The characterization of rock properties is a vital task in the challenge for hard rock mining operation. A simplified and straightforward characterization of rock properties provides information about the safety of ground structure (e.g. slope, tunnel, etc.), and the strategy to improve productivity in terms of rock breakage process. The penetration-rate of drilling has been proposed to quantify the comminution characteristics of rock by virtue of real-time logging of drilling performance otherwise the data is obtained from a time- and cost-consuming laboratory test; this is called measurement while drilling. In the mining industry, this technique can be a useful tool that has allowed for the meticulous and routine data collection of geological information from blasthole drilling operations. In this study, the mechanical performance of drill and its interaction with the rock properties is investigated in laboratory scale. The rock properties include tensile strength, hardness, and grindability, which is considered as the influential parameters of the required energy consumption for the comminution processes. For sandstone samples, the penetration-rate data shows a good correlation with tensile strength, hardness, and Bond work index; this implies that penetration-rate data can be a good indicator to estimate comminution characteristics. Additionally we carried out the same test with limestone samples. Second, field study is conducted to investigate the interaction between current blast design and rock fragmentation. Fabricating the blast design and fragmentation through the blast operation might enable to construct proper strategy to reduce the energy cost of downstream processes including crushing and grinding by using the rock characteristics measured from the blasthole drilling. The concept of this process is a part of Mine-to-Mill optimization. The thesis proposed the blueprint of Mine-to-Mill optimization, providing a guideline for further in-situ research.

Thesis (M.A.) (Social Work) --University of Limpopo, 2007. / An evaluation of Employee Assistance Programme is pivotal in any department or enterprise. An EAP must be evaluated to justify its existence to external authorities, to ascertain the extent to which the programme is fulfilling its objectives and to find ways to improve its performance. The purpose of this study was to evaluate the EAP within the Department of Public Works in Vhembe District specifically focusing on the employees’ awareness of the programme, its utilization, as well as programme adequacy. A quantitative approach was used in this study. Eighty six (86) employees from various levels in the Department were selected to participate in the study using a systemic random sampling in which every tenth person from the sampling frame was selected. Summary of the main findings The following is a summary of major findings from the study: 􀂃 The majority of the employees were aware of EAP within the Department of Public Works through meetings. 􀂃 The Employee Assistance Programme within the Department of Public Works was viewed as accessible by the majority of employees. 􀂃 The utilization rate of EAP within the Department of Public Works in Vhembe District was low, since only 29% of employees indicated that they had utilized the services before. 􀂃 The employees who had utilized the EAP were mainly referred by their supervisors while some referred themselves. Only ten percent (10%) of supervisors/managers indicated that they had referred employees to the EAP. The referral rate of employees to the EAP by supervisors/managers was low. 􀂃 EAP was viewed as confidential by the majority of employees within the Department and the level of employees’ trust to the EAP staff was high. v 􀂃 The majority of employees were not aware of the EAP policy and they had never participated in policy formulation. 􀂃 Most employees within the Department indicated that the EAP was addressing their personal problems, and the programme was viewed as useful (programme adequacy). 􀂃 The majority of employees were satisfied with the EAP within the Department of Public Works in Vhembe. 􀂃 Most employees identified a need for EAP staff to inform all employees within the Department about its services and to visit the Cost Centres frequently. / Not available

Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.

Although the concept of real estate market analysis are more widely used in real estate industry but penetration rate seem to be misunderstood by some commentators in the market. To accomplish a penetration analysis, existing models have to extensive taking the specific characteristics of explainable model and techniques that allow the market commentators to estimate penetration rate with more accuracy through existing models by integrate changes in the macro economy. The main purpose of this paper is to explain and analyze to give some issues for the prediction of how business cycle and real estate cycle will affect to penetration rate. The scope of this thesis is to study of a medium sized complete residential development in Sweden namely Gåshaga Pirar in Lidingö municipality.

Drilling is one of the important aspects in the oil and gas industry due to the high demand for energy worldwide. Drilling time is considered as the major part of the operations time where the penetration rate (ROP) remains as the main factor for reducing the time. Maximizing ROP to lower the drilling cost is the main aim of operators. However, high ROP if not controlled may impact on the well geometry in terms of wellbore instability, cavities, and hole diameter restrictions. Accordingly, more time is needed for the other operations that follow such as: pool out of hole (POOH), casing running, and cementing. Bit wear is considered as the essential issue that influences in direct way on the bit performance and reduce ROP. Predicting the abrasive bit wear is required to estimate the right time when to POOH to prevent any costly job to fish any junk out to the surface. The two-common types of bits are considered in the research, rock bits (roller cone bits) and Polycrystalline Diamond Compact bits (PDC). This study focuses more on PDC bits because about 60% of the total footage drilled in wells worldwide were drilled by PDC bits and this is expected to reach 80% in 2020. The contribution of this research is to help reducing the drilling cost by developing new tools not to estimating the time when to POOH to surface but also to measure the wear and enhance the accuracy of prediction the bit efficiency. The work is broken down into four main stages or models to achieve the objective: The first stage; estimating of the rock abrasiveness and calculate the dynamic dulling rate of the rock bit while drilling. The second stage; estimating the PDC abrasive cutters wear by driving a new model to determine the mechanical specific energy (MSE), torque, and depth of cut (DOC) as a function of effective blades (EB). The accuracy of the predicted wear achieves 88% compared to the actual dull grading as an average for bits used in five wells. The third stage; modifying the previous MSE tool to develop a more accurate approach; effective mechanical specific energy (EMSE), to predict the PDC bit efficiency in both the inner and outer cone to match the standard bit dulling. The fourth stage; predicting ROP while PDC drilling in hole by accounting three parts of the process: rock drillability, hole cleaning, and cutters wear. The results achieve an enhancement of about 40% as compared to the available previous models. Consequently, the developed models in this study provide a novelty on understanding in more details the bit rock interface process and gain an idea of the relationship between the drilling parameters to enhance the bit performance and avoid damaging the bit. This is basically about optimisation the controllable factors such as: weight on bit (WOB), rotary speed (RPM), and flow rate. The result is the reduction in time losses and the operations cost. To ensure reliability and consistency of the proposed models, they were validated with several vertical oil wells drilled in Libya. The results from the validation of the models are consistent with the real field data. The research concludes that the developed models are reliable and applicable tool for both: to assist decision-makers to know when to pull the bit out to surface, and also to estimate the bit performance and wear.

