Optimización metaheurística aplicada en la gestión de pavimentos asfálticos

Pavement engineering is a crossroads between geotechnical and transportation engineering with a sound base on construction materials. There are multiple applications of optimization algorithms in pavement engineering, emphasizing pavement management for its socioeconomic implications and back-calculation of layer properties for its complexity. A detailed literature review shows that optimization has been a permanent concern in pavement engineering. However, only in the last two decades, the increase in computational power allowed the implementation of metaheuristic optimization techniques with promising results in research and practice. Pavement management requires powerful optimization tools for multi-objective problems such as minimizing costs and maximizing the pavement state from network to project level with constrained budgets. A substantial amount of research focuses on genetic algorithms (GA), but new developments include particle intelligence (PSO, ACO, and ABC). The study must go beyond small-sized networks to improve the management of existing road infrastructure (pavement, bridges) based on mechanistic and reliability criteria.La ingeniería de pavimentos es una encrucijada entre la ingeniería geotécnica y la ingeniería de transporte con una sólida base en los materiales de construcción. Existen diferentes aplicaciones de los algoritmos de optimización en la ingeniería de pavimentos, las cuales enfatizan la gestión del pavimento por sus implicaciones socioeconómicas y el cálculo inverso de las propiedades de las capas por su complejidad. Una revisión detallada de la literatura muestra que la optimización ha sido una preocupación permanente en la ingeniería de pavimentos; sin embargo, solo en las últimas dos décadas, el incremento del poder computacional permitió la implementación de técnicas de optimización metaheurísticas con resultados prometedores en la investigación y en la práctica. La gestión del pavimento requiere poderosas herramientas de optimización para problemas con objetivos múltiples, como minimizar costos y maximizar el estado del pavimento desde el nivel de la red hasta el del proyecto con presupuestos limitados. Una cantidad sustancial de investigaciones se centra en los algoritmos genéticos (AG), pero los nuevos desarrollos incluyen inteligencia de partículas (PSO, ACO y ABC). El estudio debe ir más allá de las redes de pequeño tamaño para mejorar la gestión de la infraestructura vial existente (pavimento, puentes) con base en criterios mecanicistas y de confiabilidad

A parameter-free discrete particle swarm algorithm and its application to multi-objective pavement maintenance schemes

Regular maintenance is paramount for a healthy road network, the arteries of any economy. As the resources for maintenance are limited, optimization is necessary. A number of conflicting objectives exist with many influencing variables. Although many methods have been proposed, the related research is very active, due to difficulties in adoption to the actual practice owing to reasons such high-dimensional problems even for small road networks. Literature survey tells that particle swarms have not been exploited much, mainly due to unavailability of many techniques in this domain for multi-objective discrete problems like this. In this work, a novel particle swarm algorithm is proposed for a general, discrete, multi-objective problem. In contrast to the standard particle swarm, the bare-bones technique has a clear advantage in that it is a parameter-free technique, hence the end users need not be optimization experts. However, the existing barebones algorithm is available only for continuous domains, sans any particle velocity terms. For discrete domains, the proposed method introduces a parameter-free velocity term to the standard bare-bones algorithm. Based on the peak velocities observed by the different dimensions of a particle, its new position is calculated. A number of benchmark test functions are also solved. The results show that the proposed algorithm is highly competitive and able to obtain much better spread of solutions compared to three other existing PSO and genetic algorithms. The method is benchmarked against a number of other algorithms on an actual pavement maintenance problem. When compared against another particle swarm algorithm, it not only shows better performance, but also significant reduction in run-time compared to other POS algorithm. Hence, for large road network maintenance, the proposed method shows a lot of promise in terms of analysis time, while improving on the quality of solutions

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

An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability

Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

The blue monkey: A new nature inspired metaheuristic optimization algorithm

This paper introduces a study to Blue Monkey (BM) algorithm, which is a new metaheuristic algorithm optimization based on the performance of blue monkey swarms in nature. The BM algorithm identifies how many males in one group. Normally, outside the season of the breeding, the groups of blue monkeys have only one adult male like other forest guenons. In addition to related patas monkeys (Erythrocebus patas). Forty-three of well-known test functions, which used in the area of optimization are used as benchmark to check BM algorithm, in addition, BM verified by a comparative performance check with Artificial-Bee-Colony (ABC), Gravitational Search Algorithm (GSA), Biogeography-Based Optimizer (BBO), and Particle Swarm Optimization (PSO). The obtained results demonstrated that BM algorithm is competitive compared with the selected metaheuristic algorithms; also, BM is able to converge towards the global optimal through optimization problems. Further, this algorithm is very efficient in field of dissolving real problems with restrictions and unidentified search space. It should be mentioned that the BM algorithm has some variables and it can obtain better results. in many test functions comparing with other algorithms