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The Kashima Lighthouse, one of the lighthouses that suffered damage in the 2011 Great East Japan Earthquake, required a careful investigation to estimate its long-term behavior and seismic vulnerability. This study, therefore, undertook a meticulous process of the dynamic testing, dynamic identification, and long-term structural health monitoring of the Kashima Lighthouse. The results of dynamic tests reveal that the fundamental frequencies of the Kashima Lighthouse are estimated to be around 2.60 Hz and 2.63 Hz in the east–west and north–south directions, respectively. The natural modes and damping factors are identified using an SSIM (Stochastic Subspace Identification Method). This paper also discusses the result of long-term structural health monitoring, where machine learning techniques were applied for data processing, highlighting the rigor and thoroughness of this research. [ABSTRACT FROM AUTHOR]

The Castle of Lanjarón is a 16th century stronghold located in Andalucía, Spain. After losing its military function, the castle was abandoned, leading to significant decay. Designated a national heritage site in 1985, recent efforts have sought to preserve its historical and cultural value. This study outlines an inspection and diagnosis campaign carried out on the castle. Non-destructive tests (NDTs) were employed to characterize the properties of the masonry, using both mechanical and wave-based methods. Dynamic identification was performed to determine dynamic and modal properties of the structure, which were used to develop and calibrate a three-dimensional (3D) finite element model (FEM) of the west wall, based on homogenized masonry material. Limit analysis and non-linear static (pushover) analysis under various boundary conditions were conducted to determine the maximum relative load factor in the out-of-plane direction. The results were compared to the expected peak ground acceleration (PGA) of the area, showing that the maximum load capacity of the wall exceeds local seismic demands with a safety factor of 1.39. The study highlights the efficacy of pairing a homogenized macro-modeling approach with wave-based and dynamic identification methods, particularly for resource efficiency. Finally, recommendations for future conservation efforts have been provided. [ABSTRACT FROM AUTHOR]

This paper describes the experimental campaigns on the Tower of the Palazzo dei Vicari in Scarperia, a village in the Mugello area (Tuscany) exposed to high seismic hazards. The first campaign was carried out from December 2019 to January 2020, and the Tower underwent the so-called Mugello seismic sequence, which featured an M 4.5 earthquake. Other ambient vibration tests were repeated in June 2021 and September 2023 when another seismic sequence struck the area near Scarperia. These tests aimed to characterise the Tower's dynamic behaviour under ambient and seismic excitations and check the response of the Tower over time. The experimental results were then used to calibrate a finite-element model of the Tower and estimate its seismic vulnerability. Several numerical simulations were conducted on the calibrated model using the NOSA-ITACA code for nonlinear structural analysis of masonry buildings. The dynamic behaviour of the Tower subjected to a seismic sequence recorded in 2023 by a seismic station at the base was investigated by comparing the velocities recorded along the Tower's height with their numerical counterparts. Furthermore, several pushover analyses were conducted to investigate the collapse of the Tower as the load's distribution and direction varied. [ABSTRACT FROM AUTHOR]

There is a growing need for monitoring the structural health conditions of aging structures and for prioritizing maintenance works to extend their safe service life. This requires cheap, flexible, and reliable tools suitable for everyday use in engineering practice. This paper presents a computer vision-based technique combining motion magnification and statistical algorithms to calculate structural natural frequencies under environmental noise excitation, and its application to a reinforced concrete elevated water tank. Digital videos were recorded from various standpoints and post-processed by tracking in time either the variation of the grey-intensity or the motion of selected pixels. Computer vision-based outcomes were validated against accelerometric measurements and integrated to them to improve the understanding of the dynamic behaviour of the water tower, which, counterintuitively, resulted anything but trivial to predict. [ABSTRACT FROM AUTHOR]

