Structural Impairment Detection Using Arrays of Competitive Artificial Neural Networks

Aging railroad bridge infrastructure is subject to increasingly higher demands such as heavier loads, increased speed, and increased frequency of traffic. The challenges facing railroad bridge infrastructure provide an opportunity to develop improved systems of monitoring railroad bridges. This dissertation outlines the development and implementation of a Structural Impairment Detection System (SIDS) that incorporates finite element modeling and instrumentation of a testbed structure, neural algorithm development, and the integration of data acquisition and impairment detection tools. Ultimately, data streams from the Salmon Bay Bridge are autonomously recorded and interrogated by competitive arrays of artificial neural networks for patterns indicative of specific structural impairments. Heel trunnion bascule bridges experience significant stress ranges in critical truss members. Finite element modeling of the Salmon Bay Bridge testbed provided an estimate of nominal structural behavior and indicated types and locations of possible impairments. Analytical modeling was initially performed in SAP2000 and then refined with ABAQUS. Modeling results from the Salmon Bay Bridge were used to determine measureable quantities sensitive to modeled impairments. An instrumentation scheme was designed and installed on the testbed to record these diagnostically significant data streams. Analytical results revealed that main chord members and bracing members of the counterweight truss are sensitive to modeled structural impairments. Finite element models and experimental observations indicated maximum stress ranges of approximately 22 ksi on main chord members of the counterweight truss. A competitive neural algorithm was developed to examine analytical and experimental data streams. Analytical data streams served as training vectors for training arrays of competitive neural networks. A quasi static array of neural networks was developed to provide an indication of the operating condition at specific intervals of the bridge's operation. Competitive neural algorithms correctly classified 94% of simulated data streams. Finally, a stand-alone application was integrated with the Salmon Bay Bridge data acquisition system to autonomously analyze recorded data streams and produce bridge condition reports. Based on neural algorithms trained on modeled impairments, the Salmon Bay Bridge operates in a manner most resembling one of two operating conditions: 1) unimpaired, or 2) impaired embedded member at the southeast corner of the counterweight

Models and methods for operational planning in freight railroads

textRailroads are facing increasing demand for freight transportation. Effective planning and scheduling are crucial to improve the utilization of expensive resources (such as crew and track), reduce operational costs, and provide on-time service. This dissertation focuses on problem modeling and solution method development for real planning problems faced by railroads. It consists of three chapters that study two important planning problems in the daily operations of U.S. freight railroads: crew assignment and train movement planning. Chapter 2 proposes an optimization model to decide crew-to-train assignments and deadheads for double-ended crew districts. We develop an effective solution approach, combining optimization and a standalone heuristic, that generates optimal solutions in minutes. The excellent performance of this solution approach makes it well-suited for implementation within a real-time decision support tool for crew dispatchers. Chapter 3 discusses crew repositioning given the uncertainty in trains’ arrival and departure times. We propose models that minimize the expected crew holding, train delay, and deadheading cost, and develop both exact and heuristic solution methods to provide insights for crew planning under train schedule uncertainty. The last chapter studies the movement planning problem for trains traveling in a territory with multiple through tracks (mainlines) and various junctions. We explore a number of heuristic algorithms to obtain good solutions within a reasonable amount of time. The contributions of this dissertation include modeling enhancements, algorithmic development, implementation and computational testing, and validation using real data.Operations Research and Industrial Engineerin

