Optimization Model for Maintenance Planning of Loading Equipment in Open Pit Mines

Maintenance plays a significant role in operating costs in the mining industry. Improving this matter controls maintenance costs and enhances productivity and production effectively. Shovels are one of the most widely used loading machines in non-continuous activities. Thus, evaluating and optimizing their availability is one of the essential solutions to achieving high productivity and cost reduction. This paper presents a mathematical programming model to maximize availability and minimize the total expected costs. We programmed the proposed nonlinear planning model using the Symbiotic Organisms Search (SOS) meta-heuristic algorithm in Matlab software. It determines the optimal maintenance intervals for different parts of the shovel. The maintenance benefit analysis approach selects various maintenance activities in optimal maintenance intervals. The model is implemented in a practical case study, Chadormalu Iron Mine, to evaluate its performance. The failure distribution matches the Weibull distribution function. The computational results show the efficiency of the presented approach

Assessing the Frequency and Severity of Malware Attacks: An Exploratory Analysis of the Advisen Cyber Loss Dataset

In today\u27s business landscape, cyberattacks present a significant threat that can lead to severe financial losses and damage to a company\u27s reputation. To mitigate this risk, it is essential for stakeholders to have an understanding of the latest types and patterns of cyberattacks. The primary objective of this research is to provide this knowledge by utilizing the Advisen cyber loss dataset, which comprises over 137,000 cyber incidents that occurred across various industry sectors from 2013 to 2020. By using text mining techniques, this paper will conduct an exploratory data analysis to identify the most common types of malware, including ransomware. Furthermore, the study will include a likelihood and severity analysis to evaluate the financial impact of these cyberattacks on businesses. Ultimately, this study aims to shed light on the prevalence and financial repercussions of malware incidents and provide businesses with valuable insights to help develop effective cybersecurity strategies

Small Unmanned Aircraft Systems (UAS) for Engineering Inspections and Geospatial Mapping

Small unmanned aircraft systems (UAS) carrying consumer-grade nonmetric cameras are increasingly utilized to generate high-resolution 3D geospatial data. Low cost, ease of operation, widespread availability and low altitude maneuvering capabilities of UAS, as well as the rapid development of technology and methods, make UAS-based photogrammetry applicable to many civil engineering applications such as visualization, 3D mapping, progress monitoring, construction, structural inspection, maintenance, and monitoring. Using computer vision techniques, Structure from Motion (SfM) and Multi-View Stereo (MVS), it is possible to reconstruct 3D scenes from inexpensive, consumer-grade cameras mounted on a UAS. Despite the increasing popularity of UAS, significant research questions remain regarding the accuracy of UAS-based mapping products. In addition, new tools and methods are required to manage and process the vast amount of data generated by UAS. This research explores several novel approaches to address some of these needs in the field of UAS-based photogrammetry. First, a new approach is developed for supplementing the 3D point clouds derived from UAS imagery with thermal infrared (TIR) imagery. Currently, there are several challenges for reconstructing accurate 3D scenes or point clouds solely from overlapping TIR imagery. For instance, consumer-grade TIR cameras have relatively low resolution and a narrow field view; moreover, such cameras usually generate images with blurred edges and textures. An approach is proposed and evaluated for generating 3D TIR-RGB point clouds utilizing coacquired TIR and RGB images. First, a 3D point cloud is generated using the RGB images; afterward, the TIR data is attributed to the 3D point cloud using a boresight technique. Using the proposed approach, dense point clouds are generated that contain both RGB and TIR information. Such an approach can be beneficial for many thermal mapping and inspection projects, including heat loss inspection, non-destructive testing of structures, and electrical parts inspection for power transmission. Second, the research examines the feasibility of utilizing UAS-based photogrammetry for detecting the change of above-ground pipelines. Since it is inexpensive to fly, UAS are ideal for inspecting, monitoring, and detecting changes in the sites over time. In the researched approach, repetitive UAS flights were conducted to derive 3D digital terrain models in order to detect and measure the movement of pipelines in the scene. The results are compared with displacement measurements taken from conventional ground surveys using real-time kinematic (RTK) global navigation satellite system (GNSS) receivers and total stations. Thirdly, this study introduces new dense point cloud quality factors (DPQF) to use as proxy indicators for assessing the accuracy of SfM-MVS dense point clouds. Simulated and empirical experiments are used to assess the accuracy of image-based 3D reconstructed models with respect to different data collection and site condition scenarios. The spatial correlation between the DPQFs and the reconstruction error is investigated and interpreted for multiple experiments. The results of this study show that the DPQF can be a helpful additional field of information for 3D point clouds. Last, this research introduces a new web GIS tool, named BridgeDex, for management of time-aware series of high-resolution bridge inspection images, such as images collected from handheld digital cameras or cameras on UAS. This tool can be used to manage and query bridge inspection images, bridge reports, and other relevant metadata. This web-based prototype provides the user a simple interface for viewing, panning and zooming in and out of bridge imagery collected over the years as a result of numerous bridge inspections. The tool provides the user an intuitive, organized method for evaluating and managing bridge inspection data

