Academic literature on the topic 'Rail area detection'
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Journal articles on the topic "Rail area detection"
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Fang, Zhongbin, Xiaojie Huang, Kangquan Ye, et al. "An algorithm for extracting groove rail area based on improved Hough transform." MATEC Web of Conferences 336 (2021): 02025. http://dx.doi.org/10.1051/matecconf/202133602025.
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In order to improve the accuracy and real-time performance of the automatic cleaning of groove rails in modern trams, this paper proposes a groove rail region extraction algorithm based on improved Hough transform. First, in order to speed up the detection and remove noise, the algorithm performs a series of pre-processing on the images collected by the camera, and then use the Canny edge detection method to extract the edge feature information of the groove rail. Finally, the algorithm is improved on the basis of the traditional Hough transform method according to the actual environment. The algorithm proposes three constraints from the straight line length, the slope of the straight line and the distance between the left and right edges, making the algorithm more feasible and accurate in extracting groove rail area. The extraction accuracy reached 97.9%, and the average extraction speed was 0.1903s, laying the foundation for the automatic cleaning of trough rails of modern trams.
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Choi, Jung-Youl, and Jae-Min Han. "Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects." Applied Sciences 14, no. 5 (2024): 1874. http://dx.doi.org/10.3390/app14051874.
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In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms.
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di Scalea, Francesco Lanza, Ivan Bartoli, Piervincenzo Rizzo, and Mahmood Fateh. "High-Speed Defect Detection in Rails by Noncontact Guided Ultrasonic Testing." Transportation Research Record: Journal of the Transportation Research Board 1916, no. 1 (2005): 66–77. http://dx.doi.org/10.1177/0361198105191600110.
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Recent train accidents have reaffirmed the need to develop rail defect detection systems that are more effective than those used today. This paper proposes new inspection systems for detecting transverse-type cracks in the rail head, notoriously the most dangerous flaws in rails. In principle these systems can be applied to both continuous welded rail and jointed tracks because bidirectional inspection can be implemented. However, the systems may fail to detect defects located close to a joint. The proposed technology uses ultrasonic guided waves that are detected by remote sensors positioned as far away as 76 mm (3 in.) from the top of the rail head. An impulse hammer is used to generate waves below 50 kHz that can successfully detect cracks larger than 15% of the head cross-sectional area. For smaller cracks-those as shallow as 1 mm-a pulsed laser is used for generating waves above 100 kHz. The inspection ranges are at least 10 m (32 ft) for cracks larger than 15% of the head area and at least 500 mm (20 in.) for surface head cracks as shallow as 1 mm. The defect detection reliability is improved by using both reflection and transmission measurements.
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Wang, Zhangyu, Xinkai Wu, Guizhen Yu, and Mingxing Li. "Efficient Rail Area Detection Using Convolutional Neural Network." IEEE Access 6 (2018): 77656–64. http://dx.doi.org/10.1109/access.2018.2883704.
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Tverdomed, Volodymyr, Anatoliy Gorban, and Lesia Kushmar. "Image segmentation method of rail head defects and area measurement of selected segments." MATEC Web of Conferences 390 (2024): 04008. http://dx.doi.org/10.1051/matecconf/202439004008.
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The operation safety of railway transport, which is the most important economic and social factor, is largely determined by the technical condition of the rail track and measures to maintain the quality of its track management system. One of the system elements for ensuring the accident-free operation of the track is the technical diagnosis of rails using a method complex of non-destructive control of rails, such as acoustic (ultrasonic), magnetic, combined, etc., and monitoring of the track using methods of measuring the geometry of the rail track and its disturbances. When the wheel interacts with the rail, especially on high-speed and load-stressed sections, defects and damage inevitably occur in the rails. A rather large share of such defects are on the rolling surface of the rail head. Formed defects develop rapidly, which seriously complicates the safety of train traffic. Therefore, accurate and quick detection of defects on the rolling surface of the rail head is very important. However, it is quite difficult to detect defects on the rolling surface of the rail by the acoustic (ultrasound) method due to the violation of tight contact between the rolling surface of the rail head and the piezoelectric transducer. In this case, it is quite convenient to detect surface defects of the rail head using video control. The article provides a comparative analysis of segmentation methods. There has been presented the method of image segmentation of main rail defects based on general contour preparation and parallel-hierarchical (PH) transformation using their classification. The parallel-hierarchical transformation method allows to increase the segmentation accuracy of individual areas in the original image compared to similar ones. The algorithm of pyramidal generalized-contour preparation and the criterion system allows, by calculating the threshold for each level of the gray scale, to present the study of the image with the corresponding contour preparations at the segmentation level. Modeling of recursive generalized-contour preparation and PH transformation method for image segmentation problem of rail head defects shows that, compared to the segmentation method based on the increase of areas, the accuracy of image segmentation is better. A modified method of calculating the image contour area based on the coding of lines forming the boundaries of the black and white areas of the two-gradation image has been given.
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Li, Liming, Rui Sun, Shuguang Zhao, Xiaodong Chai, Shubin Zheng, and Ruichao Shen. "Semantic-Segmentation-Based Rail Fastener State Recognition Algorithm." Mathematical Problems in Engineering 2021 (March 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/8956164.
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Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects.