The dynamic model describes the relationship between the motions of robots and the forces or torques causing these motions. This work describes a novel dynamic identification method for general serial manipulators from an analytical perspective based on Lie theory. Unlike the existing work, the proposed method does not rely on the Kronecker product to obtain the linear identification model from the closed-form dynamic equation. We derived the dynamic equation of a single rigid body into a linear model using symbolic computation. Then using the Newton–Euler method, we obtained the equivalent linear identification model for the manipulator. By analyzing the expression of the identification model, we discussed the impact of different joint types on the linear independent columns of the coefficient matrix in the identification model. Several criteria were proposed for directly extracting the linear independent columns based on the robot's joint screw axes without the need for experimental data. Furthermore, a recursive method was proposed to obtain the complete expression of the base parameters (BPS). The optimal excitation trajectory and physical feasible constraints are also incorporated into the workflow of the proposed identification method. Compared to the existing dynamic identification method, the method significantly reduces the computational complexity and does not limited to prismatic and revolute joints. The method was demonstrated using a RS010N manipulator. Through a series of experiments, the BPS has been obtained. The differences between the actual torques and the calculated torques from the BPS is quiet small, demonstrating the effectiveness of the identification method. Finally, a set of physical feasible dynamic parameters was searched from the BPS using linear matrix inequalities. [ABSTRACT FROM AUTHOR]

Accelerating performance and braking performance are the core performance of a vehicle, and tire load analysis is a prerequisite for performance studying. A calculation and correction method of dynamic tire loads is proposed in this paper. First, the numerical and experimental models of tire dynamic load identification are established, by measuring the acceleration signals at different positions of the steering knuckle arm, the tire deformation in three directions is inversely calculated. Second, the longitudinal force, lateral force and vertical force of the tire are estimated according to the tire stiffness and deformation. Finally, the tire loads are corrected by data filtering and tire stiffness adjustment. The results show that the average agreement between the calculated load and the test load is more than 90%. The calculation and correction method can provide a theoretical basis and reference for analyzing the dynamic change characteristics of tire load. [ABSTRACT FROM AUTHOR]

Accurate online estimation of the payload parameters benefits robot control. In the existing approaches, however, on the one hand, only the linear friction model was used for online payload identification, which reduced the online estimation accuracy. On the other hand, the estimation models contain much noise because of using actual joint trajectory signals. In this article, a new estimation algorithm based on parameter difference for the payload dynamics is proposed. This method uses a nonlinear friction model for the online payload estimation instead of the traditionally linear one. In addition, it considers the commanded joint trajectory signals as the computation input to reduce the model noise. The main contribution of this article is to derive a symbolic relationship between the parameter difference and the payload parameters and then apply it to the online payload estimation. The robot base parameters without payload were identified offline and regarded as the prior information. The one with payload can be solved online by the recursive least squares method. The dynamics of the payload can be then solved online based on the numerical difference of the two parameter sets. Finally, experimental comparisons and a manual guidance application experiment are shown. The results confirm that our algorithm can improve the online payload estimation accuracy (especially the payload mass) and the manual guidance comfort. [ABSTRACT FROM AUTHOR]

The ability of structural health monitoring (SHM) systems to detect defects and damage in existing structures is currently a key issue in the life-cycle management of civil infrastructure. In this respect, the use of dynamic identification to detect the loss of prestress load, grouting defects, cracking, and corrosion in post-tensioned concrete (PC) bridges is receiving special interest as an attractive tool that may drive predictive maintenance for disaster risk mitigation, motivating widespread implementation of SHM systems in engineering practice. In this study, an experimental program on reduced-scale PC beams representative of typical girder bridge decks, at the Department of Structures for Engineering and Architecture, University of Naples Federico II, is presented. The specimens were characterized by different prestressing load levels, grout injection conditions of post-tensioning ducts, and strand rupture during testing. Operational modal analysis (OMA) was carried out at multiple steps of each test, which were associated with different damage states of the specimens. OMA results show that both natural frequencies and mode shapes of PC girders are not significantly influenced by slight cracking corresponding to service load levels, while indicating some frequency variations in the case of local strand failure. [ABSTRACT FROM AUTHOR]

