Browsing by Author "Palese, Michael E."
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Item Ballast particle behavior under varying conditions using tri-axial inertial sensors(University of Delaware, 2021) Palese, Michael E.Maintaining ballast condition is an important aspect of maintaining a healthy track infrastructure system. Cyclic train loading over sections of track naturally degrades the ballast which must maintain a proper layer thickness and ballast particle gradation to optimally function. Over time, repeated axle loadings deform the ballast layer and degrade the ballast particles changing the effective ballast particle gradation and thus the strength of the track substructure. This behavior is historically handled by maintenance procedures aimed at reinforcing the track structure by strengthening the ballast. ☐ Maintenance engineers benefit from detailed information about specific degradation behavior which allows them to perform timely and effective maintenance strategies such as tamping, undercutting and ballast cleaning. Historically, ideal maintenance scheduling has relied heavily on past experience, manual inspection, and field observations. Delaying maintenance past when it is required can cause degradation of track geometry, which can result in safety concerns and substantial track damage. Conversely, performing maintenance too early increases costs. Since track deterioration results in larger financial consequences, railroads tend to err on the side of caution. ☐ The research conducted herein focuses on developing a specialized ballast condition evaluation hardware and associated software to quantify ballast condition and failure criteria. SmartRocks are battery-powered wireless devices that resemble a ballast particle that are placed in track within the existing ballast layer and are capable of measuring acceleration and orientation in real time using tri-axial accelerometers and tri-axial gyroscopes. SmartRocks are installed in chosen sections of ballast, operating remotely, and transmitting recorded data to receivers installed on the field side of the track. Using SmartRock data, analysis was conducted to investigate SmartRock behavior, and thus ballast behavior, under various track conditions in an effort to correlate their behavior with known installation site information. ☐ To compare individual SmartRock measurements, the data was first subject to a basis change such that all data was in a uniform track coordinate basis. Following, extensive Exploratory Data Analysis (EDA) was conducted to identify and compare SmartRock measurement characteristics using descriptive statistics and unsupervised machine learning methods which are fully explained. Using the available SmartRock data, analysis showed that they behave differently under varying conditions of design and installation. For example, the data clearly shows that SmartRock response from passing trains differed for SmartRocks installed under-tie pads vs. those installed without under-tie pads. It is hypothesized that response data from SmartRocks allows for determinations of ballast behavior which in turn can lead to better understanding of ballast degradation and failure mechanisms. SmartRock response data is analyzed and explained, with examples showing how the data was used to make determinations about the two track sections in which SmartRocks were installed.Item Landslide hazard assessment framework for cut-slopes along railroad rights-of-way using statistical analysis of images(University of Delaware, 2023) Palese, Michael E.Transportation corridors created using cuts and fills often introduce steep slopes, or geohazards, along rights-of-way that require frequent monitoring to guarantee their stability. Slope failure events, in which material is wasted onto rights-of-way, have historically caused infrastructure damage, operational suspensions, train accidents, and in the worst cases, loss of life. Numerous techniques, both statistical and deterministic in nature, have been developed to spatially and temporally assess unstable slope hazards and failure risk. However, these methods are reliant on the scope and accuracy of geotechnical, maintenance, and remotely-sensed datasets. ☐ The railway industry has benefited from the rise in "Big Data" analysis techniques over the last several decades using routinely collected inspection data such as right-of-way videos and track geometry measurements. Using computer science and engineering methods, the introduction of large and more varied datasets has allowed new types of analyses to be developed to optimize railroad construction, maintenance, and operational activities. This dissertation introduces a hazard assessment framework for cut-slopes in railroad rights-of-way using quantitative assessments of videos recorded during track geometry inspection runs. Using developed computer vision models, candidate hazardous sections and unstable slope features were identified to statistically analyze slope condition along a railroad section using a novel hazard assessment framework. Application of the framework indicated that right-of-way inspection videos could be used as an additional remotely-sensed data source to enhance statistical slope hazard assessments. ☐ This research aimed to expand upon previous investigations into the quantitative analysis of right-of-way videos to bolster inspection data collected during routine track geometry car runs, specifically to assess cut-slope hazards. It was hypothesized that combining slope hazard analysis results with remotely-sensed geospatial data sources will allow the study area to be assessed both locally and regionally. The hazard assessment framework aimed to utilize both data sources as inputs to spatially analyze locations along the studied track section to statistically determine those most susceptible to a failure event. Importantly, the proposed framework was dependent on only remotely-sensed data and thus the produced hazard scores do not require sending engineers to take geotechnical measurements and instead could be used to prioritize locations of interest for follow-up site investigations.