COMPUTATIONAL MODELING OF CLIMATE ATTRIBUTES AND CONDITION DETERIORATION OF CONCRETE HIGHWAY PAVEMENTS

An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. Road and highway infrastructures performance in any country is impacted by load repetitions and it is further compromised by climate attributes and extreme weather events. Damages to roads and bridges are among the infrastructure failures that have occurred during these extreme events. If maintenance and rehabilitation are not done promptly, the damages to the road caused by heavy traffic and extreme climate may lead to life-threatening conditions for road users. A disruption in any one system affects the performance of others. For example, damages in road and bridge infrastructure will delay the recovery operation after a disaster. In 2018, a total of 331 natural disaster occurrences were reported worldwide, which resulted in 14,385 deaths. From 1900 to 2000, in 119 years, 14,854 natural disaster occurrences were reported which caused 32,651,605 deaths. Natural disaster occurrences like hurricanes, floods, droughts, landslides, etc. may be influenced by specific climate mechanisms like El Niño and Southern Oscillation (ENSO). Several climate attributes models were developed in this research employing Auto-Regressive Integrated Moving Average (ARIMA) methodology. The sea surface temperature data were analyzed and a prediction model was developed to predict future ENSO years. The model successfully predicted the 2018-2019 El Niño year. The model prediction showed that the next El Niño years will be 2021-22 and 2025-26. The model prediction also shows that the next La Niña year will be 2028-29. Global mean sea level (GMSL) data were analyzed and a prediction model was developed. The predicted annual rate of change in GMSL is 0.6 mm/year from 2013 to 2050. But a higher annual rate of change (1.4 mm/year) is predicted from 2031 to 2050. Northern hemisphere (Arctic) sea ice extent and southern hemisphere (Antarctic) sea ice extent data were investigated and two different models were developed. The model prediction shows that the total loss of northern hemisphere sea ice extent in 2050 will be 1.66 million km2. But the total gain of southern hemisphere sea ice extent will be 1.24 million km2. The net change of global sea ice extent will be -0.24 million km2, which indicates a loss of sea ice. The model predictions of the climate attributes can be used to understand and assess the future climate change in different climate zones worldwide. This understanding of climate changes and future predictions of climate attributes will help to develop climate adaptation strategies and better prepare the communities for extreme weather-related natural disaster occurrences. The condition deterioration progression of infrastructures, such as roads and bridges, is caused by load repetitions, as well as climate attributes and extreme weather. Pavements undergo maintenance and rehabilitations periodically to provide a smooth riding experience to the riders. Previous researches never considered maintenance and rehabilitation action history in the development of the condition deterioration model. This research considered the maintenance and rehabilitation history in the development and implementation of pavement condition deterioration models. The development of the IRI prediction model using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) considered the Long Term Pavement Performance (LTPP) climatic region, pavement structural properties, and traffic. The developed models are more objective, incorporate important input variables that are easily available, and are easy to implement in decision making. The concrete highway pavement IRI deterioration prediction models were developed and evaluated in this research for LTPP datasets of 1,482 for JPCP, 577 for JRCP, and 575 for CRCP. Comparatively, the AASHTO MEPDG performance equations were developed using fewer test sections. Three performance models were developed for output variable, IRI (outside wheel path) (m/km) for Jointed Plain Concrete Pavement (JPCP), Jointed Reinforced Concrete Pavement (JRCP), and Continuously Reinforced Concrete Pavement (CRCP). The input variables are similar for all the models. An in-depth study of M&R history collected from the LTPP database for all concrete pavement produced several CN_Code. The best models were found with the CN_Code developed based on the IRI value improvement and the type of M&R action and this variable is a continuous variable where number increment indicates the frequency of M&R action provided in the pavement section. The models’ final structure and accuracy statistics can be summarized as: JPCP (13-19-1; ANN R2 =0.94 and MLR R2 =0.49), JRCP (11-19-1; ANN R2 =0.95 and MLR R2 =0.58), and CRCP (14-19-1; ANN R2 =0.95 and MLR R2 =0.83). The ANN models show better accuracy in predicting pavement performance compare to the multiple regression models for all types of concrete pavements. The developed IRI prediction models can successfully characterize the behavior (i.e. the increase of IRI values with time and decrease of IRI value after maintenance and rehabilitation). The ANN models can be used to provide future M&R action by changing CN_Code frequency and the model successfully distinguishes the behavior of IRI (i.e. decrease of IRI after M&R action and increase of IRI with time as CESAL increases). The developed condition deterioration models for concrete highway pavement present a significant improvement on the models currently used in the mechanistic-empirical pavement design method. It is recommended to implement the pavement condition deterioration model developed in this research for life-cycle asset management and M&R programs

Effects of Diatomite and SBS on Freeze-Thaw Resistance of Crumb Rubber Modified Asphalt Mixture

Asphalt mixture is susceptible to moisture damage under the effect of freeze-thaw (F-T) cycles. In this paper, crumb rubber (CR) was used to modify stone mastic asphalt (SMA) and the effects of diatomite and styrene butadiene styrene (SBS) on antifreezing performances of crumb rubber modified SMA (CRSMA) were investigated. Regression analysis and modified grey model (MGM) were used to construct the prediction models for properties of modified mixtures. CRSMA, CR and diatomite modified SMA (CRDSMA), and CR and SBS modified SMA (CRSSMA) were prepared in laboratory, respectively. Process of F-T cycles was designed. Air void, indirect tensile strength (ITS), and indirect tensile stiffness modulus (ITSM) were measured to evaluate the antifreezing performances of CRSMA, CRDSMA, and CRSSMA. Results indicate that air voids increase with the increasing of F-T cycles. ITS and ITSM all decrease with the increasing of F-T cycles. The addition of diatomite and SBS can reduce the air void and improve the ITS and ITSM of CRSMA. CRSSMA presents the lowest air void, highest tensile strength, and largest stiffness modulus, which reveals that CRSSMA has the best F-T resistance among three different kinds of mixtures. Moreover, MGM (1, 2) models present more favorable accuracy in prediction of air void and ITS compared with regression ones

