the unusual demand the vehicle?s places on their human operators: ? Roadways and working areas change frequently in mining operations. ? The sheer mass of mining equipment such as trucks requires control inputs (e.g., braking) far in advance of the desired action. ? Rough roads, loading and unloading impacts can subject the driver to dangerous shocks and vibration. ? Visibility is sharply curtailed by the bulk of the vehicle generating large blind spots. All of the above issues can be attributed partially to the lack of visibility (Pyles et al., 2003). Driver visibility and communications are key issues to reduce mining truck accidents. Analysis of fatalities and injuries involving mining equipment demonstrates the need to develop new intervention and control strategies to mitigate the risks associated with mining equipment operation. Ongoing development of these and other new strategies is required to achieve a reduction of injury in the mining industry

Jan 2, 2010

By E. J. Sun

The ADS system was developed to reduce accidents related to haul trucks operating under low visibility conditions in surface mining and construction sites, which is based on the GPS, mesh-wireless networks, and the Google-Earth 3D graphic engine as the graphic interface and mine-mapping server. This system has the capability to pin-point and track vehicles in real time using a 3D interface, which is based on user-based AutoCAD mine maps using the Google-Earth graphics interface. All equipped vehicles are shown in a 3D mine map stored in a local server through a wireless network. When low visibility conditions are present, the system indicates available exit/escape routes for driver safety. The research results indicate that the ADS system potentially increases reliability and reduces uncertainty in open pit mining operations by customizing the local 3D digital mining map, constructing the truck 3D models, tracking vehicles in real time using a 3D interface and indicating available escape routes for driver safety.

Oct 1, 2009

By Rebecca Hope, Derick Moolman, Frikkie Malan

"Despite the lip service that has been paid to pro-active plant maintenance processes over recent years, a significant portion of maintenance in industry is still reactive. The business driving force to prevent expensive production opportunity costs arising from “break-down maintenance” is obvious. Various benefits can be derived from successfully deploying remote equipment diagnosis technology and associated business processes.From an end-client’s viewpoint these include the ability to predict and take actions to prevent equipment breakdowns, increasing the lifetime of equipment by responding to appropriate operating instructions and therefore improving the return on capital expenditure. Better planning and scheduling of maintenance priorities and resources with performance measures like an Equipment Health Index are also possible. The knowledge that a qualified supplier engineer is constantly in touch with the operating conditions of their equipment, albeit remotely, is more than a comfort, and can be translated into quantifiable cost savings and improved supplier service levels.This paper analyses the reasons for the inadequate level of pro-active plant maintenance practice in industry today, and presents a number of important criteria in selecting appropriate solutions to this problem. A case study of a specific real-time, remote equipment management application is presented."

Jan 1, 2003

By Rebecca Hope, Derick Moolman, Frikkie Malan

"Despite the lip service that has been paid to pro-active plant maintenance processes over recent years, a significant portion of maintenance in industry is still reactive. The business driving force to prevent expensive production opportunity costs arising from “break-down maintenance” is obvious. Various benefits can be derived from successfully deploying remote equipment diagnosis technology and associated business processes.From an end-client’s viewpoint these include the ability to predict and take actions to prevent equipment breakdowns, increasing the lifetime of equipment by responding to appropriate operating instructions and therefore improving the return on capital expenditure. Better planning and scheduling of maintenance priorities and resources with performance measures like an Equipment Health Index are also possible. The knowledge that a qualified supplier engineer is constantly in touch with the operating conditions of their equipment, albeit remotely, is more than a comfort, and can be translated into quantifiable cost savings and improved supplier service levels."

