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Design codes provide requirements for traditional slabs to ensure both safety levels and in‐service functionality criteria. However, for existing structures, an initial diagnostic knowledge phase is essential to characterize the actual health state, e.g. based on structural health monitoring (SHM) with operational modal analysis (OMA) techniques. Considering recent advancements in glass as a structural material, laminated glass (LG) slabs are increasingly used in constructions and are mostly designed to account for the intrinsic brittleness and limited tensile resistance of glass. The latest pre‐code CEN/TS 19100:2021 is paving the road for an imminent future Eurocode 10 dedicated to providing new standards for the innovative use of structural glass. In the current study, a brief review of the design challenges of working with structural glass is presented. After that, the authors provided a simple experimental dynamic identification example of an LG pedestrian walkway located in the Basilica of Aquileia, Trieste (Italy). Specifically, the fundamental mode was compared to a healthy and a partially fractured LG slab. Functionality aspects related to comfort standards with respect to in‐service vibration issues are also detailed. [ABSTRACT FROM AUTHOR]

The article focuses on the dynamic identification and finite element (FE) modelling of a butterfly-arch stress-ribbon pedestrian bridge in Fuzhou, Fujian, China. The Stochastic Subspace Identification method yields an estimate of the operational modal parameters. A highly synchronous tri-axial wireless sensor network was deployed on the bridge deck to record the structure's ambient vibration. Eight stable modes in the frequency range 3.59–14.92 Hz were found, associated with prevalent bending and torsional deformations. Four distinct FE models of the bridge with progressive complexity and accuracy were developed to investigate the sensitivity of the modal features to the modelling choices. The FE model characterised by the fittest agreement with the experimental modal parameters was used for the automatic parametric optimisation based on a sensitivity-based algorithm. The stiffness of the springs simulating the soil-structure interaction, the elastic modulus of the concrete deck and the elastic modulus of the tendons were chosen as updating parameters for a total of eight parameters. The effect of non-structural elements (handrails) and prestress on the modal features are also investigated. The final advanced FE model developed can serve as baseline for a long-term monitoring of the bridge during its life-cycle, and also provides some recommendations to practitioners and scholars all over the world for the modelling and analysis of this particular kind of footbridges. [ABSTRACT FROM AUTHOR]

The paper delivers a short report about the modal identification carried out on a historical masonry palace in the city centre of L'Aquila, Palazzo Pica Alfieri, retrofitted after the 2009 earthquake. A concise description of the damage pattern and the retrofitting interventions heads the presentation of the results of the dynamic identification using Operational Modal Analysis. The identified mode shapes and natural frequencies drive a discussion about the detectability and the assessment of the modifications induced by the structural interventions using low-vibration measurements. Differently from more uncomplicated structures, the production of a Finite Element model representative of an existing masonry complex requires considerable efforts: the masonry structure may not behave like a continuum, due to the presence of cracks and unknown structural discontinuities. Still, the results of the dynamic identification bestow valuable information about the structural behaviour from a qualitative viewpoint, even if not backed by a faithful FE model. The paper addresses the aspects possibly related to the dynamic identification of a monumental masonry building, based on the shreds of evidence arisen from the presented case study. [ABSTRACT FROM AUTHOR]

Operational modal analysis methods have been proven especially useful to identify existing structures and infrastructures under serviceability conditions. However, the installation of sensing systems for monitoring continuously an ever larger number of existing constructions has motivated significant efforts towards the automation of the available methods. Within this framework, the present paper introduces a new paradigm for the automatic output-only modal identification of linear structures under ambient vibrations, namely the intelligent automatic operational modal analysis (i-AOMA). It exploits the covariance-based stochastic subspace (SSI-cov) algorithm for the output-only identification of the modal parameters and its workflow consists of two main phases. Initially, quasi-random samples of the control parameters for the SSI-cov algorithm are generated. Once the SSI-cov algorithm is performed for each sample, the corresponding stabilization diagrams are processed in order to prepare a database for training the intelligent core of the i-AOMA method. This is a machine learning technique (namely a random forest algorithm) that predicts which combination of the control parameters for the SSI-cov algorithm is able to provide good modal estimates. Afterward, new quasi-random samples of the control parameters for the SSI-cov algorithm are generated repeatedly until a statistical convergence criterion is achieved. If the generic sample is classified as feasible by the intelligent core of the i-AOMA method, then the SSI-cov algorithm is performed. Finally, stable modal results are distilled from the stabilization diagrams and relevant statistics are computed to evaluate the uncertainty level due to the variability of the control parameters. The proposed i-AOMA method has been applied to identify the modal features of the Al-Hamra Firduos Tower, an iconic 412.6 m tall building located in Kuwait City (Kuwait). The final results well agree with a previous experimental study, and it was also possible to identify two new vibration modes of the structure. The implemented open-source Python code is made freely available. • Automatic operational modal analysis assisted by machine learning. • Quantification of the uncertainty due to the variability of the control parameters. • Open-source Python-based code for intelligent automatic operational modal analysis. • Modal identification of an iconic tall building, the Al-Hamra Firduos Tower. [ABSTRACT FROM AUTHOR]