INFLUENCE OF MATERIAL CHARACTERIZATION IN THE DESIGN OF TUNNEL LIGHTING INSTALLATIONS

The paper describes the influence of the characterisation of reflectance behaviour of tunnel pavements and wall materials on the tunnel lighting design. CIE 189 document suggests considering lambertian behaviour for inter-reflection calculations for road luminance evaluation at design stage, because, unfortunately, no bi-directional reflection data for tunnel surfaces are commonly available. This simplification is supported by the low impact of inter- reflection contribution to road luminance. A European funded research project ha the task of developing the metrological support for the road surface characterisation in new geometries of measurements. The paper suggests to apply the outcomes on new geometries to tunnel wall materials characterisation suggesting that the suggested SURFACE observation angle of 2.29° can be useful for short tunnel too, including wall surfaces

Bio-Based Renewable Additives for Anti-Icing Applications (Phase II)

The performance and impacts of several agro-based anti-icers along with a traditional chloride-based anti-icer (salt brine) were evaluated. A statistical design of experiments (central composite design) was employed for developing anti-icing liquids consisting of cost-competitive chemicals such as agro-based compounds (e.g., Concord grape extract and glycerin), sodium chloride, sodium metasilicate, and sodium formate. The following experimentally obtained parameters were examined as a function of the formulation design: ice-melting capacity at 25°F (−3.9°C), splitting strength of Portland cement mortar samples after 10 freeze-thaw/deicer cycles, corrosion rate of C1010 carbon steel after 24-hour immersion, and impact on asphalt binder stiffness and m-value. One viable formula (“best performer”) was tested for thermal properties by measuring its differential scanning calorimetry (DSC) thermograms, the friction coefficient of asphalt pavement treated by this anti-icing formulation (vs. 23 wt.% NaCl and beet juice blend) at 25°F after being applied at 30 gallons per lane mile (1 hour after simulated trafficking and plowing), and other properties (pH, oxygen demand in COD). Laboratory data shed light on the selection and formulation of innovative agro-based snow- and ice-control chemicals that can significantly reduce the costs of winter maintenance operations

Optimization of highway work zone decisions considering Short-term and Long-term Impacts

With the increase of the number, duration, and scope of maintenance projects on the national highway system, transportation agencies face great challenges in developing effective comprehensive work zone management plans which minimize the negative impacts on road users and workers. The types of maintenance operation, timing, duration, configuration, and user impact mitigation strategies are major considerations in developing work zone management plans. Some of those decisions may not only affect road users during the maintenance phase but also have significant impacts on pavement serviceability in future years. This dissertation proposes a systematic methodology for jointly optimizing critical work zone decisions, based on analytical and simulation models developed to estimate short-term impacts during the maintenance periods and long-term impacts over the pavement life cycle. The dissertation starts by modeling the effects of different work zone decisions on agency and user costs during the maintenance phase. An analytic one-time work zone cost model is then formulated based on simulation analysis results. Next, a short-term work zone decision optimization model is developed to find the best combination of lane closure and traffic control strategies which can minimize the one-time work zone cost. Considering the complex and combinatorial nature of this optimization problem, a heuristic optimization algorithm, named two-stage modified population-based simulated annealing (2PBSA), is designed to search for a near-optimal solution. For those maintenance projects that may need more detailed estimation of user delay or other impacts, a simulation-based optimization method is proposed in this study. Through a hybrid approach combining simulation and analytic methods along with parallel computing techniques, the proposed method can yield satisfactory solutions while reducing computational efforts to a more acceptable level. The last part of this study establishes a framework for jointly optimizing short-term and long-term work zone decisions with the objective of maximizing cost-effectiveness. Case studies are conducted to test the performance of the proposed methods and develop guidelines for development of work zone management plans

Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems

Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6\u27s states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities that may cause loss of many lives. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail a massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 still need to improve the current practice and systems to robustly identify and accurately predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. The outcomes from this project would help engineers and decision-makers in Region 6\u27s State DOTs optimize and sequence transportation recovery processes at a regional network level with necessary recovery factors and evaluating its long-term impacts after disasters

Impact of Embedded Carbon Fiber Heating Panel on the Structural/Mechanical Performance of Roadway Pavement

INE/AUTC 12.3

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione

Optimized scheduling of highway work zones

Highway maintenance activities usually require lane closures and disrupt traffic operations. Because of budget constraints, project deadlines, and the resulting traffic impact, the objective of this dissertation is to improve the efficiencies of traffic operation and maintenance work, and minimize the total project cost (i.e., agency cost and road user cost) by optimizing work zone schedules. This dissertation focuses on the maintenance projects on multiple-lane highways. The objective total cost function is formulated while considering a discrete maintenance time-cost function and time-dependent traffic diversions. However, the work zone scheduling problem is a combinatorial optimization problem and difficult to solve analytically. This dissertation transformed the complicated problem into two separate steps: determining the time-dependent traffic diversion by the User Equilibrium Assignment, and minimizing the total project cost by a Genetic Algorithm. An iterative algorithm that integrates the two steps was developed. The optimized work zone schedule and the associated optimal diverted traffic flow can be found simultaneously after multiple iterations. Case studies and extensive sensitivity analyses were conducted to analyze various scheduling scenarios with or without a time-cost function and traffic diversion. The relations among key decision variables were analyzed. Conclusions and recommendations are provided, and directions of future research efforts are discussed

2022 Technical Program

INSPIRE University Transportation Center 2022 Annual MeetingAugust 1-2, 202