Browsing by Author "Recabarren, Matías"

Now showing 1 - 7 of 7
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    (33, 33) Abell, José A.; Moreno-Casas, Patricio A.; Recabarren, Matías; 33
    In an era where technology continually reshapes the landscape of professional practice, it has become relevant to equip engineering students with advanced computational skills beyond programming. This article presents a novel discipline-based framework designed to integrate advanced computational skills into engineering education. Responding to challenges such as the disconnection between computational abilities and domain-specific knowledge, and student demotivation due to overwhelming technological challenges, this study aims to validate the impact of the framework on domain learning, computational skill acquisition, and perceived future utility. Implementing a case study approach, we explore the development of high-performance computing skills within a project-based learning context in Civil Engineering. Results indicate significant improvements in students' understanding of both computational concepts and the engineering domain, evidenced by enhanced self-perception and positive Technology Acceptance Model outcomes. The framework facilitated a meaningful connection between computational skills and professional applications, as seen in students' project reflections. Despite the promising results, the necessity for instructors to possess and impart computational knowledge is highlighted as an important factor for successful integration. This study contributes to educational computing research by providing a scalable approach to embedding advanced computational skills in engineering curricula, addressing existing educational challenges, and suggesting directions for future research.
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    33
    (33, 33) Abell, José A.; Crempien, Jorge G.F.; Recabarren, Matías; 33
    ShakerMaker is an open-source python framework which simplifies the generation of synthetic broad-band seismograms, produced by finite-fault kinematic representations of earthquake ruptures, using a 1-D layered model of the crust and the frequency–wavenumber (f–k) method. It is designed to bring closer the engineering seismology and earthquake engineering communities, by catering to the earthquake simulation needs of both disciplines. One particular goal of this framework is to provide a simple way to produce high-fidelity earthquake motions for use with the domain-reduction method, simplifying the setup of physically accurate finite-element simulations of multi-scale seismological and earthquake engineering problems through the use of a new specialized file format. ShakerMaker's core is composed of a high-performance Fortran implementation of the f–k method, that is exposed to the user as a python framework. Its software architecture emphasizes simplicity, extensibility, and performance, allowing users to specify complex simulation scenarios with short scripts. The message passing interface is used to achieve scalability from simple single-processor machines to HPC clusters.
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    Challenges for computer vision as a tool for screening urban trees through street-view images
    Arevalo-Ramirez, Tito; Alfaro, Anali; Figueroa, José; Ponce-Donoso, Mauricio; Saavedra, Jose M.; Recabarren, Matías; Delpiano, José
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    Evolution of students' interaction using a gamified virtual learning environment in an engineering course
    Corvalán, Benjamín; Recabarren, Matías; Echeverría, Alejandro
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