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Shen, Tuo, Jinhuang Zhou, Tengfei Yuan, Yuanxiang Xie, and Xuanxiong Zhang. "LiDAR-Based Urban Three-Dimensional Rail Area Extraction for Improved Train Collision Warnings." Sensors 24, no. 15 (2024): 4963. http://dx.doi.org/10.3390/s24154963.
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The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms.
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Cao, Jinghao, Yang Li, and Sidan Du. "Robust Artificial Intelligence-Aided Multimodal Rail-Obstacle Detection Method by Rail Track Topology Reconstruction." Applied Sciences 14, no. 7 (2024): 2795. http://dx.doi.org/10.3390/app14072795.
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Detecting obstacles in the rail track area is crucial for ensuring the safe operation of trains. However, this task presents numerous challenges, including the diverse nature of intrusions, and the complexity of the driving environment. This paper presents a multimodal fusion rail-obstacle detection approach by key points processing and rail track topology reconstruction. The core idea is to leverage the rich semantic information provided by images to design algorithms for reconstructing the topological structure of railway tracks. Additionally, it combines the effective geometric information provided by LiDAR to accurately locate the railway tracks in space and to filter out intrusions within the track area. Experimental results demonstrate that our method outperforms other approaches with a longer effective working distance and superior accuracy. Furthermore, our post-processing method exhibits robustness even under extreme weather conditions.
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Zheng, Danyang, Liming Li, Shubin Zheng, et al. "A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2021 (July 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/2565500.
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As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.
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Signore, James M., Mohamed G. Abdel-Maksoud, and Barry J. Dempsey. "Fiber-Optic Sensing Technology for Rail-Buckling Detection." Transportation Research Record: Journal of the Transportation Research Board 1584, no. 1 (1997): 41–45. http://dx.doi.org/10.3141/1584-06.
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Buckling and cracking of steel rails is a contributing factor in accidents on railroads today. Detection and notification of buckled track sections before a train reaches these locations will significantly increase rail safety. A fiber-optic-based sensing system, with the fiber affixed to a beam, was developed and evaluated to detect buckled regions. The purpose of this research is to evaluate the sensitivity of the fiber-optic sensing system to buckling of a long structural member. Numerous facets of fiber-optic sensing have been explored. Fiber-to-steel bonding techniques were examined and tested. Full-scale laboratory testing was conducted by elastically buckling a 24.4-m-long (80-ft) wide-flange section with hydraulic rams. Typical measurement accuracy within 10 percent of theoretical predictions was achieved by optical time domain reflectometry techniques. For field testing, however, a more robust solution is sought and is currently under development. It is suggested that a lower-cost fiber break or bend detector is a suitable option. The optical fiber will break or bend at the location of rail elongation in the buckled area, allowing the detection equipment to locate the buckled area.
Dissertations / Theses on the topic "Rail area detection"
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Shrestha, Survesh Bahadur. "Defect Detection on Rail Base Area Using Infrared Thermography." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2758.
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This research aims to investigate the application of infrared thermography (IRT) as a method of nondestructive evaluation (NDE) for the detection of defects in the rail base area. Rails have to withstand harsh conditions during their application. Therefore, defects can develop in the base area of rails due to stresses such as bending, shear, contact, and thermal stresses, fatigue, and corrosion. Such defects can cause catastrophic failures in the rails, ultimately leading to train derailments. Rail base defects due to fatigue and corrosion are difficult to detect and currently there are no reliable or practical non-destructive evaluation (NDE) methods for finding these types of defects in the revenue service. Transportation Technology Center, Inc. (TTCI) had previously conducted a research on the capability of flash IRT to detect defects in rail base area based on simulation approach. The research covered in this thesis is the continuation of the same project.In this research, three rail samples were prepared with each containing a notched-edge, side-drilled holes (SDHs), and bottom-drilled holes (BDHs). Two steel sample blocks containing BDHs and SDHs of different sizes and depths were also prepared. Preliminary IRT trials were conducted on the steel samples to obtain an optimal IRT setup configuration. The initial inspections for one of the steel samples were outsourced to Thermal Wave Imaging (TWI) where they employed Thermographic Signal Reconstruction (TSR) technique to enhance the resulting images. Additional inspections of the steel samples were performed in the Southern Illinois University-Carbondale (SIUC) facility. In case of the rail samples, the SDHs and the notched-edge reflectors could not be detected in any of the experimental trials performed in this research. In addition, two more rail samples containing BDHs were prepared to investigate the detection capabilities for three different surface conditions: painted, unpainted, and rusted. The painted surface provided a best-case scenario for inspections while the other conditions offered further insight on correlating the application to industry-like cases.A 1300 W halogen lamp was employed as the heat source for providing continuous thermal excitation for various durations. Post-processing and analysis of the resulting thermal images was performed within the acquisition software using built-in analysis tools such as temperature probes, Region of Interest (ROI) based intensity profiles, and smoothing filters. The minimum defect diameter to depth (aspect) ratio detected in preliminary trials for the steel sample blocks were 1.0 at a diameter of 4.7625 mm (0.1875 in) and 1.5 at a diameter of 3.175 mm (0.125 in). For the inspection of painted rail sample, the longest exposure times (10 sec) provided the best detection capabilities in all sets of trials. The three holes having aspect ratio greater or equal to 1.0 were indicated in the thermal response of the painted and rusted samples while only the two holes having aspect ratio greater or equal to 1.5 were indicated in the unaltered sample. Indications of reflectors were identified through qualitative graphical analysis of pixel intensity distributions obtained along a bending line profile. The results obtained from the painted sample provided a baseline for analyzing the results from the unpainted and rusted rail samples. This provided an insight on the limitations and requirements for future development. The primary takeaway is the need for an optimized heat source. Poor contrast in the resulting image for the unpainted and rusted rail samples is experienced due to both noise and lack of penetration of the heat energy. This could have been due to decreased emissivity values. Moreover, the excitation method employed in this research does not comply with current industry standards for track clearances. Therefore, exploration of alternative excitation methods is recommended.
