Passive devices for vibration control are widely adopted in earthquake engineering for mitigation of seismic effects obtaining an efficient, robust and not expensive structural protection. They are largely used in the seismic protection of industrial machines, technical equipment, buildings, bridges and others more as reliable and affordable solutions. Moreover their performances are extremely sensitive to their dynamic mechanical behavior; a reliable identification of their mechanical behavior is therefore of key importance, despite the current lack of accurate and simple standard procedures to identify parameters and models for those devices. In this work, a new procedure for the dynamic identification of passive devices is described, through standard laboratory dynamic tests and the use of evolutionary algorithms. This procedure allows to find proper mechanical law and parameters to use for an accurate structural analysis and earthquake-resistant structure design. The procedure uses standard pre-qualification and quality-control tests, and consists in the minimization of the integral measure of the difference between mathematic and experimental applied force to the device under an imposed displacement time history. Due to the amount of corruption source of the experimental data and to the deep non linear nature of the problem, the use of evolutive algorithms is the main way to solve hard numerical task in an efficient way. The proposed procedure is applicable to a wide range of mathematical expressions because of its inherent stability and low computational cost, and allows comparing different mechanical laws by ranking their agreement with experimental data. Results are obtained for different experimentally tested devices, that are viscous dampers and seismic isolators, and are reported in order to demonstrate the efficiency of the proposed strategy.
Many countries, especially in southern Europe, are greatly exposed to seismic hazard, which is the cause of severe damage in historical buildings or even destruction in the case of strong earthquake ground motions. The recent experience of Italian seismic events (Umbria and Marche 1997, Puglia and Molise 2002, Abruzzo 2009, Emilia 2012 has highlighted the behavior, the damage and the intrinsic vulnerability of monumental buildings. Historical and monumental constructions are characterized by an inherent vulnerability to seismic action, due to the circumstance that most of them frequently lack basic seismic features and/or were never fitted with adequate provisions against earthquake actions. This entails the need to define urgent strategies for the protection of cultural heritage from seismic hazard. This paper deals with the structural monitoring and seismic assessment of a unique masonry tower in a Apulia region in southern Italy: the bell tower of "Santa Maria di San Luca" in the city of Valenzano. This monument was monitored by means of full-scale environmental vibration testing. Measured responses were then used for modal identification. The assessment procedure includes morphological and structural knowledge, full-scale ambient vibration testing, modal identification from ambient vibration responses, finite element modeling, dynamic-based identification of the model. A satisfactory improvement in modal parameters is so obtained, resulting in a close agreement between the modal properties observed in dynamic tests and those calculated from a numerical model. Nonlinear dynamic analysis allows to identify the potential collapse mechanisms and those dangerous structural weak points which may play a fundamental role in the seismic vulnerability of the towers.
The milling industry envisions solutions to become fully compatible with the industry 4.0 technology where sensors interconnect devices, machines and processes. In this contest, the work presents an integrated solution merging a deeper understanding and control of the process due to real-time data collection by MicroNIR sensors (VIAVI, Santa Rosa, CA)—directly from the manufacturing process—and data analysis by Chemometrics. To the aim the sensors were positioned at wheat cleaning and at the flour blends phase and near infrared spectra (951–1608 nm) were collected online. Regression models were developed merging the spectra information with the results obtained by reference analyses, i.e., chemical composition and rheological properties of dough by Farinograph® (Brabender GmbH and Co., Duisburg, Germany), Alveograph® (Chopin, NG Villeneuve-la-Garenne Cedex, France) and Extensograph®.(Brabender GmbH and Co., Duisburg, Germany) The model performance was tested by an external dataset obtaining, for most of the parameters, RPRED higher than 0.80 and Root Mean Squares Errors in prediction lower than two-fold the value of the reference method errors. The real-time implementation resulted in optimal (100% of samples) or really good (99.9%–80% of samples) prediction ability. The proposed work succeeded in the implementation of a process analytical approach with Industrial Internet of Things near infrared (IIoT NIR) devices for the prediction of relevant grain and flour characteristics of common wheat at the industrial level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.