Dynamics of Civil Structures, Volume 2

For application in operational modal analysis considering simultaneously the temporal and spatial response data of multi-channel measurements, the multivariate-autoregressive (MV-AR) model was used. The parameters of MV-AR model are estimated by using the least squares method via the implementation of the QR factorization as an essential numerical tool and are used to extract the structural damage sensitive features. These parameters are used to develop the Vectors of autoregressive model and Mahalanobis distance, and then to identify the damage features and damage locations. Verification of the proposed method using a series of white noise response data of a steel structure is demonstrated. This method is thus very effective for damage detection in case of ambient vibrations dealing with output-only modal analysis. In addition, comparisons and discussions on the proposed method with other methods, such as stochastic subspace identification and wavelet-based energy index, are also presented.

Time-varying environmental and operational conditions such as temperature and external loading may often mask subtle structural changes caused by damage and have to be removed for successful structural damage identification. In the paper, a symplectic geometry spectrum analysis method is employed to decompose a time series into the sum of a small number of independent and interpretable components, in which one can determine which components are caused by external influences. The symplectic geometry spectrum analysis method is performed in four steps: embedding, symplectic QR decomposition, grouping and diagonal averaging. One excellent advantage of the method is that it can deal with nonlinear time series which is inherently rooted in structural damage due to crack opening and closing. Numerical simulation shows that the method is promising to detect structural damage in the presence of environmental and operational variations.

Structural health monitoring (SHM) techniques have been studied over the past few decades to detect the deficiencies affecting the performance of the structures. Detecting and localizing these deficiencies require long-term data collection from dense sensor networks which creates a challenging task for data transmission and processing. To address this problem, a comparative study of two image-based compressive sensing approaches for multiple damage localization is presented in this paper. The first methodology consists of compressive sampling from the sensor network in global and local formats. Then through statistical change point analysis on the sampled datasets, and Bayesian probability estimation, the study estimates the location of damage. The second algorithm implements compressive sensing to the subset of samples obtained from the sensor network space divided into blocks. The damage existence and location are determined by statistical hypothesis testing of Discrete Cosine Transformation (DCT) coefficients avoiding the original signal recovery. In order to evaluate the performance of both algorithms, multiple damage scenarios are simulated in steel gusset plate model. The comparison results are presented in terms of compression ratios and successful detection rates.

To mitigate the expensive and time-consuming nature of visual structural inspections, vibration-based structural damage detection methods have been proposed that rely on different damage-sensitive features. These features are derived from data collected by sensor networks implemented on the structure. Damage detection through vibration-based feature analysis thus far has relied on simulation or the responses of fixed sensor networks for feature creation. Another type of monitoring scheme, called mobile sensing, has the ability to eliminate the limited spatial information constraint of fixed sensor networks. However, regardless of the monitoring approach of a real-world structure, data sets can incur cases of missing data, either due to such situations like sensor malfunction or loss of communication connectivity, or in the case of mobile sensing due to the nature of the approach itself. In this paper, a fixed sensor network is implemented on a scale laboratory frame structure, and observations are removed from the resulting complete datasets to simulate data missingness. Damage is simulated through interchangeable, variable stiffness elements that make up the frame. Damage detection is conducted by fitting a numerical model to the data and assessing the significance of the change in the model parameters when damage is introduced.

This paper discusses the structural assessment of a red-tagged four-story school building in Sankhu, Nepal. The building had a masonry-infilled reinforced concrete frame which was severely damaged during the 2015 Gorkha Earthquake. The concentration of damage in the west end of the first story indicates that the frame exhibited torsional response to the ground excitation. The authors visited the structure 2 months after the earthquake, collected LiDAR scans, and recorded the ambient vibrations of the damaged structure. The LiDAR data has been used to create a three-dimensional point cloud of the building which has allowed the identification of the locations and geometry of the major cracks but also the measurement of the permanent deformations of the building. The structure was also instrumented with four unidirectional accelerometers on every floor; two at opposite corners, to capture the translational and torsional motion. The translational and torsional modes have been identified with an operational modal analysis method and have been used to validate a finite element model of the structure. The comparison indicates that the model can capture the modal properties of the structure utilizing the strut modeling approach for the infill panels.