Displacement is a key parameter to evaluate the serviceability and safety of structures, but the traditional contact measurement method has the disadvantage of low efficiency and high cost, and it is difficult to accurately measure the dense displacements. To solve the above problems, a noncontact measurement method for the dynamic displacement and vibration properties of structures with computer vision technology and adaptive variational modal decomposition was proposed. A high-speed camera was employed to capture the motion images of a cantilever beam structure under the excitation of an impact hammer, and target detection and feature extraction algorithms without targets were developed to obtain the dynamic displacements of the structure under the impact load. Furthermore, a high-precision identification method for the vibration frequency and damping ratio of a structure based on the variational modal decomposition and the Hilbert transform was proposed. The effectiveness of the proposed method has been validated by the noncontact vibration measurement tests of a cantilever beam. The results show that the maximum measurement errors of displacement extracted by the circular center detection algorithm and the image feature detection algorithm are 0. 093 mm and 0. 046 mm, respectively, which proves that the displacement extraction accuracy of the feature detection algorithm is more accurate. Meanwhile, the variational modal decomposition method is used for the separation of the measured displacement signals and the identification of the structural vibration properties. It is seen that the identified natural frequencies and damping ratios by the circular target detection are almost the same as the results obtained from feature extraction algorithms. The research results can provide a new method for high-precision multipoint displacement monitoring and dynamic identification of bridge structures under unexpected events, and it has the advantages of low cost, ease of use, and flexible choice of measurement points. [ABSTRACT FROM AUTHOR]

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM's efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns. [ABSTRACT FROM AUTHOR]

In the present work, a large database comprising a set of features related to geometry, type of boundary condition, type of material and experimental value of the first natural frequency for 184 historic masonry towers is collected. The database is then used to develop empirical formulas for the estimation of the fundamental frequency related to the first bending mode. The data are divided into a training set and a validation set for a correct assessment of the empirical equations, generated through power regression and artificial neural network. Different combinations of features are exploited as independent variables to investigate their influence on the dynamic behaviour of masonry towers. To improve the accuracy of the prediction, distinct models are generated for specific clusters with likely homogeneous behaviour, according to the boundary conditions (isolated or bounded) and type of materials (brick, stone or mixed masonry). [ABSTRACT FROM AUTHOR]

It is of great significance to accurately identify the rockburst hazard areas and give the hazard level and its evolution law for rockburst prevention and control. In this study, the method combining deformation localization with multivariate geophysical indicators spatial scanning is used to explore the precursor characteristics of microseismic in the area of high-energy microseismic events and track the dynamic evolution process of rockburst hazard areas. Based on the principle of deformation localization, the gradient significance indicator is used to identify the deformation localization areas and delineate the hazard area. The sliding window scanning method is used to study the spatial distribution characteristics of physical indicators such as b value, A(b) value and S value in the deformation localization areas. The b value, A(b) value, S value, ∆F and A(t) value corresponding to the high-energy microseismic events identified by the gradient significance index during excavation are used as the threshold values for classifying the rockbrust hazard level during the mining operation. The Bayesian network method is used to analyze the effectiveness of each physical indicator in predicting the hazard areas, and a comprehensive predicting hazard areas model is constructed to calculate the weight of physical indicators and obtain the comprehensive predicting indicators. The 513 working face is analyzed as an example. The results show that the geophysical indicators can identify the microseismic gathering signal and assess the hazard areas. Three microseismic events gathering areas are determined according to the measured data of 513 working face. The spatial scanning results of physical indicators and the gathering areas of microseismic data have the synchronization characteristics. When some high-energy microseismic events occur, the physical indicator value of the area is higher than the rockburst hazard threshold, and the hazard areas identified by the physical indicator spatial scanning is basically consistent with the gathering areas of microseismic data. The integrated prediction model is used to predict the hazard area during the mining period of the working face. The results show that the rockburst hazard events mostly occur in the strong high hazard areas predicted by the integrated prediction indicator. With the superposition of microseismic data during the mining period, the high rockburst hazard areas is further concentrated, and the overlap degree with the high hazard event location is higher. The prediction efficiency of the integrated prediction indicator is generally higher than that of single physical indicator, which significantly enhances the ability to accurately predict the rockburst hazard areas.

The Castle of Lanjarón is a 16th century stronghold located in Andalucía, Spain. After losing its military function, the castle was abandoned, leading to significant decay. Designated a national heritage site in 1985, recent efforts have sought to preserve its historical and cultural value. This study outlines an inspection and diagnosis campaign carried out on the castle. Non-destructive tests (NDTs) were employed to characterize the properties of the masonry, using both mechanical and wave-based methods. Dynamic identification was performed to determine dynamic and modal properties of the structure, which were used to develop and calibrate a three-dimensional (3D) finite element model (FEM) of the west wall, based on homogenized masonry material. Limit analysis and non-linear static (pushover) analysis under various boundary conditions were conducted to determine the maximum relative load factor in the out-of-plane direction. The results were compared to the expected peak ground acceleration (PGA) of the area, showing that the maximum load capacity of the wall exceeds local seismic demands with a safety factor of 1.39. The study highlights the efficacy of pairing a homogenized macro-modeling approach with wave-based and dynamic identification methods, particularly for resource efficiency. Finally, recommendations for future conservation efforts have been provided.

