Master of Science / Department of Civil Engineering / Sunanda Dissanayake / Although extensive research has been conducted on urban freeway capacity estimation methods, minimal research has been carried out for rural highway sections, especially sections within work zones. This study filled that void for rural highways in Kansas. This study estimated capacity of rural highway work zones in Kansas. Six work zone locations were selected. An average of six days’ worth of field data was collected, from mid-October 2013 to late November 2013, at each of these work zone sites. Two capacity estimation methods were utilized, including the Maximum Observed 15-minute Flow Rate Method and the Platooning Method divided into 15-minute intervals. The Maximum Observed 15-minute Flow Rate Method provided an average capacity of 1469 passenger cars per hour per lane (pcphpl) with a standard deviation of 141 pcphpl, while the Platooning Method provided a maximum average capacity of 1195 pcphpl and a standard deviation of 28 pcphpl. Based on observed data and analysis carried out in this study, the recommended capacity to be used is 1500 pcphpl when designing work zones for rural highways in Kansas. This research provides the proposed standard value of rural highway work zone capacities so engineers and city planners can effectively mitigate congestion that would have otherwise occurred due to impeding construction/maintenance.

The fundamentals for continued growth of broadband wireless remain sound. According to the Ericsson’s official forecasts, the addressable global market of wireless internet broadband connectivity reaches to 320 million users by the end of 2010. The opportunity for BWA/WiMAX to serve those who want to switch to broadband service is huge in many parts of the world where wireline technologies may not be feasible.The current document (Capacity and Cell-range Estimation for Multitraffic Users in Mobile WiMAX) is prepared as a master’s program final thesis to peruse the service provision capabilities of Mobile WiMAX innovate technology in more details. An elaborate excerpt of the technical subjects of IEEE-802.16e-2005 standard is gathered in the first chapter to provide the reader with a practical concept of Mobile WiMAX technology. The following chapter is aimed to collect the required knowledge for WiMAX planning problem. An innovate methodology to calculate the system’s actual throughput and a traffic model for mixed application users are proposed with a step by step description to derive an algorithm to determine the maximum number of subscribers that each specific Mobile WiMAX sector may support. The report also contains a Matlab code –enclose in the appendix– that tries to implement the entire algorithm for different system parameter and traffic cases to ease the Mobile WiMAX planning problem. The last chapter introduces the mostly used propagation models that suit the WiMAX applications.The presented methodology would help those operators that plan to implement a wide coverage network in a city. Using the introduced methodology, service providers will be able to estimate the number of base stations and hence the network investment and profitability. / Uppsatsnivå: D

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The fast-growing electric vehicles (EVs) market demands huge efforts from car manufacturers to develop and improve their current products’ systems. A fast charge of the battery pack is one of the challenges encountered due to the battery limitations regarding behaviour and additional degradation when exposed to such a rough situation. In addition, the outcome of a study performed on a battery does not apply to others, especially if their chemistries are different. Hence, extensive testing is required to understand the influence of design decisions on the particular energy storage device to be implemented. Due to batteries’ nonlinear behaviour that is highly dependent on external variables such as temperature, the dynamic load and aging, another defying task is the widely studied state of charge (SOC) estimation, commonly considered one of the most significant functions in a battery management system (BMS). This thesis presents an extensive battery fast charging aging test study equipped with promising current charging profiles from published literature to minimize aging. Four charging protocols are carefully designed to charge the cell from 10 to 80% SOC within fifteen minutes and have their performances discussed. A dual state estimation algorithm is modelled to estimate the SOC with the assistance of a capacity state of health (SOHcap) estimation. Finally, the dual state estimation model is validated with the fast charging aging test data. / Thesis / Master of Science in Mechanical Engineering (MSME)

