Effect of Vinyl Flooring on the Modal Properties of a Steel Footbridge

Damping ratios associated with non structural elements play an important role in mitigating the pedestrian-induced vibrations of slender footbridges. In particular, this paper analyses the effect of vinyl flooring on the modal parameters of steel footbridges. Motivated by the unexpected high experimental damping ratios of the first vibration modes of a real footbridge, whose deck was covered by a vinyl flooring, this paper aims at assessing more accurately the experimental damping ratios generated by this non-structural element on steel footbridges. For this purpose, a laboratory footbridge was built and vinyl flooring was installed on it. Its numerical and experimental modal parameters without and with the vinyl flooring were determined. The operational modal analysis method was used to estimate experimentally the modal parameters of the structure. The damping ratios associated with the vinyl flooring were obtained via the substraction between the experimental damping ratios of the laboratory footbridge with and without the vinyl flooring. An average increase of the damping ratios of 2.069% was observed due to the vinyl flooring installed. According to this result, this type of pavement may be a useful tool to significantly increase the damping ratios of steel footbridges in order to reduce pedestrian-induced vibrationsMinisterio de Economía y Competitividad DPI2014-53947-RFondo Europeo de Desarrollo Regional DPI2014-53947-

Vibration Isolation and Alignment of Multiple Platforms on a Non-Rigid Supporting Structure

Acknowledgments: The authors would like to thank the editors and anonymous reviewers for their useful comments Funding: This work was funded by the University of Exeter (UoE), and the College of Engineering, Mathematics, and Physical Sciences (CEMPS).Peer reviewedPublisher PD

Self-control of a lively footbridge under pedestrian flow

Congreso celebrado en la Escuela de Arquitectura de la Universidad de Sevilla desde el 24 hasta el 26 de junio de 2015.In this paper, a case study about a lively footbridge is developed; the vibration levels caused by the pedestrian action are controlled by the change of the modal parameters of the structure due to the pedestrian-structure interaction. A detailed finite element model of the structure has been updated from an operational modal analysis. The updated model has been used to obtain the numerical acceleration at the mid-span of the footbridge under different pedestrian flows. A relation between the maximum acceleration and the pedestrian density on the deck has been obtained numerically, pointing out an improvement in the comfort level of the structure when the number of pedestrians increases. This result validates a design rule for cable-stayed footbridges in order to avoid vibratory problems, since the first vertical natural frequency of the structure remains below the range that characterizes the pedestrian walking action

Accurate Long-term Air Temperature Prediction with a Fusion of Artificial Intelligence and Data Reduction Techniques

In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and C\'ordoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and C\'ordoba regions.Comment: 33 pages, 14 figures, 7 tables, under revie

Mathematical optimization for planning and design of cycle paths

[EN] The daily need for citizens to move for different activities, whatever its nature, has been greatly affected by the changes. The advantages resulting from the inclusion of the bicycle as a mode of transport and the proliferation of its use among citizens are numerous and extend both in the field of urban mobility and sustainable development.Currently, there are a number of programs for the implementation, promotion or increased public participation related to cycling in cities. But ultimately, each and every one of these initiatives have the same goal, to create a mesh of effective, useful and cycling trails that allow the use of bicycles in preferred routes with high guarantees of security, incorporating bicycle model intermodal urban transport.With the gradual implementation of bike lanes, many people have begun to use them to get around the city. But everything again needs a period of adaptation, and the reality is that the road network for these vehicles is full of obstacles to the rider. The current situation has led to the proposal that many kilometers of cycle paths needed to supply the demand of this mode of transport and, if implemented and planned are correct and sufficient.This paper presents a mathematical programming model for optimal design of a network for cyclists is presented. Specifically, the model determines a network of bicycle infrastructure, appropriate to the characteristics of a network of existing roads.As an application of the proposed model, the result of these experiments give a number of useful conclusions for planning and designing networks of cycle paths from a social perspective, applied to the case in the city of Malaga.Liñán Ruiz, R.; Pérez Aracil, J.; Cabrera Cañizares, V. (2016). Mathematical optimization for planning and design of cycle paths. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1813-1819. https://doi.org/10.4995/CIT2016.2015.4089OCS1813181

