Robot Structural Analysis Tutorials

[Ceumar Pee]

Yeahrecently I've bought some tutorials from these guys, one for Revit Structure an another for Robot Structure for steel structures. First let me tell that some tutorials are more suitable if you don't have any previous knowledge of the software, for example the one for Revit Structure it's better if you don't have a lot of knowledge about Revit as a whole. I worked with Revit some years ago, but after moving to Advance Steel for a while I kinda forgot most of Revit's work-flow, so at least this first tut got me in the right path...

The other for Robot was quite helpful as I'm actually new to the software, and I actually wasn't sure what was the workflow e.g. when to assign the code properties to bars, when to define loads etc., specially the part of the code was kinda confusing to me, so it gave me some crystal clear idea of what steps should I follow.

Well, why should we give 60$ to them, better to give you cca half the price. In that way you will refund some many and we get tutorial at cheaper price. If you are interested contact me to milosi...@ymail.com

Are you kidding me? I've checked out the tuts down at Udemy, and no disrespect to the author but he treats RSA like drafting software which is not! After what happened to the Miami surfside building and multiple other bridges across the USA that report structural damage due to poor design, it gets to my nerves seeing how somebody thinks is a qualified structural engineer just because he can click some icons in a structural analysis software, all in all, if you are serious about engineering and respect your own career stay away from Udemy.

I am new to Autodesk Robot. I realize Robot has better area (shell) meshing capabilities than most other strucutral analysis packages. I am wondering if there is a good tutorial video that outlines the steps from importing a curved surface mass object from Revit into Robot and then meshing it?

I have modelled a dome structure and a pipe sturcutre. I have to coombine them structurally and then create a structural analysis mesh. Attached is my attempt of modelling the structure, but I have no luck object-combining the pipe and the dome. The final product should be a pipe (chimney if you will) sitting on top of the dome. Is there a good isntructional video on how to merge/combine the pipe with the dome and then meshing the whole structure for structural analysis. Basically I am trying to make use of the curved surface meshing capabilities of Robot which seems to not be found in most other sturctural analysis program. My intent is to export the mesh to a .dxf. Can you perhaps create a an intructional video/screencast or point me to a relevant tutorial video?

The objective of this workshop is to introduce participants to ideas and solutions for improved alarm management based on seamless integration of information from process and alarm databases complemented with process connectivity information. Process-data based alarm system design aims at obtaining optimal alarm parameters for filters, deadbands, delay timers, and alarm limits, based on evaluation metrics, including alarm detection delay and false and missed alarm rates. The advanced alarm analytics tools that will be presented at this workshop are able to detect nuisance alarms and discover hidden patterns from alarm and event historian using statistical learning and data mining approaches. Historical datasets combined with process topology information make it possible to capture propagation paths of abnormalities and thus can help with root cause analysis.

The focus of this workshop is to present recent advances and new techniques of industrial alarm management using sensor and alarm data analytics. The emphasis in this workshop will be on how to conduct advanced data analytics to extract useful in-formation from data to help in designing optimal alarm systems, finding out problems, and discovering hidden patterns. Interesting topics covered in this workshop include correlated alarms, alarm floods, alarm system design, causality inference, root cause analysis, and visualization.

The intended audience for this workshop would be industrial practitioners working on real alarm managing problems, vendors designing alarm systems, researchers studying advanced alarm management solutions, graduate students with interests in data science and its application to solve industrial problems.

Sirish L. Shah has been with the University of Alberta since 1978, where he held the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 2000 to 2012. He is the recipient of the Albright & Wilson Americas Award of the Canadian Society for Chemical Engineering (CSChE) in 1989, the Killam Professor in 2003, the D.G. Fisher Award of the CSChE for significant contributions in the field of systems and control, the ASTECH award in 2011 and the 2015-IEEE Transition to Practice award. The main areas of his current research are process and performance monitoring, analysis and rationalization of alarm systems. He has co-authored three books, the first titled, Performance Assessment of Control Loops: Theory and Applications, a second titled Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches, and a more recent monograph on Capturing Connectivity and Causality in Complex Industrial Processes. He is Emeritus Professor at the University of Alberta, a Fellow of the Canadian Academy of Engineering and the Chemical Institute of Canada.