Jan 1, 2003

By T. Lupek, M. Buxton, A. de Jong

"With regard to the EU funded project ‘Real-Time Mining’ a sensor-based acquisition, collection and transmission of data for the resource extraction process will be developed. The key concept promotes the change in paradigm from a discontinuous to a continuous process monitoring. Especially concerning material characterization, machine performance and underground positioning ‘Real-Time Mining’ will provide sensor solutions as well as the interfaces for integration, management and visualization of the collected data for a permanent updating process of the resource model with the aim of near real-time optimization of decisions. On the basis of ‘Real-Time Mining’-Output a permanent monitoring of costs can be performed as well in both, the installation and the production process of a mine. Due to the collected data in terms of machine performance connected with the underground positioning information added with personnel tracking and mine surveys a comprehensive overview of process performance is available. By evaluating those information in a specific manner costs per unit will arise for monitoring and possibly controlling actions. INTRODUCTION By having a look into the future of mineral extraction projects, a new challenge for the Ore Mining Industry will arise in the short and medium term. Due to the depletion of currently mined mineral reserves in addition to the persistently high demand of mineral raw materials, the extraction of complex reserves will be necessary. By characterizing such deposits as not constantly graded and geometrically varying, the extraction process, especially concerning the requirement of a minimized zero-value material handling, will become economically ineffective by utilizing the traditional mineral resource management methods. Regarding the state-of-the-art mineral resource management process along the mining value chain, an intermitted, discontinuous sequence of actions is applied (Figure 1). Recently data will be collected from drill hole cores. These rock samples are analyzed in a laboratory. Based on those analyses a resource model is created whereby unknown data between drill holes is interpolated. This resource model is the basis for all mine planning actions including the selection of the mining method, extraction sequencing and the prediction of the expected performance. With the start of the production, real conditions (concentrations of elements, hardness, stability, etc.) can be observed and measured. As the production process is running accordingly to the above described mine plan, the verification of the initial assumptions happens at the earliest after processing of the extracted material. Even if deviations between the predicted and the actual performance are now recognized, the execution of corrective actions is not possible anymore."

Jan 1, 2016

By Éric Poulin, Simon Gariépy, Mario Paredes Malca

"While the design of a control strategy depends on the available process knowledge, its implementation depends on the available process information. On-line chemical analyzers are today widely spread in the mineral processing industry. A real-time concentration measurement of chemical components is valuable information that could bring control strategies to whole new levels. However, the use of these measures rarely reaches anything more than process monitoring. The reason is simple: the on-line chemical measurements quality is most of the time insufficient to be directly included in control strategies. The choice is then made to let the critical eye of the engineer or the operator appreciates the measurements before making any decision based on it.Data reconciliation through mass balance is a technique today widely used in the mineral processing industry to increase the accuracy and the reliability of plant data, mainly for accounting and survey analysis purposes. Real-time data reconciliation of on-line measurements appears as a natural coupling of technologies that could bring new process optimization trends. Once overcome the technical difficulties, full valorization of the instrumentation investment could be reached.This paper presents the application of real-time data reconciliation for process control and performance monitoring. It proposes a general approach, discusses challenges and illustrates concepts with industry experiences."