Design and evaluation of an educational platform for implementing and testing bilateral control algorithms

This paper describes the design and evaluation of a new platform created in order to improve the learning experience of bilateral control algorithms in teleoperation. This experimental platform, developed at Universidad Politécnica de Madrid, is used by the students of the Master on Automation and Robotics in the practices of the subject called “Telerobotics and Teleoperation”. The main objective is to easily implement different control architectures in the developed platform and evaluate them under different conditions to better understand the main advantages and drawbacks of each control scheme. So, the student’s tasks are focused on adjusting the control parameters of the predefined controllers and designing new ones to analyze the changes in the behavior of the whole system. A description of the subject, main topics and the platform constructed are detailed in the paper. Furthermore, the methodology followed in the practices and the bilateral control algorithms are presented. Finally, the results obtained in the experiments with students are also shown

Design and experimental characterization of a novel passive magnetic levitating platform

This work proposes a novel contactless vibration damping and thermal isolation tripod platform based on Superconducting Magnetic Levitation (SML). This prototype is suitable for cryogenic environments, where classical passive, semi active and active vibration isolation techniques may present tribological problems due to the low temperatures and/or cannot guarantee an enough thermal isolation. The levitating platform consists of a Superconducting Magnetic Levitation (SML) with inherent passive static stabilization. In addition, the use of Operational Modal Analysis (OMA) technique is proposed to characterize the transmissibility function from the baseplate to the platform. The OMA is based on the Stochastic Subspace Identification (SSI) by using the Expectation Maximization (EM) algorithm. This paper contributes to the use of SSI-EM for SML applications by proposing a step-by-step experimental methodology to process the measured data, which are obtained with different unknown excitations: ambient excitation and impulse excitation. Thus, the performance of SSI-EM for SML applications can be improved, providing a good estimation of the natural frequency and damping ratio without any controlled excitation, which is the main obstacle to use an experimental modal analysis in cryogenic environments. The dynamic response of the 510 g levitating platform has been characterized by means of OMA in a cryogenic, 77 K, and high vacuum, 1E-5 mbar, environment. The measured vertical and radial stiffness are 9872.4 N/m and 21329 N/m, respectively, whilst the measured vertical and radial damping values are 0.5278 Nm/s and 0.8938 Nm/s. The first natural frequency in vertical direction has been identified to be 27.39 Hz, whilst a value of 40.26 Hz was identified for the radial direction. The determined damping values for both modes are 0.46% and 0.53%, respectively.Ministerio de Economía y Competitivida

Eliminating stick-slip vibrations in drill-strings with a dual-loop control strategy optimized by the CRO-SL algorithm

Funding: This work was partially supported by the Spanish Ministerial Commission of Science and Technology (MICYT) through project number TIN2017-85887-C2-2-P Acknowledgments: The authors would like to thank Marian Wiercigroch and Vahid Vaziri from the Centre for Applied Dynamics Research, University of Aberdeen, for providing the realistic drill-string parameters used in this work.Peer reviewedPublisher PD

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

One month in advance prediction of air temperature from Reanalysis data with eXplainable Artificial Intelligence techniques

In this paper we have tackled the problem of long-term air temperature prediction with eXplainable Artificial Intelligence (XAI) models. Specifically, we have evaluated the performance of an Artificial Neural Network (ANN) architecture with sigmoidal neurons in the hidden layer, trained by means of an evolutionary algorithm (Evolutionary ANNs, EANNs). This XAI model architecture (XAI-EANN) has been applied to the long-term air temperature prediction at different sub-regions of the South of the Iberian Peninsula. In this case, the average August air temperature has been predicted from ERA5 Reanalysis data variables, obtaining good predictions skills and explainable models in terms of the input climatological variables considered. A cluster analysis has been first carried out in terms of the average air temperature in the zone, in such a way that a number of sub-regions with different air temperature behaviour have been defined. The proposed XAI-EANN model architecture has been applied to each of the defined sub-regions, in order to find significant differences among them, which can be explained with the XAI-EANN models obtained. Finally, a comprehensive comparison against some state-of-the-art techniques has also been carried out, concluding that there are statistically significant differences in terms of accuracy in favour of the proposed XAI-EANN model, which also benefits from being an XAI model