1,721,046 research outputs found
Diagnostic and Proof Load Tests on Bridges
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Diagnostic and Proof Load Tests on Bridges
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Direct Structural Damping Identification Method
No full textAll structures exhibit some form of damping, but despite a large literature on the damping, it still remains one of the least well-understood aspects of general vibration analysis. The synthesis of damping in structural systems and machines is extremely important if a model is to be used in predicting vibration levels, transient responses, transmissibility, decay times or other characteristics in design and analysis that are dominated by energy dissipation. In this paper, a new structural damping identification method is proposed. The proposed structural damping identification is a direct method and requires prior knowledge of accurate mass and stiffness matrices. The proposed method doesn’t require initial damping estimates. The effectiveness of the proposed structural damping identification method is demonstrated by numerical and experimental studies. Firstly, a numerical study is performed using lumped mass system. The numerical study is followed by a case involving actual measured data of cantilever beam structure. The results have shown that the proposed structural damping identification method can be used to derive accurate model of the system. This is illustrated by matching of the complex FRFs obtained from the analytically damped model with that of experimental data.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Signal Processing for Sensing and Monitoring of Civil Infrastructure Systems
No full text‘It is the year 2025 and I am compiling this article for an instant VPD (videopod) that is streamed over the world. An EESR (Educational Expert Service Request) came from an empathetic computer HIAS (Hi, I am Sam) that matched my qualifications with a quest by online activists SFT (Searching for Truth) to examine global interactions in education. This online SFT think tank is examining brilliance in action with ideas generated through WCN (wireless communications networks) in their brains. I have consulted and updated my IM (I am) virtual self that contains my visual image and bodily movements with facial expressions, having internalized video images with my values and actions, and monitored my biological rhythms. My IM will present my best contemporary self via a virtual social network system with a database of my past interactions and intelligent decisions. I have spoken certain words: gifted students; global issues; sustainability; social change, etc. The intelligent search site has screened millions of information bits from journal articles, research studies, multimedia presentations and contemporary thought; related this to my previous compilations; compared this with other expert trends in thoughts and compiled my VPD. My global (and galactic) audience is instantaneous and can drop in at any time to request a chat with their IM or add new information to the compilation or a TW (transformational WIKI). I link this to my virtual families with simultaneous translations into other ethnic languages and send the link to my authentic family connections on four continents. Join in this virtual knowledge conversation, recreated constantly. Here it is…’
Monitoring of a Movable Bridge Mechanical Components for Damage Identification using Artificial Neural Networks
No full textThis paper presents a review of the results of a structural health monitoring (SHM) study to track the performance of a gearbox and rack-pinion of an operating movable bridge. These mechanical components are critical parts of bascule type bridges and damage of these components need to be identified and diagnosed, since an early detection of faults may help to avoid major damage to the structure and also avoid unexpected bridge closures. The prediction of the gearbox and rack-pinion fault detection is carried out with artificial neural networks (ANN) using the time domain vibration signals. Several statistical parameters are selected as characteristic features of the time-domain vibration signals. Monitoring data is collected during regular opening and closing of the bridge, as well as during artificially induced damage conditions. The results indicate that the vibration monitoring data, with selected statistical parameters and particular network architecture, give good results to predict the undamaged and damaged condition of the bridge
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