Solving transportation scheduling problems : models and algorithms

Given the increasing load on current logistics and transportation systems, it is crucial to improve resource utilization and reduce operational costs. This can be achieved by developing better models and algorithms for transportation planning and scheduling. The main challenges include the mathematical modeling of operational rules, uncertainties in operations, and large-scale problem size. This dissertation addresses crew scheduling in freight railways and vehicle routing problems (VRP) for mail processing and distribution centers (P&DCs). Our goal is to develop models and algorithms that improve efficiency and reduce operating costs. In Chapter 2, we propose an optimization model to support real-time freight railway crew assignment decisions. Due to workload balance requirements and operating regulations, the optimization model is difficult to solve for realistic instances. Hence, we propose model improvements and develop effective solution techniques to find optimal or near-optimal solutions very quickly. Chapter 3 extends the freight rail crew scheduling problem by incorporating uncertainty in train arrival and departure times. We propose a stochastic programming model, but this model is solvable only the number of scenarios is small. As a consequence, we develop heuristics that use an analytical model to calculate the expected total cost of a given choice of crew deadheads. Using this cost evaluator, we develop four local search based heuristic algorithms to sequentially improve crew scheduling decisions under uncertainty. In Chapter 4, we first cluster the pickup and drop off points in mail P&DCs into zones and then minimize the number of vehicles required and the total distance traveled to meet daily transport demand. The clustering is performed with a greedy randomized adaptive search procedure, and two heuristics are developed to find solutions to the VRP, which proved intractable for realistic instances. The heuristics are optimization-based within a rolling horizon framework. An extensive analysis is undertaken to evaluate the relative performance of the two heuristics. The contributions of this dissertation include modeling, algorithmic development, computational testing, and validation using real and randomly generated data.Mechanical Engineerin

Railway Ecology

carbon footprint; environmental impacts of railways; transportation; wildlife; landscape; planning; engineering; efficiency; sustainability; biodiversity; animal casualties on rail

Management: A continuing bibliography with indexes, March 1983

This bibliography lists 960 reports, articles, and other documents introduced into the NASA scientific and technical information system in 1982

Managing heterogeneous traffic on rail freight networks incorporating the logistics needs of market segments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1994.Includes bibliographical references (leaves 203-209).by Oh Kyoung Kwon.Ph.D

Maine, 125th Anniversary Issue, 125 Alumni Who Have Made a Difference

A special edition of the University of Maine Alumni Association magazine, Maine, prepared for the 125th anniversary of the university. The edition highlighted the accomplishments of 125 alumni to include men and women who made a contribution to a wide range of fields -- engineers, physicians, novelists, Olympians, mountain climbers, photographers, politicians, foresters, farmers, economists, educators, and soldiers. Selections were made by a committee of alumni in an effort to represent the spirit of the university -- one of innovation, vision, and service

Maine, Volume 72, Number 3, 125th Anniversary Issue, 125 Alumni Who Have Made a Difference

Contents: A special edition prepared for the 125th anniversary of the university highlighting the accomplishments of 125 alumni contributing to a wide range of fields -- engineers, physicians, novelists, Olympians, mountain climbers, photographers, politicians, foresters, farmers, economists, educators, and soldiers. Selections were made by a committee of alumni in an effort to represent the spirit of the university -- one of innovation, vision, and service.https://digitalcommons.library.umaine.edu/alumni_magazines/1084/thumbnail.jp

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Profit Based Simulation Model for The Rail Transportation Industry

Schedules often conflict in the rail transportation industry. Operations managers assign resources and make scheduling decisions with no visibility of the revenue, cost, and profitability characteristics of the route they are manipulating. Transit speed decisions focus on ensuring trains safely reach their destination on time with little regard given to the actual service needs of the customer. Although all customers want on-time deliveries, few actually pay a premium to garner this level of preferential treatment. Operating in this type of environment results in decisions that severely erode profits. In this dissertation, a simulation model referred to as the Rail Profit Model (RPM) is developed to test three transit strategies that reveal how transit speed decisions impact supply chain and rail service provider profits and to lay the groundwork to challenge the cultural premise that the rail industry must behave like the trucking industry in order to thrive. In fact, the Rail Profit Model demonstrates that most trains should maintain the most economical speed to maximize profits. The model also identifies specific scenarios where increasing speed to arrive on time is the most profitable solution, contributing to the ability to leverage revenue management techniques to ensure customers pay the adequate premium that on-time delivery requires. Equipped with the Rail Profit Model, operations managers can now examine transit speed decisions and de-conflict competing resources to form recommended solutions that preserve maximum profits for the rail service provider and supply chain