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

Predicting pavement performance utilizing artificial neural network (ANN) models

Pavement management systems (PMS) play a significant role in cost-effective management of highway networks to optimize pavement performance over predicted service life of the pavements. Successful PMS implementation requires accurate performance prediction modeling to plan future maintenance and rehabilitation strategies. The Iowa DOT manages three primary highway systems (i.e., Interstate, US, and Iowa highways) that represent 8% (approximately 9,000 miles) of the total roadway system in the state (114,000 miles), but these systems carry around 62% of the total vehicle miles traveled (VMT) and 92% of the total large truck VMT (ASCE, 2015). These highways play a major role in Iowa’s economy because highways are important to several sectors (e.g., agriculture, manufacturing, and industry). According to the Bureau of Transportation Statistics, in 2012 around 263.36 billion tons of goods valued at $195.99 billion were transported on Iowa highways (BTS, 2012). PMSs that use robust pavement prediction models are needed to ensure continued optimum performance of Iowa highways. In the past, these models were developed from historical information about pavement condition data. In this research, historical climate data was acquired from the Iowa Environmental Mesonet and integrated with pavement condition data to include all related variables in prediction modeling. An artificial neural network (ANN) model was used to predict the performance of ride, cracking, rutting, and faulting indices on different pavement types. The goodness of fit of the ANN prediction models was compared with multiple linear regression (MLR) models. The results show that ANN models were more accurate in predicting future conditions than MLR models. The contribution of input variables in prediction models were also determined and discussed. The results indicated that weather factors directly influence highway pavement conditions, and that ANN model results can be used by decision makers and maintenance engineers to determine proper treatment actions and pavement designs to withstand harsh weather over the years. An ANN model that was used to estimate the correlation between the rutting depth and structural capacity of asphalt pavements suggests that rutting depth can be an indicator of structural capacity. As such, an ANN approach might be feasible for small transportation agencies (e.g., cities and counties) that cannot afford to collect structural information

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Development of Empirical and Mechanistic Empirical Performance models at Project and Network levels

Performance prediction models are a vital component in pavement management systems (PMS). Along with decision trees, prediction models are used to set priorities for maintenance and rehabilitation planning, and ultimately for budget allocations at the network level. Reliable and accurate prediction of pavement deterioration over time helps transportation agencies accurately predict future spending and save significant amounts of money. Within a PMS, raw performance data is often converted into aggregated performance indices, such as the Riding Comfort Index (RCI), to quantify the road’s roughness, or the Distress Surface Index (SDI), to quantify accumulated pavement distress. Technology has evolved rapidly in the last two decades, making data collection for pavement conditions (i.e. roughness and distress data) more feasible for transportation agencies. However, transportation agencies, especially at the municipal level, only maintain condition data to evaluate the present pavement status. Only limited attempts have so far been made to develop or enhance existing deterioration models in pavement management systems, using periodically collected condition data over time. A well-maintained historical database of pavement condition measurements and performance indices can be a useful source for the development of performance prediction models. In some cases, however, the database may contain incomplete data and insufficient information to develop reliable performance models. In addition to inconsistency in the historical performance data, the age of the pavement or the date of the last maintenance/ rehabilitation treatment may not be available to develop the pavement performance over time. The goal of this research is to develop enhanced empirical performance models capable of capturing the unpredictable and indeterminate nature of pavement deterioration behavior. This research provides a methodology to develop empirical models in the absence of the construction and/or rehabilitation dates. The models developed in this research use limited available historical data, and examine different parameters, such as pavement thickness, traffic pattern, and subgrade condition. Parameters such as the date of pavement construction and the age of the pavement are also incorporated into the proposed models, and are constrained by local experience and engineering judgment. A linear programming optimization technique is employed to develop the empirical models presented in this research. The approach demonstrated in this research can also be expanded to account for additional parameters, and can easily be adapted to match the needs of different agencies based on their local experience. In addition, the current research develops a second set of deterioration models based on mechanistic-empirical principles. Models incorporated into the mechanistic-empirical design guide are locally calibrated. A genetic algorithm optimization technique is employed to guide the calibration process, in order to determine the coefficients that best represent pavement performance over time. The two sets of performance models developed in this research are compared at both the project and network level of analysis. A decision-making framework is implemented to incorporate the two sets of models, and a comprehensive life cycle cost analysis is carried out to compare design alternatives in the project level analysis. The two model sets are also evaluated at the network level analysis using a municipal pavement management system. Two budget scenarios are executed, based on the developed performance models, and a comparison between network performance and budget spending is presented. Finally, a summary and current research contribution to the pavement industry will be presented, along with recommendations for future research

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Advanced Underground Space Technology

The recent development of underground space technology makes underground space a potential and feasible solution to climate change, energy shortages, the growing population, and the demands on urban space. Advances in material science, information technology, and computer science incorporating traditional geotechnical engineering have been extensively applied to sustainable and resilient underground space applications. The aim of this Special Issue, entitled “Advanced Underground Space Technology”, is to gather original fundamental and applied research related to the design, construction, and maintenance of underground space

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC