CARLOS HERNANDO HIGUERA SANDOVAL

@uptc.edu.co

ESCUELA DE TRANSPORTE Y VÍAS - FACULTAD DE INGENIERÍA
UNIVERSIDAD PEDAGÓGICA Y TECNOLÓGICA DE COLOMBIA

CARLOS HERNANDO HIGUERA SANDOVAL


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https://researchid.co/carloschhs

RESEARCH INTERESTS

Diseño de estructuras de pavimentos, Evaluación y Rehabilitación de estructuras de pavimentos, Mezclas asfálticas, Caracterización de asfalto, Materiales para carreteras, Mecánica de pavimentos, Modelos de fatiga y ahuellamiento de mezclas asfálticas, Construcción y mantenimiento de carreteras.
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Scopus Publications

Scopus Publications

  • Susceptibility to moisture damage in asphalt mixes with blast furnace dust as aggregate
    Ricardo Ochoa-Díaz, Gloria Elizabeth Grimaldo-León, Carlos Hernando Higuera Sandoval
    Dyna Colombia, 2024
    One of the ongoing challenges in engineering is mitigating the degradation of road infrastructure caused by water in asphalt mixes. This study aims to assess the impact of blast furnace dust, a byproduct of the steel manufacturing process, on moisture-induced damage in asphalt mixes. Three asphalt mixes were developed following the Marshall methodology: one comprising conventional crushed aggregates, while the others substituted 50% and 100% of the conventional fine aggregate with blast furnace dust. Material characterization procedures and chemical analysis of the blast furnace dust were conducted. Once compliance with the specified material requirements was verified and analyzed, water susceptibility was evaluated through an indirect tensile test across various void content levels. Similarly, leveraging the Superpave gyratory compactor, several compaction indices were determined to estimate the compaction behavior of each mix. Based on the findings, the inclusion of blast furnace dust in an asphalt mix proved satisfactory due to its contributions in enhancing tensile strength, consequently leading to a reduction in moisture-induced damage within the asphalt mix, as well as exhibiting improved compaction behavior. Additionally, this utilization contributed to diminishing the environmental impact linked to the steel production process, where substantial quantities of this residue accumulate.
  • Effect of the grain size of recycled rubber on the behaviour of an asphalt mix
    R. Ochoa Díaz, C.H. Higuera Sandoval
    Revista Ingenieria De Construccion, 2023
    La eliminación de una gran cantidad de desechos como plástico, botellas, llantas, etc., que se generan en grandes cantidades y producen un impacto y riesgo ambiental en las zonas donde se producen y almacenan. El presente estudio pretende utilizar el grano de caucho reciclado (GCR), proveniente de las llantas desechadas, en la fabricación de concreto asfáltico. Se diseñaron siete mezclas utilizando la metodología Marshall, una mezcla sin la adición de grano de caucho que será la mezcla base de comparación y seis mezclas con adición de 1% de grano caucho de diferentes tamaños, los cuales oscilan entre pasa tamiz de 4.76 mm (No. 40) y retenido tamiz de 0.075 mm (No. 200). Una vez determinadas las respectivas fórmulas de trabajo se realizaron pruebas de desempeño como: susceptibilidad al daño por humedad, resistencia a la deformación plástica, módulo resiliente, resistencia a la fatiga y resistencia al deslizamiento a cada una de las mezclas. Los resultados demuestran que la incorporación de grano de caucho produce en algunos casos una leve disminución en el contenido óptimo de cemento asfáltico, incremento en la estabilidad Marshall, mejora de la deformación plástica, incremento en la resistencia, mejor vida a la fatiga en comparación con la mezcla convencional. Los resultados de las pruebas de laboratorio indican que, al utilizar grano de caucho reciclado, se pueden obtener concretos asfálticos con mejores características técnicas requeridas y se pueden construir un pavimento amigable con el medio ambiente.
  • Multiagent Reinforcement Learning Applied to Traffic Light Signal Control
    Carolina Higuera, Fernando Lozano, Edgar Camilo Camacho, Carlos Hernando Higuera
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
    We present the application of multiagent reinforcement learning to the problem of traffic light signal control to decrease travel time. We model roads as a collection of agents for each signalized junction. Agents learn to set phases that jointly maximize a reward function that encourages short vehicle queuing delays and queue lengths at all junctions. The first approach that we tested exploits the fact that the reward function can be splitted into contributions per agent. Junctions are modeled as vertices in a coordination graph and the joint action is found with the variable elimination algorithm. The second method exploits the principle of locality to compute the best action for an agent as its best response for a two player game with each member of its neighborhood. We apply the learning methods to a simulated network of 6 intersections, using data from the Transit Department of Bogota, Colombia. These methods obtained significant reductions in queuing delay with respect to the fixed time control, and in general achieve shorter travel times across the network than some other reinforcement learning based methods found in the literature.
  • Demonstration of Multiagent Reinforcement Learning Applied to Traffic Light Signal Control
    Carolina Higuera, Fernando Lozano, Edgar Camilo Camacho, Carlos Hernando Higuera
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
    We present a demonstration of two coordination methods for the application of multiagent reinforcement learning to the problem of traffic light signal control to decrease travel time. The first approach that we tested exploits the fact that the reward function can be splitted into contributions per agent. The second method computes the best response for a two player game with each member of its neighborhood. We apply both learning methods through SUMO traffic simulator, using data from the Transit Department of Bogota, Colombia.