Flood Inundation Mapping using HEC-RAS and GIS for Shelby County, Tennessee

Flood zones with 1% and 0.02% of annual flooding chance are projected in the ‎Federal Emergency Management Agency’s (FEMA) digital flood insurance rate maps ‎‎(DFIRMs) and are suited for identifying flood risk at the largest impacts. However, less ‎severe floods, which are not mapped in DFIRMs, still cause significant damage and ‎occur on a more frequent basis. This article employs an easy-to-setup GIS-based ‎solution for rapid inundation mapping of small flood events. The linear interpolation ‎technique (LITE Flood) is developed to rescale the hydraulic behavior inherent with a ‎larger flood event without performing additional hydraulic simulations. The approach is ‎evaluated by comparing the results to the corresponding storm scenarios simulated in ‎the HEC-RAS, a standard river hydraulics simulator. The case study is a portion of the ‎Wolf River and its two main tributaries in Shelby County that is located in the southwest ‎corner of Tennessee, USA, where stream channelization mitigated large flood events but ‎has caused frequent flooding from less severe storms. Results indicate that LITE Flood ‎can be used to delineate more frequent storm events, thereby aiding local community ‎emergency response agencies who often do not have the expertise to perform more ‎sophisticated hydraulic modeling but do have a GIS capacity.

LITE Flood: Simple GIS-Based Mapping Approach for Real-Time Redelineation of Multifrequency Floods

Flood zones with 1 and 0.02% of annual flooding chance are projected in FEMA\u27s digital flood insurance rate maps (DFIRMs) and are suited for identifying flood risk at the largest impacts. However, less severe floods, which are not mapped in DFIRMs, still cause significant damage and occur on a more frequent basis. This article uses a simplified rapid geographic information system (GIS)-based solution for on-the-fly inundation mapping of small flood events. The linear interpolation technique (LITE Flood) was developed to approximate the prone flood zones based on river stage without performing additional hydraulic simulations. The approach was evaluated by comparing the results to the corresponding storm scenarios simulated in a standard river hydraulics simulator. The case study is a portion of Wolf River and its two main tributaries in Shelby County, which is located in the southwest corner of Tennessee. The stream channelization of the lower portion of Wolf River has mitigated large flood events, while causing frequent flooding from less severe storms. LITE Flood produced results with good to acceptable accuracy. LITE Flood can be used for rapid, cost-effective, and real-time mapping of multifrequency floods at a large scale, thereby aiding local community emergency response agencies who often do not have the expertise to perform more sophisticated hydraulic modeling but do have a GIS capacity

The impact of traffic noise on housing values

We study the impact of traffic noise and how it is systematically capitalized into the house value discount. By using the speed and volume values of traffic flows as inputs to the noise prediction model, we created noise nuisance rings using a geographic information system (GIS) for the entire road and rail road transportation system in Shelby County, Tennessee. We found that traffic nuisance, in general, has a significantly negative impact on housing values. The discount on housing values increases in the noise nuisance levels. In addition, the increased intensity of traffic volume within the Memphis Aerotropolis boundary leads to a further decrease in housing values

The impact of traffic noise on housing values

We study the impact of traffic noise and how it is systematically capitalized into the house value discount. By using the speed and volume values of traffic flows as inputs to the noise prediction model, we created noise nuisance rings using a geographic information system (GIS) for the entire road and rail road transportation system in Shelby County, Tennessee. We found that traffic nuisance, in general, has a significantly negative impact on housing values. The discount on housing values increases in the noise nuisance levels. In addition, the increased intensity of traffic volume within the Memphis Aerotropolis boundary leads to a further decrease in housing values