3. Producción
Browse
5 results
Search Results
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Designing valid humanitarian logistics scenario sets: Application to recurrent Peruvian floods and earthquakes(IGI Global, 2020-09-18)Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Designing valid humanitarian logistics scenario sets: application to recurrent Peruvian floods and earthquakes(IGI Global, 2021-05-28)Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluación de la vulnerabilidad sísmica del puente de carretera "Moche" ubicado en la carretera nacional PE1N usando el método de espectro de capacidad(Universidad Nacional de Ingeniería, 2022-08-08)In active seismic zones, such as the Peruvian coast, tools for predicting the potential negative impacts of earthquakes are essential for planning mitigation, emergency, and recovery of transportation facilities. For instance, the capacity of a road bridge network after a seismic event to carry traffic flow depends on the degree of expected damage and the related repair costs and downtime. This paper determines the degree of vulnerability of highway bridges to the action of probable earthquakes during the structure's lifetime by applying the capacity spectrum assessment method known as FRACAS (FRAgility through CApacity Spectrum assessment). This approach allows fragility curves to be obtained from analyzing a structure subjected to a series of seismic records of different characteristics. In this way, the method can explain the effect of variability in seismic demand and structural characteristics on the damage statistics simulated for the type of structure and evaluate the associated uncertainty in predicting fragility. As a case study, it is applied the methodology described in the evaluation of the Moche bridge, located in the region of La Libertad (Peruvian coast), which is in an Operational damage state according to an in situ inspection. From the seismic vulnerability assessment, it is obtained that until earthquakes with accelerations of 1.7g, the Moche bridge has a 60% probability of incursion into a Life Safety damage state, being the probability of incursion into a Collapse damage state less than 10%. This methodology is recommended to determine the reliability of bridges, considering the cumulative damage due to probable seismic events during the bridges' lifetime. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data driven identification and current control on dual active bridge systems for low power applications(2026-07-01)This work presents a data-driven methodology for the identification and control of Dual Active Bridge (DAB) DC–DC converters, aimed at reducing the complexity associated with highly nonlinear analytical models. By exploiting an angle phase-shift representation instead of a detailed PWM-based model, the proposed approach simplifies both system excitation and identification while preserving the essential power-transfer dynamics of the converter. A frequency-domain control strategy is subsequently developed based on the identified model, enabling systematic loop-shaping and robust controller design. The resulting discrete-time controller achieves accurate reference tracking and stable regulation in simulation tests, demonstrating that the proposed pipeline provides an effective and practical alternative to conventional model-based control approaches for DAB systems Key words. System identification, non lineal systems, dual active bridge, data-driven control. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Structural Damage Detection Using an Unmanned Aerial Vehicle-Based 3D Model and Deep Learning on a Reinforced Concrete Arch Bridge(Multidisciplinary Digital Publishing Institute (MDPI), 2025)Visual inspection is a common method for detecting structural damage, but has limitations in terms of subjectivity, time, and access. This research proposes an innovative approach to identify cracks using a 3D model generated from photographs of an unmanned aerial vehicle (UAV) and the use of a convolutional neural network (CNN). These networks are effective in detecting complex patterns, improving the accuracy and efficiency of damage identification based on simple visual inspection. The case study is the old Villena Rey bridge in Lima, Peru. The methodology covers (i) the development of a 3D model of the bridge structure, (ii) the extraction of photographs of the model and its binary segmentation, (iii) the application of deep learning through the training and testing phase of a CNN to achieve crack detection in photographs, and (iv) damage location within the 3D model. An 88.4% accuracy was achieved in crack detection, identifying 18 damage points, of which 3 turned out to be false positives. Additionally, it was determined that the left pillar in the southern area of the bridge presented the highest concentration of damage, which underlines the effectiveness of the method used.1