In this study, an optimized damage detection using genetic algorithm (GA) in beam-like structures without using baseline data. For this purpose, a vibration-based damage detection algorithm using a damage indicator called ‘Relative Wavelet Packet Entropy’ (RWPE) was applied to determine the location and severity of damage. To improve the algorithm, GA was utilized to optimize the algorithm so as to identify the best choice of wavelet parameters. To examine the robustness and accuracy of the proposed method, numerical examples and experimental cases with different damage depths were considered and conducted.

The acoustoelastic theory has been widely utilized for nondestructive stress measurement in structural components. Most of the currently available techniques operate at the high-frequency, weakly-dispersive portions of the dispersion curves, and rely on time-of-flight measurements to quantify the effects of stress state on wave speed. This adversely affects the sensitivity and accuracy of such techniques, and renders their accuracy limited by the precision within which time-of-flight can be determined.In this work, a novel acoustoelastic-based stress measurement technique is developed by combining dispersion compensation algorithms and numerical optimization schemes. Dispersion compensation allows the use of highly-stress-sensitive, low-frequency flexural waves for stress measurement, which in turn enhances the sensitivity of the developed technique. The need for accurate time-of-flight measurements is eliminated in this work by analyzing the entire propagated waveform to reconstruct the dispersion curve over the frequency range of interest. The fact that an entire section of the dispersion curve, as opposed to a single wave speed measurement, is used to calculate the state of stress in the structure enhances the technique’s accuracy and robustness. A criterion for optimal selection of excitation waveform is developed in this work. The effects of material properties uncertainties on the accuracy of stress measurements are also investigated.

Functionality of movable bridge highly depends on the performance of the mechanical components including gearbox and motor. Therefore, on-going maintenance of these components are extremely important for uninterrupted operation of movable bridges. Unfortunately, there have been only a few studies on monitoring of mechanical components of movable bridges. As a result, in this study, a statistical framework is proposed for continuous maintenance monitoring of the mechanical components. The efficiency of this framework is verified using long-term data that has been collected from both gearbox and motor of a movable bridge. In the first step, critical features are extracted from massive amount of Structural Health Monitoring (SHM) data. Next, these critical features are analyzed using Moving Principal Component Analysis (MPCA) and a condition-sensitive index is calculated. In order to study the efficiency of this framework, critical maintenance issues have been extracted from the maintenance reports prepared by the maintenance personnel and compared against the calculated condition index. It has been shown that there is a strong correlation between the critical maintenance actions, reported individually by maintenance personnel, and the condition index calculated by proposed framework and SHM data. The framework is tested for the gearbox.

Mode shapes (MSs) have been extensively used to identify structural damage. This paper presents a new non-model-based method that uses principal, mean and Gaussian curvature MSs (CMSs) to identify damage in plates. A multi-scale discrete differential-geometry scheme is proposed to calculate principal, mean and Gaussian CMSs associated with a MS of a plate, which can alleviate adverse effects of measurement noise on calculating the CMSs. Principal, mean and Gaussian CMSs of a damaged plate and those of an undamaged one are used to yield four curvature damage indices (CDIs), including Maximum-CDIs, Minimum-CDIs, Mean-CDIs and Gaussian-CDIs. Damage can be identified near regions with consistently higher values of the CDIs. It is shown that a MS of an undamaged plate can be well approximated using a polynomial with a properly determined order that fits a MS of a damaged one. New fitting and convergence indices are proposed to quantify the level of approximation of a MS from a polynomial fit to that of a damaged plate and to determine the proper order of the polynomial fit, respectively. The proposed method is numerically applied to MSs of an aluminum plate with damage in the form of a thickness reduction area to investigate its effectiveness; the damage on the plate is successfully identified.

