Development and implementation of utility relocation cost estimation system

This thesis explores how to leverage information management techniques in developing a database system which can store, access and query data to generate preliminary cost estimate reports for utility relocations in highway construction projects. Although cost estimation for utility relocation is an essential part of most transportation projects, there are very few ready-to-use cost database or software platform available to fulfill this purpose for state DOTs personnel. Therefore, the research aimed to develop a database system that can provide estimates with historical cost data. The unit cost data used in this database are derived either from the executed utility agreements between TxDOT office and utility owners or a publicly available open source database. The estimated costs are computed with these pre-stored data. As a result of the research, the Utility Relocation Cost Estimation Database system was completed and has been handed over to TxDOT Austin District for further tests and implementations.Civil, Architectural, and Environmental Engineerin

Relative depth estimation from single monocular images with deep convolutional network

Field of study: Computer science.Dr. Grant Scott, Thesis Supervisor."December 2017."Depth estimation from single monocular images is a theoretical challenge in computer vision as well as a computational challenge in practice. This thesis addresses the problem of depth estimation from single monocular images using a deep convolutional neural fields framework; which consists of convolutional feature extraction, superpixel dimensionality reduction, and depth inference. Data were collected using a stereo vision camera, which generated depth maps though triangulation that are paired with visual images. The visual image (input) and computed depth map (desired output) are used to train the model, which has achieved 83 percent test accuracy at the standard 25 percent tolerance. The problem has been formulated as depth regression for superpixels and our technique is superior to existing state-of-the-art approaches based on its demonstrated its generalization ability, high prediction accuracy, and real-time processing capability. We utilize the VGG-16 deep convolutional network as feature extractor and conditional random fields depth inference. We have leveraged a multi-phase training protocol that includes transfer learning and network fine-tuning lead to high performance accuracy. Our framework has a robust modular nature with capability of replacing each component with different implementations for maximum extensibility. Additionally, our GPU-accelerated implementation of superpixel pooling has further facilitated this extensibility by allowing incorporation of feature tensors with exible shapes and has provided both space and time optimization. Based on our novel contributions and high-performance computing methodologies, the model achieves a minimal and optimized design. It is capable of operating at 30 fps; which is a critical step towards empowering real-world applications such as autonomous vehicle with passive relative depth perception using single camera vision-based obstacle avoidance, environment mapping, etc.Includes bibliographical references (pages 61-65)

A Generalized Circle Theorem on Zeros of Partition Function at Asymmetric First Order Transitions

We present a generalized circle theorem which includes the Lee-Yang theorem for symmetric transitions as a special case. It is found that zeros of the partition function can be written in terms of discontinuities in the derivatives of the free energy. For asymmetric transitions, the locus of the zeros is tangent to the unit circle at the positive real axis in the thermodynamic limit. For finite-size systems, they lie off the unit circle if the partition functions of the two phases are added up with unequal prefactors. This conclusion is substantiated by explicit calculation of zeros of the partition function for the Blume-Capel model near and at the triple line at low temperatures.Comment: 10 pages, RevTeX. To be published in PRL. 3 Figures will be sent upon reques

A constitutive model for unsaturated cemented soils under cyclic loading

On the basis of plastic bounding surface model, the damage theory for structured soils and unsaturated soil mechanics, an elastoplastic model for unsaturated loessic soils under cyclic loading has been elaborated. Firstly, the description of bond degradation in a damage framework is given, linking the damage of soil's structure to the accumulated strain. The Barcelona Basic Model (BBM) was considered for the suction effects. The elastoplastic model is then integrated into a bounding surface plasticity framework in order to model strain accumulation along cyclic loading, even under small stress levels. The validation of the proposed model is conducted by comparing its predictions with the experimental results from multi-level cyclic triaxial tests performed on a natural loess sampled beside the Northern French railway for high speed train and about 140 km far from Paris. The comparisons show the capabilities of the model to describe the behaviour of unsaturated cemented soils under cyclic loading

A Panel Study of Outsourced Maintenance Impact on Major U.S. Passenger Airlines\u27 Profitability (1995-2019)

This study investigates eight viable United States major passenger airlines\u27 outsourced maintenance impact on profitability between 1995 and 2019 by using panel data analysis. The results demonstrate that the percentage of airline outsourced maintenance and inhouse maintenance labor pay have no statistically significant impact on profitability. The researchers call for the further research with a larger sample, and more time periods to explore airlines\u27 outsourced maintenance impact on profitability

Prediction of extreme events in the OFC model on a small world network

We investigate the predictability of extreme events in a dissipative Olami-Feder-Christensen model on a small world topology. Due to the mechanism of self-organized criticality, it is impossible to predict the magnitude of the next event knowing previous ones, if the system has an infinite size. However, by exploiting the finite size effects, we show that probabilistic predictions of the occurrence of extreme events in the next time step are possible in a finite system. In particular, the finiteness of the system unavoidably leads to repulsive temporal correlations of extreme events. The predictability of those is higher for larger magnitudes and for larger complex network sizes. Finally, we show that our prediction analysis is also robust by remarkably reducing the accessible number of events used to construct the optimal predictor.Comment: 5 pages, 4 figure

Desingularization of vortices for the Euler equation

We study the existence of stationary classical solutions of the incompressible Euler equation in the plane that approximate singular stationnary solutions of this equation. The construction is performed by studying the asymptotics of equation -\eps^2 \Delta u^\eps=(u^\eps-q-\frac{\kappa}{2\pi} \log \frac{1}{\eps})_+^p with Dirichlet boundary conditions and $q$ a given function. We also study the desingularization of pairs of vortices by minimal energy nodal solutions and the desingularization of rotating vortices.Comment: 40 page

Data-driven methodologies for supporting decision-making in roadway safety and pavement management

There has been a significant rise in the utilization of data-driven methods within the contemporary realm of transportation engineering. This trend is primarily attributed to the limitations associated with experience-based methods, such as subjectivity and non-reproducibility. In contrast, data-driven methods have proven to offer a more objective and effective approach to problem analysis, thereby providing decision-makers with a reliable basis for informed decision-making. This present research focuses on two types of data-driven methodologies: geostatistical analyses utilizing geographic information systems (GIS) and cutting-edge algorithms associated with artificial intelligence (AI). In numerical analysis, data provides a means to gain valuable insights into a problem of interest. While AI-oriented methods have been shown in many studies to be more effective than traditional approaches, the accuracy of the analysis still heavily depends on the quality of the data. This dissertation endeavors to shed light on the pivotal role that data plays in both roadway safety analysis and pavement management. To accomplish this, four distinct studies are proposed that examine different aspects of data-driven methods. The studies encompass an evaluation of data consistency in motor vehicle crash databases, the identification of crash hot spots within a road network, a synthesis of advancements in the application of AI algorithms to various activities of pavement management, and an exploration of the relationship between pavement conditions and roadway safety using AI-oriented methods. The knowledge acquired from these studies serves as a foundation for future research, advancements, and the adoption of innovative approaches to enhance the efficiency of safety analysis and pavement management. This research ultimately facilitates informed decision-making, effective resource allocation, and the implementation of cost-effective interventions to enhance roadway safety and optimize pavement management practices.Civil, Architectural, and Environmental Engineerin