The Kashima Lighthouse, one of the lighthouses that suffered damage in the 2011 Great East Japan Earthquake, required a careful investigation to estimate its long-term behavior and seismic vulnerability. This study, therefore, undertook a meticulous process of the dynamic testing, dynamic identification, and long-term structural health monitoring of the Kashima Lighthouse. The results of dynamic tests reveal that the fundamental frequencies of the Kashima Lighthouse are estimated to be around 2.60 Hz and 2.63 Hz in the east–west and north–south directions, respectively. The natural modes and damping factors are identified using an SSIM (Stochastic Subspace Identification Method). This paper also discusses the result of long-term structural health monitoring, where machine learning techniques were applied for data processing, highlighting the rigor and thoroughness of this research.

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility. [ABSTRACT FROM AUTHOR]

Past earthquakes highlighted the vulnerability of cleanrooms, especially in earthquake-prone countries like Italy; the post-earthquake reconnaissance showed that damage to cleanrooms led to immeasurable economic losses. This study investigates the seismic performance of an innovative cleanroom, seismically designed and provided with innovative components. Full-scale shake table tests were performed on the cleanroom according to ICC-ES AC156 protocol; the cleanroom was tested under operating conditions. Both dynamic properties and seismic behavior of the specimen were assessed. The tests showed the excellent seismic behavior of the cleanroom, confirming that simple devices can significantly improve the seismic performance of nonstructural elements. [ABSTRACT FROM AUTHOR]

Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring. [ABSTRACT FROM AUTHOR]

Post-earthquake damage surveys systematically highlight the seismic vulnerability of monumental structures, calling for simple assessment procedures to address the design of effective retrofitting interventions. The structural complexity characterizing monumental structures, however, makes a reliable prediction of their seismic response a relevant challenge of engineering interest. Ambient vibration tests (AVTs) provide valuable support to achieve such a task, improving the knowledge of the actual dynamic behavior of the structure and, consequently, the reliability of the seismic assessment. In this context, the paper illustrates the integration of AVTs outcomes with the evaluation of the seismic performance of historic masonry structures by presenting the comprehensive application to a case study, the bell tower of the Saint Lawrence's Cathedral in Genoa, Italy. The research combines the assessment of the global seismic response of the tower, investigated through a simplified mechanical model, with the local verification of the pinnacles placed at its top, referring to a displacement-based approach on a macro-block model. An extensive ambient vibrations measurement campaign carried out in May 2020 allowed for a comprehensive operational identification of the bell tower and its pinnacles, clarifying the ongoing dynamic interaction with the main body of the church. This valuable information was successfully employed, first, to accurately reproduce the actual constraint conditions induced by the church on the bell tower, a determining factor in the modeling of its global seismic response and, second, to reliably quantify the seismic amplification caused by the tower filtering effect to be used as the seismic input for the local verification of the pinnacles. [ABSTRACT FROM AUTHOR]

As ageing structures and infrastructures become a global concern, structural health monitoring (SHM) is seen as a crucial tool for their cost-effective maintenance. Promising results obtained for modern and conventional constructions suggested the application of SHM to historical masonry buildings as well. However, this presents peculiar shortcomings and open challenges. One of the most relevant aspects that deserve more research is the optimisation of the sensor placement to tackle well-known issues in ambient vibration testing for such buildings. The present paper focuses on the application of optimal sensor placement (OSP) strategies for dynamic identification in historical masonry buildings. While OSP techniques have been extensively studied in various structural contexts, their application in historical masonry buildings remains relatively limited. This paper discusses the challenges and opportunities of OSP in this specific context, analysing and discussing real-world examples, as well as a numerical benchmark application to illustrate its complexities. This article aims to shed light on the progress and issues associated with OSP in masonry historical buildings, providing a detailed problem formulation, identifying ongoing challenges and presenting promising solutions for future improvements. [ABSTRACT FROM AUTHOR]