This paper presents the detailed experiment setups and data analysis results of the ambient vibration testing of two irregular high-rise buildings done by a joint research team from the University of British Columbia and Tongji University. Two full-scale tests were conducted in May 2015 at the Siping Campus of Tongji University, Shanghai, China. The first Multi-Functional Building (MFB) has 21 stories with a typical floor height of 4 m. The structure shows significant irregularity in both plane and vertical direction, where every three floors combine into one L-shaped unit and the L-shape floor rotates 90° along the height of the building. The second building tested is the Shanghai International Design Center (SIDC). SIDC is an h-shape asymmetry double tower structure consists of main tower (25 story) and secondary tower (12 story). The two towers are rigidly connected with steel truss system between the 11th and 12th floors. Considering the complexities and irregularities of the two buildings, it is essential to study their dynamic properties and the associated principal modes of vibration. Several modal analysis techniques were adopted to determine the modal parameters of two buildings.

Wind turbine blades are exposed to continuously-varying aerodynamic forces, gravitational loads, lightning strikes, and weather conditions that lead the blade damage such as leading and trailing edge splits, cracks and holes. In this study, actively-controlled acoustic sources were utilized in order to excite the blade’s cavity structure from internal. The blade damage manifests itself in changes to the acoustic cavity frequency response functions and to the blade acoustic transmission loss. Proposed research examines the use of wireless sensing approach for detecting surface damage of the blades, while they are rotating when wind turbine is operational. A subscale wind turbine was built and used for carrying out preliminary experimental studies. Sensing system and strategy was benchmarked both using computational (FEM) model of the blades as well as the experimental results in the lab.

This article presents the application of a new damage assessment method based on a supervised learning algorithm that uses the principle of maximum entropy. The proposed algorithm employs, as reference information, the natural frequencies and mode shapes obtained with a numerical model of the structure in an undamaged condition and under different damage scenarios. The algorithm is validated with experimental tests on a laboratory six-levels-structure that is 2 m tall. The structure was anchored to a horizontal vibrating shaker table and it was excited at the support level by different seismic records and colored noise. The change in the structure from a normal condition to a damaged one is made by three consecutive reductions in the cross-section of a column. Additionally, a perturbation is introduced as a 0.5 % increase in the total mass of the structure. The experimental modal properties are identified by the Stochastic Subspace and Multivariable Output Error State sPace (MOESP) methods and they are compared with those obtained with a numerical model. The identification, location and quantification of damage that has been obtained with the proposed algorithm in the different test conditions, agrees well with the experimental damage, even for the conditions were few sensors are located in the structure.

Advances in sensor deployment and computational modeling have allowed significant strides to be made recently in the field of Structural Health Monitoring (SHM). One widely used SHM technique is to perform a vibration analysis where a model of the structure’s pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability and unknown values for model parameters. Not accounting for uncertainty in the analysis can lead to false-positives or false-negatives in the assessment of the structural condition. To manage the aforementioned uncertainty, we propose a robust-SHM methodology that combines three technologies. A time series algorithm is trained using “baseline” data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate “size” of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes in time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM.

The presented study concerns experimental dynamic identification of (slightly) anisotropic bladed rotors under operating conditions. Since systems with a rotating rotor do not fall into a category of time invariant system, a straightforward application of modal analysis is not valid. Under assumptions of linearity and constant angular speed, a system with rotating rotor can be considered as a linear periodically time variant (LPTV) system; dynamic identification of such systems require dedicated methods. The Harmonic OMA Time Domain (H-OMA-TD) method is one of very few techniques able to deal with anisotropic rotors. This study demonstrates the method on a simple six degrees-of-freedom mechanical system with a three-bladed rotor. It shows that the method is capable of identifying the phenomena specific for anisotropic rotors. The technique is compared with another technique, multiblade coordinate (MBC) transformation, and the advantages of H-OMA-TD become apparent when the rotor is anisotropic. Finally, the method is demonstrated on data measured on a real Vestas V27 wind turbine and data obtain via HAWC2 simulations of the same wind turbine.

