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

Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

This study evaluates the applicability of using a robust, novel, data-driven method in proposing surrogate models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio of coarse-grained soils using only the results of the grain size distribution analysis. The data-driven analysis has been conducted using evolutionary polynomial regression analysis (MOGA-EPR), employing a comprehensive database. The database included the particle diameter corresponding to a percentage of the passing of 10%, 30%, 50%, and 60%, coefficient of uniformity, coefficient of curvature, dry unit weight, optimum moisture content, and California bearing ratio. The statistical assessment results illustrated that the MOGA-EPR provides robust models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio. The new models’ performance has also been compared with the empirical models proposed by different researchers. It was found from the comparisons that the new models provide enhanced accuracy in predictions as these models scored lower mean absolute error and root mean square error, mean values closer to one, and higher a20−index and coefficient of correlation. Therefore, the new models can be used to ensure more optimised and robust design calculations

Modeling of unconfined compressive strength and Young's modulus of lime and cement stabilized clayey subgrade soil using Evolutionary Polynomial Regression (EPR)

In this study, the evolutionary polynomial regression (EPR) method has been employed to develop simple models with reasonable accuracy to predict the compressive strength and Young's modulus of the lime/cement stabilized clayey subgrade soil. For this purpose, the different specimens with the various cement and lime contents, at three moisture contents (dry side, wet side, and optimum moisture content) were fabricated and were cured for 7, 14, 21, 28 and, 60 days to conduct the unconfined compressive strength (UCS) test. According to the test results, a dataset consisting of 75 records for each additive was prepared. Results of this study show that the R2 value of the developed model for predicting UCS of cement-stabilized clay soil is equal to 0.96 and 0.95 for training and testing sets, respectively. These two values for lime-stabilized soil are 0.91 and 0.87, respectively. Moreover, the R2 for predicting Young's modulus of cement-stabilized clay soil is equal to 0.90 and 0.89 for training and testing set, respectively. These two values for predicting Young's modulus of lime-stabilized soil are 0.88 and 0.94, respectively. The sensitivity analysis showed that for the Portland cement stabilized clayey subgrade, the percentage of the Portland cement and moisture content are the most significant parameters for predicting the UCS and Young's modulus, respectively. In contrast, for the lime-stabilized clayey subgrade soil, the most important parameters are the moisture content and the UCS, respectively

CO2-Optimization Design of Reinforced Concrete Retaining Walls Based on a VNS-Threshold Acceptance Strategy

This paper presents one approach to a methodology to design reinforced concrete cantilever retaining walls for road construction using a hybrid multistart optimization strategic method based on a variable neighborhood search threshold acceptance strategy (VNS-MTAR) algorithm. This algorithm is applied to two objective functions: the embedded carbon dioxide (CO 2) emissions and the economic cost of reinforced concrete walls at different stages of materials production, transportation, and construction. The problem involved 20 design variables: four geometric variables (thickness of the stem and the base slab; toe and heel lengths), four material types, and 12 variables for the reinforcement setup. Results first indicate that embedded emissions and cost are closely related and that more environmentally friendly solutions than the lowest cost solution are available at a cost increment of less than 1.28%. The analysis also indicated that reducing costs by 1 Euro could save up to 2.28%kg in CO 2 emissions. Finally, the cost-optimized walls require approximately 4.8% more concrete than the best environmental ones, which need 1.9% more steel. © 2012 American Society of Civil Engineers.This work was supported by the Generalitat Valenciana (Research Project GV/2010/086) and by the Universitat Politecnica de Valencia (Research Project PAID-06-09). The authors are grateful to the anonymous reviewers for their constructive comments and useful suggestions. The authors are also grateful Dr. Debra Westall for her thorough revision of the manuscript.10000-01-01Yepes Piqueras, V.; Gonzalez Vidosa, F.; Alcalá González, J.; Villalba Izquierdo, P. (2012). CO2-Optimization Design of Reinforced Concrete Retaining Walls Based on a VNS-Threshold Acceptance Strategy. Journal of Computing in Civil Engineering. 26(3):378-386. doi:10.1061/(ASCE)CP.1943-5487.0000140S37838626