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Glisson, Wesley J., Courtney J. Conway, Christopher P. Nadeau, and Kathi L. Borgmann. "Habitat models to predict wetland bird occupancy influenced by scale, anthropogenic disturbance, and imperfect detection." WILEY, 2017. http://hdl.handle.net/10150/625200.
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Understanding species-habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation data influenced model performance. These models identified associations between environmental variables and occupancy. We used bird survey and spatial environmental data at 2473 locations throughout the species' U.S. range to create and validate occupancy models and produce predictive maps of occupancy. We compared habitat-based models at three spatial scales (100, 224, and 500 m radii buffers) with and without anthropogenic disturbance variables using validation data adjusted for imperfect detection and an unadjusted validation dataset that ignored imperfect detection. The inclusion of anthropogenic disturbance variables improved the performance of habitat models at all three spatial scales, and the 224-m-scale model performed best. All models exhibited greater predictive ability when imperfect detection was incorporated into validation data. Yuma Ridgway's rail occupancy was negatively associated with ephemeral and slow-moving riverine features and high-intensity anthropogenic development, and positively associated with emergent vegetation, agriculture, and low-intensity development. Our modeling approach accounts for common limitations in modeling species-habitat relationships and creating predictive maps of occupancy probability and, therefore, provides a useful framework for other species.
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Chiang, Lee-Chen, and 姜禮城. "Ⅰ. Determination of the Chemical Oxgen Demand. Ⅱ. Determination the Efficiency of Detection Chemical Components and Physical Components for the Rainwater in Jong-Lih Area by Acid rain Automatic Monitoring." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/85025288337277938831.
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碩士<br>淡江大學<br>水資源及環境工程學系<br>87<br>Ⅰ. Determination of the Chemical Oxgen Demand in the Wastewater Using Microwave Energy — The Comparison Between the Closed Method And the Open Focus Reflux Method.
In environmental analytic chemistry microwave-assisted methods for sample preparation have been increased rapidly in recent years .The closed vessel method and open focus reflux method were used for the determination of chemical oxygen demand (COD) in the digestion-oxidation step which was by using microwave energy.
The goals of this study are to develop the mercury-free microwave assisted,digestion for COD determination and to compare between the open focus microwave and the closed vessel sample preparation methods.
The optimized conditions for the closed vessel method are： sample, 5ml； sulfuric acid(0.55﹪Ag+), 7ml；0.0705M silver nitrate,1ml； 0.0417M potassium dichromate, 2ml； power(using domestic microwave oven): 512 W, reaction time: 5mins. The optimized conditions for the open focus reflux method are： sample, 5ml； sulfuric acid(0.55﹪Ag+), 7ml；0.0705M silver nitrate,1ml； 0.0417M potassium dichromate, 2ml； power(using open focus microwave oven): 300W, reaction time: 5mins. The average recovery of the closed vessel method for quality control samples were 98.3﹪and S. D. 1.8﹪.The average recovery of the open focus reflux method for quality control samples was 98.7﹪and S. D. 1.2﹪The ratio between COD values, determined by the open focus method and the closed vessel methods is 0.9360. The results show that both methods are comparable.
Ⅱ. Determination the Efficiency of Detection Chemical and Physical Components for the Rainwater in Chung-Li Area by Acid rain Automatic Monitoring.
The aims of this study are to characterize the rainfalls by physical and chemical parameter Chung-Li area and to compare the data from acid rain monitor with the results obtained in the laboratory.
The physical and chemical parameters included temperature, rainfalls intensity, electrical conductivity, turbidity, pH value cation concentration (Na+,K+,NH4+,Ca2+,Mg2+) and anion concentration( Cl-,NO3-,SO42-).The results showed that the pH values, temperature, rainfalls intensity and electrical conductivity were very good as compared with that from the lab. However, the sulfate and nitrate by acid rain monitor, s spectrophotometer were poor probably because of bubbles and contamination in the line .
Ion chromatography with conductivity detector has been used to determine cations and anions separately. The nitric acid solution(0.5mM) was used with cation-exchange column (MCI GEL SCK01) having low cation exchange capacities. The dilute 3mM Vanillin Acid and 2.8mM N-Methydiethanolamine(pH6.2) have been the main eluent species used for anions.
The result shows the range of ratios (SO42 /NO3-)was 1~5 and the range of pH values were 4~7 for the rainwater in Chung-Li area.
Books on the topic "Rail area detection"
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Office, General Accounting. Air pollution: Improvements needed in detecting and preventing violations : report to the chairman, Subcommittee on Oversight and Investigations, Committee on Energy and Commerce, House of Representatives. GAO, 1990.
Find full textBook chapters on the topic "Rail area detection"
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Migliaccio, M., F. Nunziata, A. Marino, C. Brekke, and S. Skrunes. "Ocean Applications." In Polarimetric Synthetic Aperture Radar. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56504-6_6.
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AbstractIn this chapter, the most promising techniques to observe oil slicks and to detect metallic targets at sea using polarimetric synthetic aperture radar (SAR) data are reviewed and critically analysed. The detection of oil slicks in SAR data is made difficult not only by the presence of speckle but also by the presence of, e.g. biogenic films, low-wind areas, rain cells, currents, etc., which increase the false alarm probability. The use of polarimetric features has been shown to both observe oil slicks and distinguish them from weak-damping look-alikes but also to extract some of their properties. Similarly to oil slicks, the same factors can hamper the detection of metallic targets at sea. The radiometric information provided by traditional single-channel SAR is not generally sufficient to unambiguously detect man-made metallic targets over the sea surface. This shortcoming is overcome by employing polarimetry, which allows to fully characterize the scattering mechanism of such targets.
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Sinatra, Tiin, Edy Maryadi, Syahrul, Syukri Darmawan, Ginaldi Ari Nugroho, and Asif Awaludin. "Evaluation of Detecting and Tracking Algorithms of Reflectivity Area Based on Rain Scanner Observation Data." In Springer Proceedings in Physics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9768-6_74.
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Ramos Helena G., Rocha Tiago J., Pasadas Dário J., and Ribeiro A. Lopes. "Defect Detection with Velocity Induced Eddy Currents Using a Permanent Magnet." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-407-7-263.
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Rail flaw detection is at present an important research and development area. Technological developments augmented the circulating speed of trains, improved their comfort and increased the use of railway transportation in Europe making the safety of railway networks a paramount issue. One item associated with today's rail track assessment is inspection speed. This paper presents a contribution to attain a solution for this problem. It proposes a testing method whose sensitivity increases with the moving speed of the probe in relation with the material to be tested.
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Malinskiy, S. V., A. V. Abramov, and V. O. Sharova. "AUTOMATIC DETERMINATION OF DANGEROUS RAILWAY SECTIONS BOUNDARIES." In Intelligent Transportation Systems. FSBEO HPE Moscow State University of Railway Engineering (MIIT), 2025. https://doi.org/10.30932/9785002587582-2025-624-630.
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The article considers the solution of the problem of automatic detection of boundaries of dangerous sections of a railway track using the cumulative sum algorithm when modeling the geometry of a rail track using AR and ARMA models. The parameters of the algorithm for its application in detecting the moment of change in the properties of vertical irregularities are determined.
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V. Ngo, Binh, and Ya-Fu Lee. "Ecology of the Granular Spiny Frog Quasipaa verrucospinosa (Amphibia: Anura - Dicroglossidae) in Central Vietnam." In Protected Area Management - Recent Advances. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99656.
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We conducted a large-scale assessment at 35 primary forest sites and 42 secondary forest sites in Bach Ma National Park, central Vietnam, using the detection/non-detection data for each site over multiple visits, to quantify the site proportions that were occupied by granular spiny frogs (Quasipaa verrucospinosa). We additionally investigated the effect of site covariates (primary versus secondary forests) and sample covariates (temperature, humidity, and precipitation) to examine the environmental needs that may be incorporated for conserving rain forest amphibians in Vietnam. From the best model among all candidate models, We estimated a site occupancy probability of 0.632 that was higher than the naïve occupancy estimate of 0.403 and a 57% increase over the proportion of sites at which frogs were actually observed. The primary forest variable was an important determinant of site occupancy, whereas occupancy was not associated with the variable of secondary forest. In a combined AIC model weight, the detection model p (temperature, humidity, precipitation) included 90.9% of the total weight, providing clear evidence that environmental conditions were important sample covariates in modeling detection probabilities of granular spiny frogs. Our results substantiate the importance of incorporating occupancy and detection probabilities into studies of habitat relationships and suggest that the primary forest factor associated with environmental conditions influence the occupancy of granular spiny frogs.
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Kohli, Shruti. "Exploring Cyber Security Vulnerabilities in the Age of IoT." In Detecting and Mitigating Robotic Cyber Security Risks. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2154-9.ch014.
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The modernization of rail control systems has resulted in an increasing reliance on digital technology and increased the potential for security breaches and cyber-attacks. Higher-level European Train Control System(ETCS) systems in particular depend on communications technologies to enable greater automation of railway operations, and this has made the protecting the integrity of infrastructure, rolling stock, staff and passengers against cyber-attacks ever more crucial. The growth in Internet of Things (IoT) technology has also increased the potential risks in this area, bringing with it the potential for huge numbers of low-cost sensing devices from smaller manufacturers to be installed and used dynamically in large infrastructure systems; systems that previously relied on closed networks and known asset identifiers for protection against cyber-attacks. This chapter demonstrates that how existing data resources that are readily available to the railways could be rapidly combined and mapped to physical assets. This work contributes for developing secure reusable scalable framework for enhancing cyber security of rail assets
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Jia, Qiang, Feng Han, Yuan Li, Miaomiao Qi, and Zhibo Liu. "Track Damage Recognition Algorithm Based on Improved Convolution." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200232.