The higher levels of Structural Health Monitoring (SHM)—localisation, classification, severity assessment—are only accessible using supervised learning in the data-based approach. Unfortunately, one does not often have data from damaged structures; this forces a dependence on unsupervised learning i.e. novelty detection. This means that detection is sensitive to benign environmental and operational variations (EOVs) in or around the structure. In this paper a two-stage procedure is presented: identify EOVs in training data using a nonlinear manifold approach and remove EOVs by utilising the interesting tool of heteroscedastic Gaussian processes (GPs). In Classical GPs models, the data noise is assumed to have constant variance throughout the input space. This assumption is a drawback most of the time, and a more robust Bayesian regression tool where GP inference is tractable is needed. In this work a combination of data projection and a non-standard heteroscedastic GP is presented as means of visualising and exploring SHM data.

The Shanghai Tower is one of the super-tall buildings under construction in the world and will be the tallest structure in China after completion. The building stands approximately 632 m and has 128 stories. It was designed with a double curtain wall system, where a triangular outer facade gradually shrinks and twists clockwise at approximately 120° along the height of the building. On May 8th, 2015, an Ambient Vibration testing was performed on the building in its final stages of construction by a collaborative team from the University of British Columbia and Tongji University. The full-scale test had several purposes that included the evaluation of the current instrumentation techniques and the identification of the dynamic properties of the tower. A set of seven GPS-timed velocity/acceleration recorders was utilized, where two measurements were taken on almost every 15 floors in each setup. This paper presents the signal processing results by using the Frequency Domain Decomposition (FDD) and the Enhanced Frequency Domain Decomposition (EFDD) techniques. The most significant lateral and torsional mode shapes and associated periods of vibration were determined within the frequency range of 0.1–2 Hz. The results will provide useful information for further finite element model update.

This paper presents a novel method for recovering base shear forces of building structures with unknown nonlinearities from sparse seismic-response measurements of floor accelerations. The method requires only direct matrix calculations (factorizations and multiplications); no iterative trial-and-error methods are required. The method requires a mass matrix, or at least an estimate of the floor masses. A stiffness matrix may be used, but is not necessary. Essentially, the method operates on a matrix of incomplete measurements of floor accelerations. In the special case of complete floor measurements of systems with linear dynamics and real modes, the principal components of this matrix are the modal responses. In the more general case of partial measurements and nonlinear dynamics, the method extracts a number of linearly-dependent components from Hankel matrices of measured horizontal response accelerations, assembles these components row-wise and extracts principal components from the singular value decomposition of this large matrix of linearly-dependent components. These principal components are then interpolated between floors in a way that minimizes the curvature energy of the interpolation. This interpolation step can make use of a reduced-order stiffness matrix, a backward difference matrix or a central difference matrix. The measured and interpolated floor acceleration components at all floors are then assembled and multiplied by a mass matrix. A sum (or weighted sum) of the resulting vector of inertial forces gives the base shear. The proposed algorithm is suitable for linear and nonlinear hysteretic structural systems.

Load rating is the process of determining the safe live-load carrying capacity of a bridge and is thus a major basis in prioritizing maintenance operations and allocation of resources. Traditionally, bridge evaluation standards provide two approaches to load rating, namely the analytical calculations and the empirical static load tests. Analytical load ratings are generally based on simplifying assumptions and may not closely reflect the current physical condition of the bridge. Empirical load tests provide a more realistic picture of live-load capacity of a bridge, but their application has been considerably limited by cost, time, test truck requirement, traffic interruption, and safety. Recently, a new approach named in this paper as “vibration-calibrated model-based load rating” has been investigated by a number of researchers. In this method, a vibration test is performed on the bridge and a refined finite element model is calibrated so as to replicate the observed modal response of the bridge. The calibrated model, which is adjusted to reflect the real in-situ condition and performance of the bridge, will then be used for the purpose of realistic load rating. This paper starts by reviewing the limitations of the current analytical and empirical methods and provides an in-depth explanation of the vibration-based method for load rating. Finally, recent research on this approach is reviewed and a discussion on the advantages, disadvantages and special considerations involved is presented. The areas that require more research and future work are also highlighted.