Jan 1, 2006

By M. U. Khan, A. D. S. Gillies

"Diesel exhaust is a major cause of large number of occupational diseases. Acute and continuous exposure to Diesel Particulate Matter (DPM) can cause numerous health issues including respiratory disease, reduced lung capacity, heart disease etc. The National Institute of Occupational Safety and Health (NIOSH) and the International Agency for Research on Cancer (IARC) consider diesel engine exhaust as carcinogenic. In large underground metal and nonmetal mining operations, regulatory compliance regarding miner’s exposure to airborne particles especially DPM is a big challenge. Complex mine networks add more difficulties in controlling DPM. MSHA relies on the NIOSH 5040 method for DPM compliance. The NIOSH 5040 method is a shift average based method which inherits some limitations. Due to these inherent limitations of the NIOSH 5040 method the concept of real-time DPM monitoring is promoted in the mining industry. Several real-time DPM monitors have been used by the researchers and efforts still continues to improve their measuring techniques and understanding. Real-time monitoring of DPM is vital to gain understanding of rapid changes in the mine atmosphere. The current study highlights the advancement in real-time DPM monitoring and mainly covers a review of several studies in which DPM concentrations were measured by utilizing real-time DPM monitors. INTRODUCTION Ventilation of underground mines is an important aspect of mining operations. Mining is a continuous activity and the ventilation networks in modern mines change with extended developments. Proper understanding of the ventilation network is a key design factor of an optimum mine ventilation plan. An important task of underground mine ventilation engineers is to provide clean air with sufficient quantity to the underground miners (Khan M. U. and Gillies A.D.S. 2015). The amount of fresh air supplied to the mine should be adequate to dilute the concentrations of contaminants present in the mine atmosphere. In the United States it is estimated that diesel powered equipment is used in thousands of mining operations (Stephenson D. J. et al. 2006). Use of diesel powered equipment is an attractive option in underground mines due to its several advantages over other available alternates. Diesel engines are rugged, dependable and fuel efficient (McKinnon 1999). It is common for diesel engines in heavy duty trucks to have a life of 1.6 million kilometers (McKinnon 1999). Looking at the developments in other energy alternatives, it can be assumed that the underground mining industry will maintain its reliance on diesel powered equipment (Anon 2001). DPM is an important source of mine air contamination in many non-gassy underground mines (Khan M. U. and Gillies A.D.S. 2015). Diesel exhaust is harmful for humans. Acute and continuous exposure to diesel exhaust concentrations can cause several health issues. Some of the health diseases caused by DPM exposure are respiratory diseases, reduced lung capacity, heart disease, and lung cancer (National Research Council. 1981, Mine Safety. 2005). In addition, NIOSH regards diesel exhaust as “potential carcinogen” (Centers for Disease Control 1988) and IARC has declared that “diesel engine exhaust is carcinogenic to humans” (IARC 2012). NIOSH has also declared that the reductions in DPM exposure would reduce cancer risks (Centers for Disease Control 1988)."

Jan 1, 2016

By Hsin Wei Wu

A real-time Diesel Particulate Matter (DPM) monitor has been developed on the basis of the successful National Institute of Occupational Health and Safety (NIOSH) designed Personal Dust Monitor (PDM) unit. The objectives of a recently completed Australian Coal Association Research Program (ACARP) study were to modify the PDM to measure the submicrometre fraction of the aerosol in a real-time monitoring underground instrument. Mine testing focused on use of the monitor in engineering evaluations to determine how conditions can be improved. Studies, including a selection during Longwall (LW) moves as described in this paper, demonstrated how DPM concentrations from vehicles fluctuate under varying ventilation and operational conditions. Correlation between the current SKC DPM measurement system and real-time DPM monitors were conducted and results from six mines show a correlation between elemental carbon (EC) and the new monitor DPM mass ranging from .51 to .81 with R2 >.90. These differences are suspected to be due to variations from mine to mine in aspects such as mine atmospheric contamination, vehicle fleet variations, fuel type, engine maintenance, engine combustion efficiency, engine behaviour or interference from other submicrometre aerosol. Real-time monitoring readily reflects the movement of individual diesel vehicles and allows pin-pointing of high exposure zones such as those encountered where various vehicles work in areas of constrained or difficult ventilation. DPM monitoring approaches that have been available for some time based on shift average monitoring do not readily allow successful engineering evaluation exercises to determine acceptability of pollution levels. Identification of high DPM concentration zones allows efficient modification of mine ventilation, operator positioning and other work practices t o reduce miners? exposures without waiting for laboratory analysis results.