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Corrosion damage reduces the load-carrying capacity of bridges which poses a threat to passenger safety. The objective of this research was to reduce the resources involved in conventional bridge inspections which are an important tool in the condition assessment of bridges and to help in determining if live load testing is necessary. This research proposes a framework to link semi-automated damage detection on prestressed concrete bridge girders with the estimation of their residual flexural capacity. The framework was implemented on four full-scale corrosion damaged girders from decommissioned bridges in Virginia. 3D point clouds of the girders reconstructed from images using Structure from Motion (SfM) approach were textured with images containing cracks detected at pixel level using a U-Net (Fully Convolutional Network). Spalls were detected by identifying the locations where normals associated with the points in the 3D point cloud deviated from being perpendicular to the reference directions chosen, by an amount greater than a threshold angle. 3D textured mesh models, overlaid with the detected cracks and spalls were used as 3D damage maps to determine reduced cross-sectional areas of prestressing strands to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011). Scaling them to real-world dimensions enabled the measurement of any required dimension, eliminating the need for physical contact. The flexural capacities of a box beam and an I-beam estimated using strain compatibility analysis were validated with the actual capacities at failure sections determined from four destructive tests conducted by Al Rufaydah (2020). Along with the reduction in the cross-sectional areas of strands, limiting the ultimate strain that heavily corroded strands can develop was explored as a possible way to improve the results of the analysis. Strain compatibility analysis was used to estimate the ultimate rupture strain, in the heavily corroded bottommost layer prestressing strands exposed before the box beam was tested. More research is required to associate each level of strand corrosion with an average ultimate strain at which the corroded strands rupture. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework. / Master of Science / Corrosion damage is a major concern for bridges as it reduces their load carrying capacity. Bridge failures in the past have been attributed to corrosion damage. The risk associated with corrosion damage caused failures increases as the infrastructure ages. Many bridges across the world built forty to fifty years ago are now in a deteriorated condition and need to be repaired and retrofitted. Visual inspections to identify damage or deterioration on a bridge are very important to assess the condition of the bridge and determine the need for repairing or for posting weight restrictions for the vehicles that use the bridge. These inspections require close physical access to the hard-to-reach areas of the bridge for physically measuring the damage which involves many resources in the form of experienced engineers, skilled labor, equipment, time, and money. The safety of the personnel involved in the inspections is also a major concern. Nowadays, a lot of research is being done in using Unmanned Aerial Vehicles (UAVs) like drones for bridge inspections and in using artificial intelligence for the detection of cracks on the images of concrete and steel members. Girders or beams in a bridge are the primary longitudinal load carrying members. Concrete inherently is weak in tension. To address this problem, High Strength steel reinforcement (called prestressing steel or prestressing strands) in prestressed concrete beams is pre-loaded with a tensile force before the application of any loads so that the regions which will experience tension under the service loads would be subjected to a pre-compression to improve the performance of the beam and delay cracking. Spalls are a type of corrosion damage on concrete members where portions of concrete fall off (section loss) due to corrosion in the steel reinforcement, exposing the reinforcement to the environment which leads to accelerated corrosion causing a loss of cross-sectional area and ultimately, a rupture in the steel. If the process of detecting the damage (cracks, spalls, exposed or severed reinforcement, etc.) is automated, the next logical step that would add great value would be, to quantify the effect of the damage detected on the load carrying capacity of the bridges. Using a quantified estimate of the remaining capacity of a bridge, determined after accounting for the corrosion damage, informed decisions can be made about the measures to be taken. This research proposes a stepwise framework to forge a link between a semi-automated visual inspection and residual capacity evaluation of actual prestressed concrete bridge girders obtained from two bridges that have been removed from service in Virginia due to extensive deterioration. 3D point clouds represent an object as a set of points on its surface in three dimensional space. These point clouds can be constructed either using laser scanning or using Photogrammetry from images of the girders captured with a digital camera. In this research, 3D point clouds are reconstructed from sequences of overlapping images of the girders using an approach called Structure from Motion (SfM) which locates matched pixels present between consecutive images in the 3D space. Crack-like features were automatically detected and highlighted on the images of the girders that were used to build the 3D point clouds using artificial intelligence (Neural Network). The images with cracks highlighted were applied as texture to the surface mesh on the point cloud to transfer the detail, color, and realism present in the images to the 3D model. Spalls were detected on 3D point clouds based on the orientation of the normals associated with the points with respect to the reference directions. Point clouds and textured meshes of the girders were scaled to real-world dimensions facilitating the measurement of any required dimension on the point clouds, eliminating the need for physical contact in condition assessment. Any cracks or spalls that went unidentified in the damage detection were visible on the textured meshes of the girders improving the performance of the approach. 3D textured mesh models of the girders overlaid with the detected cracks and spalls were used as 3D damage maps in residual strength estimation. Cross-sectional slices were extracted from the dense point clouds at various sections along the length of each girder. The slices were overlaid on the cross-section drawings of the girders, and the prestressing strands affected due to the corrosion damage were identified. They were reduced in cross-sectional area to account for the corrosion damage as per the recommendations of Naito, Jones, and Hodgson (2011) and were used in the calculation of the ultimate moment capacity of the girders using an approach called strain compatibility analysis. Estimated residual capacities were compared to the actual capacities of the girders found from destructive tests conducted by Al Rufaydah (2020). Comparisons are presented for the failure sections in these tests and the results were analyzed to evaluate the effectiveness of this framework. More research is to be done to determine the factors causing rupture in prestressing strands with different degrees of corrosion. This framework was found to give satisfactory estimates of the residual strength. Reduction in resources involved in current visual inspection practices and eliminating the need for physical access, make this approach worthwhile to be explored further to improve the output of each step in the proposed framework.

The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.