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An Irish Rail intercity train was instrumented for a period of one month with inertial sensors. In this paper, a novel calibration algorithm is proposed to determine, with reasonable accuracy, vehicle model parameters from the measured vehicle response data. Frequency domain decomposition (FDD) is used to find the dominant frequencies in the captured data. Randomly chosen 2 km data segments are chosen from a number of datasets, thereby averaging out the effects of variations in track longitudinal profile, track stiffness, signal noise and other unknowns. The remaining dominant peaks are taken to be vehicle frequencies. An optimization technique known as Cross Entropy is used to find vehicle mass and stiffness properties that best match modal vehicle eigenfrequencies identified in the frequency analysis. Finally, the calibrated vehicle is run over a measured track profile and the resulting model output is compared to measured data to validate the results. Key words: calibration, measurement, frequency domain decomposition, railway vehicle dynamics, cross entropy, optimization. Un train interurbain Irish Rail a ete instruments avec des capteurs inertiels pendant un mois. Dans le present document, un nouvel algorithme d'etalonnage est propose pour determiner, avec une precision raisonnable, les parametres de modelisation de vehicule a partir des donnees mesurees de reponse du vehicule. La decomposition du domaine des frequences (<< FDD >>) est utilisee pour trouver les frequences dominantes dans les donnees recueillies. Les segments de donnees sur 2 km choisis au hasard sont selectionnes a partir d'un certain nombre d'ensembles de donnees, ce qui permet d'obtenir une moyenne des effets des variations du profil longitudinal de la voie, de la rigidite de la voie, du bruit des signaux et d'autres inconnues. Les pics dominants restants sont consideres comme des frequences de vehicule. Une technique d'optimisation connue sous le nom d'entropie croisee (<< cross entropy >>) est utilisee pour trouver les proprietes de masse et de rigidite de vehicule qui correspondent le mieux aux frequences propres du vehicule identifiees dans l'analyse modale de frequences. Enfin, le vehicule etalonne passe sur un profil de voie mesure et les donnees du modele sont comparees aux donnees mesurees afin de valider les resultats. [Traduit par la Redaction] Mots-cles : etalonnage, mesure, decomposition du domaine des frequences, dynamique des vehicules ferroviaires, entropie croisee, optimisation., 1. Introduction Measurement of the inertial response of in-service trains has the potential to provide real-time railway track condition information to owners of the track asset. The increase in the [...]

As the world's transport infrastructure ages, the importance of bridge condition monitoring is becoming increasingly acknowledged. Large-scale deployment of existing inspection and monitoring techniques is infeasible due to cost and logistical challenges. The concept of using sensors located within vehicles for low cost 'drive-by' monitoring has become the focus of much attention in recent years. This paper presents a new data-driven approach for drive-by bridge monitoring. Machine learning techniques are leveraged to allow the influence of vehicle speed to be considered and the Operating Deflection Shape Ratio (ODSR) is presented as an alternative damage-sensitive feature to the commonly used frequency spectrum. Extensive laboratory experiments demonstrate that the method is capable of detecting midspan cracking and seized bearings. A statistical classification approach is adopted to classify damage indicators as either 'damaged' or 'healthy'. Classification accuracy is seen to vary between 65-96% and is similar whether using the frequency spectrum or ODSR. Based on the results of the laboratory testing, it is expected that this approach could be implemented on a large scale to act as an early warning tool for infrastructure owners to identify bridges presenting signs of distress or deterioration. [ABSTRACT FROM AUTHOR]

Students' engagement is a fundamental challenge in large classrooms in higher education. In recent years, innovative technologies such as electronic learning and online polling platforms have made learning more engaging, effective, and interactive. By using these platforms, educators can create more inclusive and enriching learning environments. This paper presents a novel approach in which an online technology is employed to enhance students' learning experience. In this approach, features of an online polling platform, i.e., Slido, are employed to enhance students' engagement in an engineering module, i.e., 'Mechanics of Solids', which is recognised as a fundamentally challenging module with difficult subjects. This study investigates how the interactive features of such technologies, such as real-time polls, question and answer (Q&A) sessions, and quizzes, can provide a more active and practical learning environment by improving student engagement in the classroom. In total, six online polls were designed: one for ice-breaking, two on the topics of shear forces and bending moment, two on stresses, and one on deflection. Each poll was presented to the students, and they participated in them by scanning a QR code or typing the poll's code online. The rate of students' participation in polls is extensively discussed to show the effectiveness of the proposed method. The findings of this study show a significant increase in student participation in classroom activities compared to traditional methods. Student feedback also indicates a positive learning experience with the use of the proposed approach. It is shown that the proposed approach has the potential to transform the way engineering students engage with challenging subjects, leading to enhanced learning outcomes and a more positive learning experience. [ABSTRACT FROM AUTHOR]

Simplified methods are often used for load estimations during the initial design of the foundations of offshore wind turbines (OWTs). However, the reliability of simplified methods for designing different OWTs needs to be studied. This paper provides a comparative study to evaluate the reliability of simplified approaches. The foundation loads are calculated for OWTs at the mudline level using a simplified approach and OpenFAST simulations and compared. Three OWTs, NREL 5 MW, DTU 10 MW, and IEA 15 MW, are used as reference models. An Extreme Turbulence Model wind load at a rated wind speed, combined with a 50-year Extreme Wave Height (EWH) and Extreme Operating Gust (EOG) wind load and a 1-year maximum wave height are used as the load combinations in this study. In addition, the extreme loads are calculated using both approaches for various metocean data from five different wind farms. Further, the pile penetration lengths calculated using the mudline loads via two methods are compared. The results show that the simplified method provides conservative results for the estimated loads compared to the OpenFAST results, where the extent of conservativism is studied. For example, the bending moment and shear force at the mudline using the simplified approach are 23% to 69% and 32% to 53% higher compared to the OpenFAST results, respectively. In addition, the results show that the simplified approach can be effectively used during the initial phases of monopile foundation design by using factors such as 1.5 and 2 for the shear force and bending moment, respectively. [ABSTRACT FROM AUTHOR]