Jan 1, 2010

By Milan Yekich, Emanuele Cauda, Justin Patts, Cody Wolfe

This study presents the application of low-cost dust monitors (LCDMs) for real-time monitoring of respirable dust in a mining environment. LCDMs significantly enhance data collection by providing continuous, granular and cost-effective dust concentration readings. This case study demonstrates that by deploying LCDMs, mining operators can respond quickly to changes in dust levels, improving occupational safety and air quality management. The results show how such monitoring technologies can be integrated into routine operations to reduce dust exposure and prevent health hazards such as silicosis, especially when coupled with appropriate mitigation strategies.

Nov 1, 2024

By Milan Yekich, Emanuele Cauda, Justin Patts, Cody Wolfe

A worker’s personal exposure to respirable dust in occupational environments has traditionally been monitored using established methodologies which entail the collection of an 8-hour representative sample that is sent away for laboratory analysis. While these methods are very accurate, they only provide information on the average exposure during a specific time period, generally a worker’s shift. The availability of relatively inexpensive aerosol sensors can allow researchers and practitioners to generate real-time data with unprecedented spatial and temporal granularity. Low-cost dust monitors (LCDM) were developed and marketed for air pollution monitoring and are mostly being used to help communities understand their local and even hyper-local air quality. Most of these integrated sensing packages cost less than $300 per unit, in contrast to wearable or area dust monitors specifically built for mining applications which have been around for decades but still average around $5,000 each. At the National Institute for Occupational Safety and Health (NIOSH), we are leveraging the power of high-volume data collection from networks of LCDM to establish baseline respirable hazard levels and to monitor for changes on a seasonal basis as well as following any application of control technologies. We have seen the effective use and advantages of monitoring live data before, during, and after events like shift changes, operational changes, ventilation upgrades, adverse weather events, and machine maintenance. However, many factors have prevented a systematic adoption of LCDMs for exposure monitoring: concern for their analytical performance, the complexity of use, and lack of understanding of their value are some factors. This contribution outlines a one-year case study at a mine in Wisconsin USA, covering the installation, maintenance, data visualizations, and collaboration between NIOSH researchers and the industrial hygiene professionals at the mine.

Feb 1, 2024

By Kihong Park, Matthews C. Habjan, Arthur L. Miller

New Mining Safety and Health Administration (MSHA) regulations limit the mass concentration of airborne diesel particulate matter (DPM) or, more specifically, the concentration of elemental carbon (EC), in underground mines. The mine operators are responding by introducing a variety of controls to reduce DPM in the mines, potentially including the evaluation of new maintenance procedures to reduce underground mine vehicle emissions. There is currently a lack of an inexpensive and dependable method to directly measure the DPM concentration emitted from the vehicle tailpipe. To that end, this work demonstrated a simple field portable method for estimating the mass concentration of elemental carbon exiting the tailpipe of a diesel engine using a direct reading photometer. Simultaneous measurements of tailpipe exhaust were made with a Thermo Electron Personal DataRAM 1200 photometer (particulate mass concentration based on light scattering) and by analyzing PM2.5 and PM1.0 samples collected on quartz fiber filters using the National Institute of Occupational Safety and Health (NIOSH) method 5040 (mass concentration of EC via thermal-optical method). Results indicate surprisingly good correlation (R2 ) 0.97) of the two methods when the data are adjusted for relative humidity (RH) and corrected using an empirically generated calibration factor. Although preliminary, it may be possible to implement this method in maintenance shops to monitor emission trends and to compare emissions of various vehicles in a fleet. Such data will be useful for fleet planning to meet new air quality standards.

