30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Improved Vehicle-Bridge Interaction Modeling and Automation of Bridge System Identification Techniques

The Federal Highway Administration (FHWA) recognizes the necessity for cost-effective and practical system identification (SI) techniques within structural health monitoring (SHM) frameworks for asset management applications. Indirect health monitoring (IHM), a promising SHM approach, utilizes accelerometer-equipped vehicles to measure bridge modal properties (e.g., natural frequencies, damping ratios, mode shapes) through bridge vibration data to assess the bridge\u27s condition. However, engineers and researchers often encounter noise from road roughness, environmental factors, and vehicular components in collected vehicle signals. This noise contaminates the vehicle signal with spurious modes corresponding to stochastic frequencies, impacting damage monitoring assessments. Thus, an efficient and reliable SI technique is required to process vehicle signals and extract bridge features effectively before practical deployment. To achieve this, vehicle-bridge interaction (VBI) models are often developed to simulate physical data for either initial verification of SI methodologies or for use in a model-updating algorithm to determine the bridge modal properties by tuning the model to the physical data. Common steps in the SI process include signal processing of the raw data, operational modal analysis (OMA), and leveraging machine learning (ML). This dissertation proposes a framework for efficient creation of VBI models using commercial code, develops an autonomous SI technique (APPVMD) to extract bridge frequencies from passing vehicles, provides guidelines for improving bridge frequency extraction with multi-vehicle scenarios via an extensive analytical study, demonstrates the need for improved methodologies for simulating road surface roughness effects in VBI models via comparison with physical data, and provides a substantial archive of test data and models that can be leveraged in future studies (road surface profiles using laser profilometers and vehicle acceleration data from four-post shaker testing with the associated vehicle model). The work encompasses four major studies aimed at achieving these research objectives. The first study presents a computationally efficient VBI modeling framework in commercial finite element (FE) software (Abaqus) requiring minimal user coding, suitable for industrial and research communities. The framework\u27s dynamic response is verified using literature data, and a damage modeling methodology is proposed to extend the framework to SHM applications with SI techniques in IHM. In the second study, an autonomous peak-picking variational mode decomposition (APPVMD) framework is introduced to enhance scalability in SI techniques for the IHM of a bridge network. APPVMD leverages signal processing techniques and heuristic models to autonomously extract bridge frequencies from vehicle acceleration responses without prior information or model-informed training. The framework is tested on different vehicles and bridge classes to assess its feasibility, achieving successful bridge frequency extractions in many cases. In the third study, an extensive parametric study is undertaken to determine if multiple vehicle scenarios would enhance bridge frequency identification and what vehicle types and driving speeds would be most effective. Four vehicles are considered representative of true vehicle properties found in the literature, and six bridges are taken from physical bridge data, including drawings obtained from both the literature and the South Carolina Department of Transportation (SCDOT). The study unveils interesting phenomena regarding the complex interaction between vehicles and bridges, performs brief case studies to improve bridge frequency extractions further, and proposes guidelines that researchers and engineers can follow when preparing to collect acceleration data from vehicles for bridge SI. The fourth study presents preliminary work to experimentally show that current methodologies for representing road surface roughness effects are insufficient. First, a vehicle model is developed to include road surface roughness effects and compared with experimental data collected in a previous study at Clemson University. The results suggest that commonly referenced roughness factors in the literature underestimate road surface roughness effects while inputting the average values based on road class from the ISO-8608 standard tends to exaggerate road surface roughness effects. A moving-average filter (MAF) was found to help attenuate noise but requires appropriate parameter selection. Recommendations for improving road surface roughness modeling in VBI problems are provided. Further work is conducted on a BMW 535 Xi with enhanced ride quality, including verification exercises using a four-post shaker and extensive road tests for real-life road roughness measurements during driving. The study concludes with the suggested path forward for utilizing collected data. It suggests additional tests that can unveil the tire behavior during road tests to compute the transfer function between the road surface roughness and the unsprung masses in VBI models. This dissertation concludes by summarizing the contributions made to the field IHM of bridges and outlines the next steps for future research

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version