Tongwen Chen is presently a Professor and Tier 1 Canada Research Chair in Intelligent Monitoring and Control in the Department of Electrical and Computer Engineering at the University of Alberta, Edmonton, Canada. He received the B.Eng. degree in Automation and Instrumentation from Tsinghua University (Beijing) in 1984, and the M.A.Sc. and Ph.D. degrees in Electrical Engineering from the University of Toronto in 1988 and 1991, respectively. His research interests include computer and network based control systems, process safety and alarm systems, and their applications to the process and power industries. He has served as an Associate Editor for several international journals, including IEEE Transactions on Automatic Control and Automatica. He is a Fellow of IEEE, IFAC, as well as the Canadian Academy of Engineering.

Masaru Noda is a professor in Department of Chemical Engineering at Fukuoka University, Japan. He received the B.Eng., M.Eng., and Ph.D. degrees in Chemical Engineering from Kyoto University in 1994, 1996 and 2000, respectively. His main research focus is on plant operational data analysis for safe process operation.

Jiandong Wang is presently a full professor of College of Electrical Engineering and Automation at the Shandong University of Science and Technology, Qingdao, Shandong Province, China. He received a B.E. in automatic control from Beijing University of Chemical Technology, Beijing, China, in 1997, and an M.Sc and Ph.D. in Electrical and Computer Engineering from the University of Alberta, Canada, in 2003 and 2007, respectively. From 1997 to 2001, he was a Control Engineer with the Beijing Tsinghua Energy Simulation Company, Beijing, China. From February 2006 to August 2006, he was a Visiting Scholar at the Department of System Design Engineering at the Keio University, Japan. From December 2006 to October 2016, he was an assistant/associate/full Professor with the College of Engineering, Peking University, China.

Fan Yang received the B.Eng. degree in Automation and the Ph.D. degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2002 and 2008, respectively. After working as a Postdoctoral Fellow with Tsinghua University and the University of Alberta, he joined the Department of Automation, Tsinghua University in 2011, where he is currently an Associate Professor. His research interests include topology modeling of large-scale processes, abnormal events monitoring, process hazard analysis, and smart alarm management. He was a recipient of the Young Research Paper Award from the IEEE Control Systems Society Beijing Chapter in 2006, the Outstanding Graduate Award from Tsinghua University in 2008, the Science and Technology Progress Award from the Chinese Association of Automation in 2018, and the Teaching Achievement Awards from Tsinghua University in 2012, 2014, and, 2016 and from the Chinese Association of Automation in 2016.

Wenkai Hu received the B.Eng. and M.Sc. degrees in Power and Mechanical Engineering from Wuhan University, Wuhan, Hubei, China, in 2010 and 2012, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the University of Alberta in 2016. He worked as a Post-Doctoral Fellow from Oct. 2016 to Sep. 2018, and a Research Associate from Nov. 2018 to Feb. 2019 in the University of Alberta. He is currently a Professor with China University of Geosciences, Wuhan, China. His research interests include advanced alarm monitoring, process control, and data mining for complex industrial processes.

This presentation will show the applicability and effectiveness of statistical approaches and data mining techniques in discovering meaningful patterns from historical alarm data, such as mode-based alarms, frequent alarm flood patterns, and alarm response workflow models. Design of alarm data visualization will also be discussed.

This presentation will introduce design of univariate alarm systems including alarm delay times and deadbands, operating-zone-based multivariate alarm systems, and root-cause analysis of alarms based on the clusters of similar data segments in historical databases.

This presentation will introduce advanced alarms based on process data analytics and correlation/causality analysis based on process and alarm data mining in combination with process connectivity knowledge, with applications to root cause analysis of propagated or even plant-wide abnormalities.

The Gaussian processes that appeared in the machine learning field since around 2000 are extensively utilized in various application fields because of their mathematical simplicity and ease of handling in Bayesian reasoning. They have also been recently drawing much attention in the field of control engineering such as control theory and system identification in particular for stochastic systems. In this workshop, we will have four invited speakers from such various fields as modeling, robotics, reinforcement learning, and control theory, to discuss the recent advances and future trends in basics and applications of Gaussian processes.

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