Nowadays the use of chemical gel injection for humid masonry provides many benefits. If we consider the financial aspects of this kind of remediation of humid masonry, we can say that this method is least expensive and time-consuming. In comparison with other remediation methods major interventions into the structure is not required, which could result in deterioration of the structural analysis of the works and the consequent need of special machinery, which subsequently increases spent effort and resources. There is also need for additional screens easily applied to walls breached by rising damp and this need is also required for research in dealing with this issue. Only a standard regulation dealing with remediation of wet masonry using chemical injection is directive WTA 4-4-04 / D – Injection of masonry against capillary moisture. From this point of view there is a clear need for further development in this area.

The macroscopic measure of traffic stream density is crucial in advanced traffic management systems. However, measuring the traffic stream density in the field is difficult since it is a spatial measurement. In this dissertation, several estimation approaches are developed to estimate the traffic stream density on signalized approaches using connected vehicle (CV) data. First, the dissertation introduces a novel variable estimation interval that allows for higher estimation precision, as the updating time interval always contains a fixed number of CVs. After that, the dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the traffic stream density using CV data only. The proposed model-driven approaches are evaluated using empirical and simulated data, the former of which were collected along a signalized approach in downtown Blacksburg, VA. Results indicate that density estimates produced by the linear KF approach are the most accurate. A sensitivity of the estimation approaches to various factors including the level of market penetration (LMP) of CVs, the initial conditions, the number of particles in the PF approach, traffic demand levels, traffic signal control methods, and vehicle length is presented. Results show that the accuracy of the density estimate increases as the LMP increases. The KF is the least sensitive to the initial traffic density estimate, while the PF is the most sensitive to the initial traffic density estimate. The results also demonstrate that the proposed estimation approaches work better at higher demand levels given that more CVs exist for the same LMP scenario. For traffic signal control methods, the results demonstrate a higher estimation accuracy for fixed traffic signal timings at low traffic demand levels, while the estimation accuracy is better when the adaptive phase split optimizer is activated for high traffic demand levels. The dissertation also investigates the sensitivity of the KF estimation approach to vehicle length, demonstrating that the presence of longer vehicles (e.g. trucks) in the traffic link reduces the estimation accuracy. Data-driven approaches are also developed to estimate the traffic stream density, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The data-driven approaches also utilize solely CV data. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Lastly, the dissertation compares the performance of the model-driven and the data-driven approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the linear KF approach is highly recommended in the application of traffic density estimation due to its simplicity and applicability in the field. / Doctor of Philosophy / Estimating the number of vehicles (vehicle counts) on a road segment is crucial in advanced traffic management systems. However, measuring the number of vehicles on a road segment in the field is difficult because of the need for installing multiple detection sensors in that road segment. In this dissertation, several estimation approaches are developed to estimate the number of vehicles on signalized roadways using connected vehicle (CV) data. The CV is defined as the vehicle that can share its instantaneous location every time t. The dissertation develops model-driven approaches, such as a linear Kalman filter (KF), a linear adaptive KF (AKF), and a nonlinear Particle filter (PF), to estimate the number of vehicles using CV data only. The proposed model-driven approaches are evaluated using real and simulated data, the former of which were collected along a signalized roadway in downtown Blacksburg, VA. Results indicate that the number of vehicles produced by the linear KF approach is the most accurate. The results also show that the KF approach is the least sensitive approach to the initial conditions. Machine learning approaches are also developed to estimate the number of vehicles, such as an artificial neural network (ANN), a k-nearest neighbor (k-NN), and a random forest (RF). The machine learning approaches also use CV data only. Results demonstrate that the ANN approach outperforms the k-NN and RF approaches. Finally, the dissertation compares the performance of the model-driven and the machine learning approaches, showing that the ANN approach produces the most accurate estimates. However, taking into consideration the computational time needed to train the ANN approach, the huge amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is highly recommended in the application of vehicle count estimation due to its simplicity and applicability in the field.