By J. A. Herbst

It is widely recognized that Semiautogenous Grinding (SAG) circuits require the use of effective process control techniques to achieve their full processing potential and to protect them from the harmful effects of overloads and grind-outs. This requirement is even more essential as the variability of the ore being processed increases. The new Chino Mines Company concentrator located near Santa Rita, New Mexico started operation in June of 1982. At least eight major rock types are mined and milled. Ore hardness, feed size, and specific gravity are quite variable. Typical feed rates per grinding line are 850-900 tph but extremes of 260-1200 tph have been encountered. Normal feed rate variations of between 400 and 1100 tph are encountered on a weekly and sometimes daily basis. Feed size varies with rock type from a maximum of eight inches for the harder ores to two inches for the softer ores. One somewhat unique characteristic of the Santa Rita ore body among major copper mines is the wide variation in ore specific gravity. Specific gravities of the major rock types range between 2.7 and 3.6. The specific gravity variations are due to large changes in magnetite concentration throughout the pit. The result of this is large variations in SAG mill throughput which can have a harmful effect on downstream ball milling and flotation performance. Due to these wide fluctuations in ore characteristics, it was recognized soon after startup that successful operation of the grinding circuit and the plant at large was dependent upon the development of an effective automatic grinding control strategy. Research at the University of Utah has suggested that control based upon critical but difficult or costly to measure variables would improve automatic control performance. A project team made up of the Chino process control staff, Kennecott's Process Control department and the University of Utah Metallurgy Department was established to assess these concepts at Chino. This paper describes the development and on-line plant implementation of an Extended Kalman Filter that uses simple models of the SAG milling process, to predict the volumetric loading, the rock mass, the water mass, and the ball mass within the SAG mills at Chino. The on-line estimation of these parameters is the first step in the development of an integrated SAG mill/ball mill control strategy which is hoped to L eventually maintain the grinding circuit at its maximum capacity, while balancing the grinding load between the SAG and ball mills. Examples of long term estimation with the models and filter are shown for typical operating conditions as well as a SAG mill overload and grind-out.

Jan 1, 1983

By J Moffatt, T Payten, N Spooner, L Teixeira, L A. Balzan, G Wilkie, G Tsiminis

Fluorine is a penalty element due to its reactive nature. In acidic environments many fluorine minerals dissolve to form hydrofluoric acid (HF), changing processing conditions. When smelted HF can be released, causing negative effects to nearby people and the environment, as well as potentially damaging the smelter. Sale of ore with high fluorine levels elicit a financial penalty or are banned outright. Real-time sensors create the opportunity for instantaneous decisions on ore processing due to changing conditions. A fluorine sensor would allow rises in fluorine concentration in a processing stream to be flagged, and decisions on processing conditions or diversion to stockpile before processing could be undertaken. As fluorine is a light element, it is difficult to detect with in-field XRF devices and does not work well with PGNAA. There are currently no existing sensors to quantify the presence of fluorine in real-time, and thus decisions based on fluorine grade cannot be made at minute- or even hour-scales. Sensors designed to measure the presence of minerals that contain fluorine are a possible avenue to enable the real-time detection of this element. Mineral-based sensing may also give an added advantage of additional information about how the fluorine will react under different processing steps. The authors were given a grant under CRC ORE II (project number P1–005) to search for mineralspecific signatures suitable for real-time analysis. The work focused on novel regimes and applications of fluorescence as a means to detect specific minerals. The aim was to exploit fluorescence responses unique to minerals of interest in mining systems in order to produce sensors that address gaps in existing capability. After the successful discovery of signatures specific to two major fluorine minerals an additional grant (P1–014) was funded to develop the research into a prototype on-belt sensor, in partnership with Scantech International Pty Ltd. This paper details the development of a sensor package through multiple Technology Readiness Levels (TRLs) through collaboration with government/industry grant schemes, academics from university, and a sensor-focused Mining Equipment and Technology Services (METS) company. We give a brief description of the new sensor technique and provide a case study of multi-disciplinary collaboration.