Recently, bridge collapse accidents have become increasingly frequent during the flooding season, and foundation scour is one of the main reasons. Tracking scour evolution accurately is a key premise for preventing and controlling hydrological damage. Scour identification according to the changing dynamic characteristics during the scouring process tends to be one of the top technical methodologies in scour monitoring. Although efforts have been made to investigate the dynamic identification of foundation scour, the investigations have mainly focused on the qualification of foundation scour utilizing numerical simulations. Quantitative analysis and validation through laboratory experiments with large-scale water flumes are still lacking. To bridge the gap, this study performed physical modeling experiments with a large-scale water flume for foundation scour to investigate the relationship between structural frequency and scour evolution. The research sought to validate the feasibility of using structural frequency in dynamic identification. First, scour experiments with a large-scale water flume for three piers were performed to collect the time history of acceleration signals. Then, the acceleration signals were processed to recognize the temporal evolution of structural frequency during the scouring process. Finally, the relationship between the temporal scour depth and frequency was assumed to be linear and nonlinear to fit the time history of structural frequency. The results indicated that the frequency and the square of frequency can be taken as the dynamic fingerprint in scour identification according to the frequency range. Based on the validation of large-scale flume experiments, the proposed nonlinear temporal models of frequency in the study demonstrated a good indicator for predicting scour depth. The methodology can greatly enhance the practicality and convenience of bridge scour dynamic identification. [ABSTRACT FROM AUTHOR]

Aiming at the challenges to accurately simulate complex friction models, link dynamics, and part uncertainty for high-precision robot-based manufacturing considering mechanical deformation and resonance, this study proposes a high-precision dynamic identification method with a double encoder. Considering the influence of the dynamic model of the manipulator on its control accuracy, a three-iterative global parameter identification method based on the least square method and GMM (Gaussian Mixture Model) under the optimized excitation trajectory is proposed. Firstly, a bidirectional friction model is constructed to avoid using residual torque to reduce the identification accuracy. Secondly, the condition number of the block regression matrix is used as the optimization objective. Finally, the joint torque is theoretically identified with the weighted least squares method. A nonlinear model distinguishing between high and low speeds was established to fit the nonlinear friction of the robot. By converting the position and velocity of the motor-side encoder to the linkage side using the deceleration ratio, the deformation quantity could be calculated based on the discrepancy between theoretical and actual values. The GMM algorithm is used to compensate the uncertainty torque that was caused by model inaccuracy. The effectiveness of the proposed method is verified by a simulation and experiment on a 6-DoF industrial robot. Results prove that the proposed method can enhance the online torque estimation performance by up to 20%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University / Dongnan Daxue Xuebao is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, modal and structural identification of a historic masonry bell tower in Čuntići, Croatia, damaged during the recent Petrinja earthquake, was performed. The results of the ambient vibration tests (AVT) and operational modal analysis (OMA) were used to update the finite element numerical model of the bell tower. Three modes were experimentally determined: the first two were bending modes (f1 = 4.395 Hz and f2 = 4.639 Hz), and the third was a torsional mode (f3 = 10.303 Hz). The experimentally determined and the originally calculated (preliminary NM) modal shapes agreed well, but in terms of modal frequencies, the correlation was poor. After model updating, some structural parameters were identified, and a reliable finite element numerical model was established. The proposed method can provide a reliable evaluation of the structural parameters of historic masonry buildings. [ABSTRACT FROM AUTHOR]

This paper investigates a masonry minaret's current condition and seismic performance, namely Alacakapı Mir-Alay Şah Ali Bey (The short form is Alacakapı Minaret). Two prayer halls surround the Alacakapı minaret. One of these prayer halls was severely damaged by the Sivrice-Elazığ earthquake on January 24, 2020. The other prayer hall is moderately damaged and still waiting for restoration and reinforcing solutions. The severely damaged hall was demolished. Due to the demolished hall, boundary conditions and clear height changed. Two surfaces are considered to be fixed supports. One is the bottom side, and the other is the vertical side. To determine the current condition of the minaret, first, dynamic identification is conducted by five accelerometers. Then destructive and non-destructive tests were performed to determine material properties. After that, a solid model was generated, and model calibration was conducted. Eight different formulations were considered for the model calibration proposed in the literature, reflecting a homogenized material approach that includes brick-and-mortar units. Three different mortar strengths were considered to obtain the best material properties that demonstrate the actual situation of the minaret. Local damage is realized when comparing the finite element model and operational modal analysis results. Finally, six different earthquake records were selected to determine the seismic response of Alacakapı Minaret to determine vulnerable locations for retrofit solutions. [ABSTRACT FROM AUTHOR]