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Railway inspection and maintenance in uninhabited high-cold areas are facing great challenges according to the rapid development of Sino-Russian railway. In this paper, an algorithm is proposed, which is image edge detection track structure recognition and damage detection based on improved convolution model. This paper use image acquisition method and improved two-dimensional convolution to image filtering, thus the original image matrix is processed by determinant transformation to enhance image boundary elements. On the basis of the linear characteristics obtained from edge detection, the fastener cartridge and steel rails are identified. The damage and position of steel rails are judged by the sharp change of alignment. The image recognition and verification of the existing railway demonstrates that the method has the following advantages: It improves the rate of recognition structure and has certain adaptability. At the same time, the corresponding position of the structure can be determined, which is beneficial to the identification of structural damage. Besides, it plays an important role in the daily operation and maintenance of track.
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N. Ramos-Bernal, Rocío, René Vázquez-Jiménez, Sulpicio Sánchez Tizapa, and Roberto Arroyo Matus. "Characterization of Susceptible Landslide Zones by an Accumulated Index." In Landslides - Investigation and Monitoring. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.89828.
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In order to characterize the landslide susceptibility in the central zone of Guerrero State in Mexico, a spatial model has been designed and implemented, which automatically generates cartography. Conditioning factors as geomorphological, geological, and anthropic variables were considered, and as a detonating factor, the effect of the accumulated rain. The use of an inventory map of landslides that occurred in the past (IL) was also necessary, which was produced by an unsupervised detection method. Before the design of the model, an analysis of the contribution of each factor, related to the landslide inventory map, was performed by the Jackknife test. The designed model consists of a susceptibility index (SI) calculated pixel by pixel by the accumulation of the individual contribution of each factor, and the final index allows the susceptibility cartography to slide in the study area. The evaluation of the obtained map was performed by applying an analysis of the frequency ratio (FR) graphic, and an analysis of the receiver operating characteristic (ROC) curve was developed. Studies like this can help different safeguarding institutions, locating the areas where there is a greater vulnerability according to the considered factors, and integrating disaster attention management or prevention plans.
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Jepri Sutanto, Ratih Luhuring Tyas, Desi Listianti, and Joko Triyanto. "Reliability Of of Radiation Portal Monitor Using False Alarm Testing." In Prosiding Seminar Nasional Aplikasi Isotop dan Radiasi 2021: Peran Isotop dan Radiasi untuk Indonesia yang Berdaya Saing. Penerbit BRIN, 2023. http://dx.doi.org/10.55981/brin.690.c659.
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As an effort to improve security and safety in the nuclear facility area, The Center for Nuclear Facility Engineering, National Nuclear Energy Agency of Indonesia (PRFN-BATAN) has developed a system for measuring Radiation Portal Monitor (RPM). This facility will be installed at the entrance and exit gates in the nuclear facility area which aims to monitor the safety of radioactive substances in vehicles entering and leaving the nuclear facility area. To ensure the reliability of the radiation portal monitor that has been made, testing and certification of the equipment must be carried out in accordance with the reference standard SNI IEC 62244:2016. One of the radiation characteristics requirements of the standard is to perform a false alarm test with the requirement that no unexplained alarms occur during a 100 hours operating period with the monitor in a stable background. Testing activity was carried out in the Serpong Nuclear Area - National Nuclear Energy Agency of Indonesia in the behind of Building 71. The test is carried out for 5 days by monitoring the detection of false alarms at RPM which is operated for 100 hours. The results of background testing for 5 days obtained an average count of 745 cps. However, no alarm data is activated (background or occupation), there is an increase in the count due to rain, but it does not activate the alarm.
Conference papers on the topic "Rail area detection"
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Beggs, Joel, and John H. Fitzgerald. "Preparing Gas Distribution Piping for the Construction of a Light Rail Transit System." In CORROSION 2003. NACE International, 2003. https://doi.org/10.5006/c2003-03711.
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Abstract The light rail transit (LRT) system in Minneapolis, Minnesota is being constructed in response to the need to ease freeway congestion and move more people. To prepare the gas distribution piping for detection, monitoring and control of any stray current that might occur, the gas company has isolated the piping and installed various testing and measurement facilities. Stray current provisions include buried permanent reference electrodes in strategic locations, current measuring hoops on the piping to detect magnitude and direction of current, special piping construction for pipe crossing the tracks in the downtown area and judicious use of electrical isolating joints in various lines. Baseline surveys and future testing programs are being developed. This paper describes the LRT system and the preparations being made on the gas distribution piping.
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Schütte, Jörg, and Sven Scholz. "Recent Experience on Guideway Intrusion Detection System Implementations." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2334.
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During the last decade, several Public Transportation Operators have started to test and implement technical equipment that shall detect passengers or objects intruding the clearance profile of trains in the station area. The deployed or tested systems typically supervise the track area of subways or light rails and shall stop incoming trains if an obstacle has been detected on the tracks (Guideway Intrusion Detection System, GIDS). While some scientific and operational publications can be found in the literature for Onboard Obstacle Detection Systems and on different technical solutions for Guideway Intrusion in general, there are virtually none on planning and operations experience for Wayside GIDS in transit applications. The paper therefore reports on the basic intention and operation of (wayside) GIDS in Unmanned and Driver Operations and findings about detection efficiency and feasibility. Some critical issues had been observed in most implementations and will be discussed in this overview paper. TelSys GmbH has been working with researchers from TU Dresden on Video GIDS Technology in multiple installations, accumulated substantial statistics of GIDS behavior in diverse field demonstrators (Prague, Berlin, New York City, Munich) and assessed the operational results of GIDS technologies over the years. The Video GIDS is therefore introduced as an illustration example, while the considered aspects are applicable to all GIDS Technologies.