Aug 24, 2022

By A. F. van der Merwe, R-D. Taylor, M. Jacobs, F. H. Conradie

Total bound moisture (TBM) is a typical quality indicator of industrial-grade gypsum. This gypsum is comprised of three distinct phases, namely anhydrite, dihydrate, and hemihydrate, of which only the latter is of much industrial use. TBM analysis is a lengthy laboratory procedure, and an artificial neural network (ANN) TBM inference measurement is proposed as a fast and online alternative. An ANN inference model for gypsum TBM based on plant data was developed. The inputs to the network were primarily focused on the plant's calciner, and different network topologies, data divisions, and transfer functions were investigated. Furthermore, the applicability of the TBM value as a quality indicator was investigated based on a gypsum phase analysis. A strong correlation between TBM and the gypsum hemihydrate and anhydrite content was found, validating the plant target TBM of 5.8% as a quality indicator. A network topology consisting of one hidden layer with logarithmic-sigmoid (logsig) and pure linear (purelin) transfer functions showed the best performance (R > 90%).

Oct 3, 2023

By T Belligoi

Diamond cores and drill chips provide the only direct physical evidence for ore containing rocks. Early characterisation of cores and drill chips is important as it provides critical information for downstream processing. Traditional characterisation techniques include visual logging by a geologist or analytic and spectroscopic methods but they are time consuming and can miss some important textural or geomechanical features.The goal of this work is to evaluate the application of image analysis to iron ore cores and drill chips and to suggest improvements to existing core-scanning and chip monitoring systems. Two samples of massive ore differing by their mineralogy and texture, as well as two different batches of drill chips, have been imaged and analysed. Results are compared in function of the imaging system and the classification algorithms.As a conclusion, despite the necessity for more testing and improvements in calibration and lighting, it is obvious that image analysis technologies can bring a lot of useful information in the perspective of a geometallurgical mapping of the deposit.

Jul 11, 2011

An image analysis system has been built which automatically digitizes camera input of rock fragments, analyses the digitised data to determine the sizes and shapes of the rock fragments, and outputs the analysed data to a printer in the form of a histogram or a scatter chart. The high speed of such a system allows for direct collection of data from material thrown into the air by blasting, material travelling on a conveyor belt, and material free-falling on to a stock-pile or into or out of a rock-crusher. Data on the blasted, un-crushed material provides information on changes in the lithology and structure of the rock face as it is quarried or mined: flatter fragments may represent approach to a brittle, unstable shear zone, or a change from sandstone to shale; so that the informa- tion reflects the effect on the sizes and shapes of the fragments by the blasting process and/or by the rock structure. The size and shape distributions of rock fragments produced during blasting and during processing may now be continuously monitored. Such detailed analysis of the results of fragmentation is labour-intensive and expensive when the size and shape distributions are obtained manually, but is cheap and efficient when using this image analysis system. Such a system will enable the results of blasting, quarrying, mining and crushing to be cheaply analysed and correlated with current and changing indus- trial practice so leading the way to increased productivity.

Jan 1, 1988

By Hao Zhang

Site inspection is an important component of safety management system. In current practices, the inspectors evaluate the safety condition on a construction site with the assistance of a standardised checklist. However, the checklist emphasizes on evaluating the implementation of safety management system, but provides limited task-specific check items for assessing whether the individual construction tasks are conducted under safe condition and in a safe manner. To address this issue, the research presented in this paper aims to provide safety practitioners with comprehensive and taskspecific safety information to promote effective risk identification and facilitate strategic risk control during site inspection. Specifically, this research first builds a project-specific safety information database, which stores potential risks and control measures for each construction task. The research team then develops a mobile decision support tool to retrieve information from the database and support safer decision-making on site. The tool supports users in two ways: one is to provide daily risk checklists and risk control strategies to guide step-by-step inspection alongside the project schedule; the other is to let users input any safety-related keywords during the safety inspection process and the tool returns relevant safety information by applying text mining algorithms. It is ongoing research. This paper presents an overall research framework and discusses the progress in developing safety database and data retrieval concepts. The outcome of the project will help enhance safety management practices and reduce possible risky working conditions on a construction site.

Aug 1, 2013

Real-Time Integration of Mining and Metallurgical Information for Efficient Use of Energy and Water

Sep 13, 2010