Built in 1993, the French Creek Bridge is located on highway 19 on Vancouver Island, BC, Canada. The bridge is part of the British Columbia Smart Infrastructure Monitoring System (BCSIMS), funded by the Ministry of Transportation and Infrastructure (MoTI) BC, Canada. The BCSIMS is a real-time seismic monitoring program that continuously assesses the seismic conditions of the selected bridges in BC. As part of this seismic monitoring program, an Ambient Vibration Test (AVT) was conducted in September 2014 to extract to modal properties (modal frequencies, modal damping ratios, and mode shapes) of the bridge. The Finite Element (FE) model of the bridge, developed in SAP2000, is updated using the extracted modal properties. The FE model will then be used to assess the seismic performance of the bridge in accordance with the new Canadian Highway Bridge Design Code, 2015, and this study only presents the results of the first of this assessment: the FE model updating of the bridge.

Vibration-based damage detection techniques offer interesting opportunities for Structural Health Monitoring of strategic structures and infrastructures, such as bridges, and they foster the shift from time-based maintenance to condition-based maintenance. However, effective vibration-based damage detection requires accurate estimates of the modal parameters of the monitored structure automatically obtained from the analysis of the operational response of the structure.In the present paper, the opportunities and limitations of automated output-only modal identification and modal-based damage detection for bridges are briefly reviewed. The results of an extensive experimental campaign on a scaled bridge model are then illustrated. The objective of the experimental tests was twofold. The dynamic response of the structure has been experimentally characterized for different support conditions (simply supported, with seismic isolation devices). After this first stage of analysis, an input ground motion has been applied by means of a couple of asynchronous shaking tables. As a result of the seismic input, the structure without seismic isolation devices was damaged. The effect of damage on the modal properties and the possibility to detect damage by means of selected damage features are discussed on the basis of the obtained experimental results.

Most of civil engineering structures are subjected to potential damages mainly due to dynamic oscillations induced by wind, rain or traffic. The aim of this paper is to combined Bridge Weigh-In-Motion (B-WIM) technic and Acoustic Emission (AE) monitoring in order to evaluate the health state of bridge structures. AE technique is able to determine location of active damage zones, if sufficient density of sensors is used. Within TRIMM European project, this technique was for the first time combined with Bridge-Weigh-In-Motion (BWIM) technique to correlate real traffic load level with registered cracking activity. Combination of AE and B-WIM techniques enables determining of load level at which nonlinearities start to develop. The technique provides an indication of progressing structural damage under operating traffic loads.

Knowledge of excitation loads on bridges are important for reliable design. Load models are however prone to uncertainties. Force identification using dynamic response measured on full-scale structures can be used to reduce the uncertainty. In this contribution, numerical simulations are performed to examine the feasibility of force identification on the floating pontoon Bergsoysund Bridge. We present a practical case study in which wave excitation forces and motion induced forces are estimated using only acceleration output. The sensor network considered represents the monitoring system currently installed on the bridge. A reduced order model with 26 modes is used to represent the structure in the identification. Wave force time series are generated by Monte Carlo simulations, and the acceleration response is obtained from a frequency domain solution of the equations of motion. The generated acceleration data is polluted with noise and subsequently used for identification. The results show that a joint input-state estimation algorithm is able to adequately identify a subset of hydrodynamic forces acting on the pontoons in the presence of both measurement and model errors. The translational forces are identified with a larger accuracy than the moments. Lastly, considerations and improvements for an analysis with experimental field data are presented.

Riveted steel bridges are common in the Norwegian and European railway network. Assessment of the current state is important to ensure continuous and safe operation of the railway. Furthermore, the prediction of remaining service life of these bridges is essential to allocate limited funds and resources to the most critical points in the infrastructure. Reliable and accurate numerical models are necessary tools to assess the current state and predict the remaining service life of these bridges.This paper presents work carried out in obtaining a validated finite element model of the Lerelva railway bridge. System identification was carried out with data driven stochastic subspace identification using data with both initial conditions from train passage and ambient excitation. The modal data from the system identification procedure was utilized in updating the finite element model.