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Lin, Tu-Huan, Yu-Lin Shih, Jyh-Cherng Jong, and Chian-Shan Suen. "Development of an Intelligent Module Used in Railway Level Crossings to Solve the Safety and Punctuality Issues in Urban Area." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2220.
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In urban area, frequent train services during peak hours often keep interrupting roadway traffic approaching level crossings (LXs). In Taiwan, more than 24 trains per hour pass the LXs in peak hours, and it leads to more than 40% time blocked by the barriers. To mitigate the potential risk of accidents between trains and roadway vehicles, almost 20% LXs had been installed with obstacle detection devices. It, however, leads to the following issues: (1) when the level crossings open and then close within a short period of time, the vehicles would likely be trapped in the dangerous area; (2) if the closing time at level crossings is not adjusted effectively, it would seriously impact the traffic flow and increase the possibility of trespassing; (3) the false alarm triggered by the obstacle detection devices has serious impact on train punctuality. To mitigate the influence on roadside traffic and improve the safety, this study developed an intelligent module which consists of the following functions: (1) transmitting real-time videos of level crossings to the approaching trains, (2) constant alarm time for trains with different approaching speeds, and (3) extending alarm time for trains from the opposite directions within short time periods. A level crossing located on curved line in urban area was selected to test the performance of these functions in an actual scenario. Alternative solutions were also adopted in the tests to compare the performance for reference. The results show that the module can be viable in practice, but needs further works to ensure the safety, such as developing a fail-safe mechanism and overcoming the software security issues.
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Frison, Benjamin, and David Y. Jeong. "Rail Integrity Experience on the Washington Metro System." In 2016 Joint Rail Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/jrc2016-5710.
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The Washington Metropolitan Area Transit Authority (WMATA) provides passenger rail service to the nation’s capital. Although the rail system carries only passenger trains, the rail integrity issues that WMATA must manage are similar to those that freight railroads also face. These issues include occurrences of broken rail from internal rail head defects, detection of such defects, and repair of the rail to restore service. Another example is the development of damage on the running surface of the rail, called rolling contact fatigue (RCF). Such surface damage is known to adversely affect the detection of internal rail head defects beneath RCF conditions. While WMATA’s rail integrity issues may be similar to those that freight railroads also encounter, the management of such issues are different, which are also discussed in this paper. This paper describes the recent experience of broken rails on the WMATA rail system. In addition, results from engineering fracture mechanics analyses are presented to help understand how operational, environmental, design, and maintenance factors influence rail failure.
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Hu, Ping, Joseph A. Turner, Constantine Tarawneh, Brent Wilson, and Allen J. Fuller. "Multiple Frequency Ultrasonic Detection of Subsurface Near-Race Inclusions for Improved Fatigue Life Performance." In 2015 Joint Rail Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/jrc2015-5785.
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The importance of steel cleanliness for the performance of tapered roller bearings has been clearly established and has led to on-going improvements in steel production methods. The presence of non-metallic impurities within the steel can result in hard/brittle inclusions that may serve as initiation sites for damage due to sub-surface rolling contact fatigue (RCF) if the inclusions lie within the near-race of the bearing components due to the high mechanical stress present. Current inspection standards define steel cleanliness with respect to bulk inclusion morphology, which limits inspection to a small area that may or may not be representative of the entire steel heat. In this presentation, ultrasonic surface wave methods are described for detecting subsurface inclusions directly on finished bearing inner and outer rings. We expand on our previous work to exploit the different inspection depths that can be achieved with different measurement frequencies. The impact of the different inspection depths is quantified through simulated service life testing with heavy axle loading conditions. For this study, bearing components were first subjected to ultrasonic surface wave testing at three different frequencies to identify near-race inclusions. The simulated service life testing was then used to assess the onset and propagation of RCF failure. RCF spall initiations correlated highly with the positions identified by the ultrasonic inspections suggesting that this approach has a predictive potential. However, additional research is needed to establish the specific criteria needed for such predictions with respect to the inclusion location along the race, the depth from the race surface, the inclusion morphology and the inclusion mechanical properties. This work is anticipated to improve the understanding of RCF damage initiation which will lead a higher level of safety for railroad operations.
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De Los Santos, Nancy, Robert Jones, Constantine M. Tarawneh, Arturo Fuentes, and Anthony Villarreal. "Development of Prognostic Techniques for Surface Defect Growth in Railroad Bearing Rolling Elements." In 2017 Joint Rail Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/jrc2017-2262.