40 Years Theory and Model at Wageningen UR

"Theorie en model" zo luidde de titel van de inaugurele rede van CT de Wit (1968). Reden genoeg voor een (theoretische) terugblik op zijn wer

Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method

In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 {\deg}C. In this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydro-mechanical (THM) behavior of the bentonite, since the pre-defined functional constitutive laws often lack generality and flexibility to capture a wide range of complex coupling phenomena as well as the effects of stress state and path dependency. In this work, a deep neural network (DNN)-based soil-water retention curve (SWRC) of bentonite is introduced and integrated into a Reproducing Kernel Particle Method (RKPM) for conducting THM simulations of the bentonite buffer. The DNN-SWRC model incorporates temperature as an additional input variable, allowing it to learn the relationship between suction and degree of saturation under the general non-isothermal condition, which is difficult to represent using a phenomenological SWRC. For effective modeling of the tank-scale test, new axisymmetric Reproducing Kernel basis functions enriched with singular Dirichlet enforcement representing heater placement and an effective convective heat transfer coefficient representing thin-layer composite tank construction are developed. The proposed method is demonstrated through the modeling of a tank-scale experiment involving a cylindrical layer of MX-80 bentonite exposed to central heating.Comment: 51 pages, 19 figure

Artificial Intelligence in Civil Engineering

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering

Evaluation of Strength behaviour of Cement-RHA Stabilized and Polypropylene Fiber Reinforced Clay-Sand Mixtures

In this paper, regarding the high availability of rice husk ash (RHA) in Guilan province, also, to decrease the geo-environmental issues caused by dumping RHA in the environment, different clay-sand mixtures are stabilized using the combination of cement and RHA. Polypropylene (PP) fibers are also used to decrease the growth of tensile cracks and increase the overall strength of samples. As the main scope, effect of sand content (in different conditions: with and without presence of RHA) on the compressive strength of stabilized and reinforced samples is investigated. In this regard, 28 day cured clay-sand samples are prepared and unconfined compressive strength (UCS) tests are conducted and the results are compared. It is obtained that with addition of 20% sand to the clay samples, their UCS increases in both cases of non-RHA and RHA-stabilized samples. Moreover, such behavior has been observed with the length of studied PP fibers. As the second scope, based on the conducted UCS tests on the 7-, 28- and 90- day cured clay samples, compressive strength of non-RHA samples are almost completely achieved in a 28-day curing period, while samples containing RHA continue to strengthening after such a period toward a 90-day curing period. Next, a simple relationship for the prediction of UCS of cement-RHA stabilized and PP reinforced clay is presented based on the evolutionary polynomial regression (EPR) technique. This relationship can be efficiently applied by construction engineers to obtain the appropriate mixture design for the stabilization of clay with cement, RHA and PP fibers

Annual Report: 2008

I submit herewith the annual report from the Agricultural and Forestry Experiment Station, School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks, for the period ending December 31, 2008. This is done in accordance with an act of Congress, approved March 2, 1887, entitled, “An act to establish agricultural experiment stations, in connection with the agricultural college established in the several states under the provisions of an act approved July 2, 1862, and under the acts supplementary thereto,” and also of the act of the Alaska Territorial Legislature, approved March 12, 1935, accepting the provisions of the act of Congress. The research reports are organized according to our strategic plan, which focuses on high-latitude soils, high-latitude agriculture, natural resources use and allocation, ecosystems management, and geographic information. These areas cross department and unit lines, linking them and unifying the research. We have also included in our financial statement information on the special grants we receive. These special grants allow us to provide research and outreach that is targeted toward economic development in Alaska. Research conducted by our graduate and undergraduate students plays an important role in these grants and the impact they make on Alaska.Financial statement -- Grants -- Students -- Research reports: Partners, Facilities, and Programs; Geographic Information; High-Latitude Agriculture; High-Latitude Soils, Management of Ecosystems; Natural Resources Use and Allocation; Index to Reports -- Publications -- Facult