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 model updating procedures employed in vibration-based health monitoring need to be reliable and computationally efficient. The computational time is a fundamental task if the results are used to evaluate, in quasi-real-time, the safe or the unsafe state of strategic and relevant structures. The paper presents an efficient two-step procedure for the identification of the mechanical parameters and for the assessment of the corresponding uncertainty in model updating problems. The first step solves a least squares problem, providing a first estimate of the unknown parameters. The second (iterative) step produces a refinement of the solution. Moreover, by exploiting the error propagation theory, this article presents a direct (non-iterative) procedure to assess the uncertainty affecting the unknown parameters starting from the experimental data covariance matrix. To test the reliability of the procedure as well as to prove its applicability to structural problems, the methodology has been applied to two test-bed case studies. Finally, the procedure has been used for the damage assessment in an existing building. The results provided in this article indicate that the procedure can accurately identify the unknown parameters and properly localize and quantify the damage.

Widely used simplified analytical methods for estimating the tensile force in tie-rods are clearly not applicable when they contain significant discontinuities or irregularities. A common example for which this fact becomes relevant in practice is the use of connectors to unify historical ties consisting of several segments. To address this challenge, a robust hybrid methodology is proposed which can be applied to any historical tie by employing a data-driven approach to a dataset generated using the finite element method. The methodology is applied to a real case study involving two historical ties. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The preservation of trees in urban and archeological areas is a theme of particular relevance. Modern systems of monitoring, together with approaches for deriving the main characteristics of trees influencing their response toward extreme events, are nowadays at the basis of a growing number of studies. The aim of the present paper is the dynamic identification of trees carried out by employing an approach which combines a simple data-acquisition system, direct and ambient sources of excitation, and different data-processing methods. In particular, using a single accelerometer placed at different sections of the trunk and considering excitations induced by either pulling tests or ambient vibrations, the derivation of the main frequencies and levels of modal damping characterizing the dynamic response of a sour cherry tree (Prunus cerasus) is carried out. A finite element model of the tree is also carried out to support the validation of the proposed approach and further analyze the derived outcomes. The obtained results underline the feasibility of the proposed approach in deriving information useful for assessing the behavior of trees toward dynamic actions and, consequently, of particular relevance for the identification of possible damages induced by variations in terms of dynamic characteristics (frequencies) and damping. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inertial and nonlinear friction parameters precise identification of Stewart platform (SP) is a pending problem owing to the strong coupling and small workspace. This paper proposes a novel identification model based on symmetric excitation trajectory (SET), which is verified on low impact docking mechanism (LIDM, a typical SP). Firstly, the SET is defined, and its decoupling and filtering characteristics of the SP are analyzed. The dynamic equation of the LIDM is derived from Boltzmann–Hamel–d'alembert formula, and a typical dynamic parameter identification model (TM) is obtained. Then, a symmetric dynamic parameter identification model (SM) with the decoupling characteristic is derived from the SET. Moreover, the SM is improved according to the nonlinear friction phenomenon, and a nonlinear dynamic parameter identification model (SM_Non) including acceleration term is established. Besides, the speed-span index is given to ensure sufficient excitation of friction at different velocities during the identification process. Finally, experiments on the LIDM demonstrate the validity and accuracy of the proposed methods. The results of joint current prediction experiments show that the root mean square error of the SM_Non is 92.789% lower than that of the TM. [ABSTRACT FROM AUTHOR]

Accurate interaction force estimation can play an important role in optimizing human–robot interaction in an exoskeleton. In this work, we propose a novel approach for the system identification of exoskeleton dynamics in the presence of interaction forces as a whole multibody system without imposing any constraints on the exoskeleton dynamics. We hung the exoskeleton through a linear spring and excited the exoskeleton joints with chirp commands while measuring the exoskeleton–environment interaction force. Several structures of neural networks were trained to model the exoskeleton passive dynamics and estimate the interaction force. Our testing results indicated that a deep neural network with 250 neurons and 10 time–delays could obtain a sufficiently accurate estimation of the interaction force, resulting in an RMSE of 1.23 on Z–normalized applied torques and an adjusted R 2 of 0.89. [ABSTRACT FROM AUTHOR]