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Prevention of bearing failures which may lead to catastrophic derailment is a major safety concern for the railroad industry. Advances in bearing condition monitoring hold the promise of early detection of bearing defects, which will improve system reliability by permitting early replacement of failing components. However, to minimize disruption to operations while providing the maximum level of accident prevention that early detection affords, it will be necessary to understand the defect growth process and try to quantify the growth speed to permit economical, non-disruptive replacement of failing components rather than relying on immediate removal upon detection. The study presented here investigates the correlation between the rate of surface defect (i.e. spall) growth per mile of full-load operation and the size of the defects. The data used for this study was acquired from defective bearings that were run under various load and speed conditions utilizing specialized railroad bearing dynamic test rigs operated by the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV). Periodic removal and disassembly of the railroad bearings was carried out for inspection and defect size measurement and documentation. Castings were made of spalls using low-melting, zero shrinkage Bismuth-based alloys so that a permanent record of the full spall geometry could be retained. Spalls were measured using optical techniques coupled with digital image analysis and also with a manual coordinate measuring instrument with the resulting field of points manipulated in MatLab™ and Solidworks™. The spall growth rate in area per mile of full-load operation was determined and, when plotted versus spall area, clear trends emerge. Initial spall size is randomly distributed as it depends on originating defect depth, size, and location on the rolling raceway. The growth of surface spalls is characterized by two growth regimes with an initial slower growth rate which then accelerates when spalls reach a critical size. Scatter is significant but upper and lower bounds for spall growth rates are proposed and the critical dimension for transition to rapid spall growth is estimated. The main result of this study is a preliminary model for spall growth which can be coupled to bearing condition monitoring tools to permit economical scheduling of bearing replacement after the initial detection of spalls.
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Thurston, David F. "Highway Crossing Control Using PTC Technology." In 2013 Joint Rail Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/jrc2013-2493.
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Positive Train Control (PTC) was mandated by Congress in 2008 to be installed on an extensive network of rail carriers within the United States before the end of 2015. As such, the short time frame for implementation has dictated that several key features that could be adopted within the train control area not be considered until the base development is finished. One of the areas that has been pushed off until later is the interface to Highway crossings. This is being explored in part by Caltrain in their Communications Based Overlay Signal System (CBOSS) project that adapts Incremental Train Control System (ITCS) protocols; however this approach does not undertake the full replacement of existing technology. This paper investigates the concept of operations for PTC controlled highway crossings with the premise that the existing train detection technology will be fully replaced by PTC and its train detection system. This will allow for much reduced hardware in the field while potentially adding additional functionality. Detailed analysis of the safety case will be introduced and interface logic will be included. Additional functions such as prediction, preemption, near side control and switching scenarios will be included in the research for both freight and passenger operations and how these situations can be addressed with available PTC technology.
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Ding, Yi, and Li R. Cheng. "High Speed Overload and Imbalance Load System in China." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6223.
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An Overload and Imbalance Load Detection (OID) system monitors rolling stock on the railroad tracks, and identifies and alarms on improperly loaded vehicles. In China, OID systems have been installed near train stations close to the railroad signals and curves. The location and condition of the track in the area of the OID system can affect the weighing performance. In recent years, the dedicated high-speed passenger lines have freed up a great deal of train capacity on the freight tracks, enabling freight train speed to increase from 60km/h to 80∼120km/h. It has become necessary to install OID systems on freight mainline between stations. The authors of this paper discuss the prospect and hurdles of two OID technologies on China railways.
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De Los Santos, Nancy, Constantine M. Tarawneh, Robert E. Jones, and Arturo Fuentes. "Defect Prognostics Models for Spall Growth in Railroad Bearing Rolling Elements." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6214.
Full textAbstract:
Prevention of railroad bearing failures, which may lead to catastrophic derailments, is a central safety concern. Early detection of railway component defects, specifically bearing spalls, will improve overall system reliability by allowing proactive maintenance cycles rather than costly reactive replacement of failing components. A bearing health monitoring system will provide timely detection of flaws. However, absent a well verified model for defect propagation, detection can only be used to trigger an immediate component replacement. The development of such a model requires that the spall growth process be mapped out by accumulating associated signals generated by various size spalls. The addition of this information to an integrated health monitoring system will minimize operation disruption and maintain maximum accident prevention standards enabling timely and economical replacements of failing components. An earlier study done by the authors focused on bearing outer ring (cup) raceway defects. The developed model predicts that any cup raceway surface defect (i.e. spall) once reaching a critical size (spall area) will grow according to a linear correlation with mileage. The work presented here investigates spall growth within the inner rings (cones) of railroad bearings as a function of mileage. The data for this study were acquired from defective bearings that were run under various load and speed conditions utilizing specialized railroad bearing dynamic test rigs owned by the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV). The experimental process is based on a testing cycle that allows continuous growth of railroad bearing defects until one of two conditions are met; either the defect is allowed to grow to a size that does not jeopardize the safe operation of the test rig, or the change in area of the spall is less than 10% of its previous size prior to the start of testing. The initial spall size is randomly distributed as it depends on the originating defect depth, size, and location on the rolling raceway. Periodic removal and disassembly of the railroad bearings was carried out for inspection and defect size measurement along with detailed documentation. Spalls were measured using optical techniques coupled with digital image analysis, as well as, with a manual coordinate measuring instrument with the resulting field of points manipulated in MatLab™. Castings were made of spalls using low-melting, zero-shrinkage bismuth-based alloys, so that a permanent record of the spall geometry and its growth history can be retained. The main result of this study is a preliminary model for spall growth, which can be coupled with bearing condition monitoring tools that will allow economical and effective scheduling of proactive maintenance cycles that aim to mitigate derailments, and reduce unnecessary train stoppages and associated costly delays on busy railways.
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Fukuoka, Tomotaka, Takahiro Minami, Makoto Fujiu, and Junichi Takayama. "Study of filming condition for deep learning based crack detection method." In 6th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2021. http://dx.doi.org/10.5592/co/cetra.2020.1059.
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Recently, the study of extending the service life of bridges has gained attention. In Japan, there are about 730,000 bridges with a length of 2 m or longer, and many of these were built during a period of high economic growth, and have now reached the end of their service life. Therefore, their rebuilding and the extension of their service life must be considered. However, some local public organizations have problems that insufficient manpower relative to the number of bridges to manage, as well as insufficient funding for maintenance. Thus, these organizations are unable to perform routine close visual inspections. Specific problems include “notably less staff and consulting technicians relative to the number of bridges to be managed” and “high inspection cost preventing from funding for repair.” As issues with the continuing close visual inspection of bridges are surfacing, the remote imaging system is expected to become a new inspection method that replaces close visual inspection. The practical potential of bridge inspections using images captured with a super-high-resolution camera was examined. A super-high-resolution camera enables us to take a wide area picture of a target bridge from a long distance. An image processing method could improve the efficiency of image-based inspection method. For example, a deep learning-based image processing method could extract a damaged area on a surface of a bridge automatically with high accuracy faster than human inspection. In general, the accuracy of an image processing method is affected by the quality of an input image. Filming conditions are one of the factors that determine the quality of a photo image. It is important to evaluate the effect of filming conditions to improve the reliability of an image processing method. In this paper, we evaluate the effect of the filming conditions for an image processing method by comparing the results of a deep learning-based crack detection method.
Reports on the topic "Rail area detection"
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Butler, Christopher, and Tabitha Olsen. Inventory of eastern black rail (Laterallus jamaicensis) to Inform Padre Island National Seashore prescribed fire management. National Park Service, 2025. https://doi.org/10.36967/2311590.
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The Eastern Black Rail (Laterallus jamaicensis jamaicensis) is an elusive, federally threatened marsh bird with declining populations, making effective monitoring and habitat management critical. This study investigated Black Rail occupancy and habitat characteristics at Padre Island National Seashore (PAIS) using a combination of call-playback surveys, passive acoustic monitoring, trail cameras, and vegetation sampling. Over a survey season from March to July 2024, call-playback surveys detected Black Rails at eight locations, primarily in Upper Bird Island Basin, although Eastern Black Rails were also detected at Bird Island Basin and the North Shooting Range units. Both detectability and occupancy were low, with a detection probability of only 8.8% and with only 10.6% of apparently suitable habitat occupied. Passive acoustic monitoring and trail cameras proved less effective at documenting Eastern Black Rail presence than call-playback surveys, with only one Black Rail detection via autonomous recording units (ARUs) and no detections using trail cameras. Vegetation sampling revealed that survey points were dominated by emergent vegetation, with limited coverage by shrubs, saltgrass (Distichlis spicata), and cordgrass (Spartina spp.). The above-ground biomass (AGB) of grassy and herbaceous vegetation has been positively correlated with occupancy at other sites and the relatively low AGB observed at sites on Padre Island National Seashore may help explain the limited occupancy (only 10.6% of apparently suitable habitat) observed in this study. Black Rails typically avoid areas of extensive woody vegetation, and prescribed fire can be very effective at reducing woody vegetation encroachment. Consequently, prescribed fire is likely an effective way to manage units for continued Black Rail persistence. However, adjusting the interval between burns may be required to allow AGB to increase to a higher level in order to increase occupancy.
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Paxton, Barton, and Chance Hines. Black rail inventory at Cape Lookout and Cape Hatteras national seashores. National Park Service, 2024. http://dx.doi.org/10.36967/2304485.
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The black rail (Laterallus jamaicensis) is the most secretive of the secretive marsh birds and one of the least understood species in North America. On the east coast, eastern black rails historically bred in tidal and freshwater marshes along the Atlantic coast from Massachusetts, south to Florida. Within the mid-Atlantic region suitable black rail habitat is concentrated in the high marsh along the upper elevational zone of salt marshes. This zone is dominated by salt meadow hay (Spartina patens), saltgrass (Distichlis spicata), and is often interspersed with shrubs such as marsh elder (Iva frutescens) or saltbush (Baccharis hamilifolia). North Carolina has been a stronghold for eastern black rails within the mid-Atlantic region, with the marsh complexes associated with the lower Pamlico sound supporting one of largest concentrations and highest densities of eastern black rails throughout their range. However, even within these marshes, eastern black rail populations have experienced declines marked by reductions in occupied sites and decline in numbers within historic strongholds. Evidenced by increasing confinement to the highest portions of the high marsh in recent years, sea-level rise and increased rates of high marsh inundation are likely a major contributing factor to declines. With the population of eastern black rails declining over 75% in the last 10-20 years, the U. S. Fish and Wildlife Service formally listed the eastern black rail as threatened under the endangered species act on 9 November 2020 (USFWS 2020). To fulfill the need for information to guide management decisions on projects at Cape Lookout and Cape Hatteras National Seashores and to aide in (potential?) future designations of critical habitat, we conducted widespread, systematic surveys for black rails and other secretive marsh birds within the parks during the breeding seasons of 2022 and 2023. A total of 1,222 surveys were conducted at 431 points over the course of 2 years. In addition to recording detections of all focal species, we recorded detections of 6 eastern black rails on North Core Banks where they were not previously known to occur. The population of black rails occupying the high marsh habitat on North Core Banks could account for 5-10% of the North Carolina black rail population and increase the known sites occupied within the state.
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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7587220.bard.
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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.