Completed in 2013, the Hardanger Bridge is now the longest suspension bridge in Norway and it is among the most slender suspension bridges in the world. In the context of an extensive research project initiated by the Norwegian Public Roads Administration, the bridge was installed with a comprehensive monitoring system to investigate the wind characteristics at the site along with the dynamic response of the bridge. Wind velocities are measured using nine anemometers installed on different locations at the bridge span. A representative 10 min recording with high mean wind velocity and perpendicular wind direction is selected to present the preliminary results. Time series of turbulence components and one-point spectra are presented. Spatial properties of the wind field are studied by examining the coherences of turbulence components for several separation distances. Dynamic response of the bridge is measured using 20 accelerometers distributed along the main span. Time histories of the accelerations as well as the response spectra are presented. The relationship between bridge vibrations and wind measurements is discussed.

Constructed in 1983, the Portage Creek Bridge is a three span highway bridge located in Victoria, British Columbia (BC), Canada. This bridge is a part of a smart seismic monitoring program, British Columbia Smart Infrastructure Monitoring System (BCSIMS), which funded by the British Columbia Ministry of Transportation and Infrastructure (MoTI), Canada. The BCSIMS aims to continuously monitors the seismic conditions of the selected bridges on lifeline highways in British Columbia, and as part of this goal, an ambient vibration test was carried out on the bridge in September 2014 in order to update/calibrate the finite element model of the bridge in SAP2000. The updated model will then used to assess the seismic performance of the bridge in accordance with the Canadian Highway Bridge Design Code, 2015. This paper presents the first phase of the seismic performance assessment, which includes finite element model updating using the results of the ambient vibration test.

The demand for buildings and bridge structures with long spans and light weight which are able to sustain against dynamic loads are increasing in recent years. To overcome natural weakness of concrete in tension, prestressed concrete method is one of the best ways. Partially prestressed concrete structures fill the gap between fully prestressed concrete structures and conventional reinforced concrete (RC) structures which are now accepted and applied by many engineers. Although considerable effort has been made for evaluation of seismic-resistant of RC structures, little information is available on the behavior of monolithic partially prestressed concrete (PPC) and prefabricated partially prestressed concrete (IBS) structures against earthquake. In the present paper, seismic response of PPC and IBS frames are evaluated and damage plasticity theory is implemented in order to investigate the behavior of concrete in tension and compression. For this purpose, finite element model of the PPC and IBS frames is developed and pushover, cyclic and time history analysis are conducted to simulate the seismic response. Comparison of the obtained results from PPC and IBS frames with the conventional RC frames shows significant improvement for PPC and IBS structures subjected to both severe gravity and dynamic loads simultaneously.

In seismic design, buildings are designed to respond to strong earthquake ground motions inelastically. The engineering norm followed in design and analysis is to use constant modal viscous damping ratios to account for all energy dissipation aside from that arising from material nonlinearity. In general, equivalent linear models, which aim to capture primarily the peak response with typically 2–5 % of critical damping are employed. In this paper, we show that the effective viscous damping ratio can be estimated from the dynamic response of actual building structural systems without linearization of the load-deformation characteristics. The empirical method we present, which estimates the effective viscous damping ratios of buildings, has been applied to several laboratory specimens and actual buildings. We show that the effective viscous damping ratio in a low to mid-rise reinforced concrete (RC) building responding at its dominant mode (equivalent of fundamental mode in linear elastic systems) varies linearly with the effective period of its dominant mode. The practical use of the method is demonstrated using acceleration records obtained in two 9-story small-scale RC laboratory test specimens during a series of strong base motions. Fundamental mode envelopes of hysteretic responses, that is, the backbone curves for both structures are estimated by excluding higher mode effects from the measured responses.

This study assesses the implementation of locally resonant metamaterials in seismic isolation applications, by investigating their potential feasibility in low frequency bands. To this end, via adoption of both Blochs Theorem and classical vibration analysis, both one-dimensional and two-dimensional mass-in-mass lattices are analyzed in overlapping subbands and corresponding relations for the structural parameters are derived. The lattices are first examined in an infinite setup, for determining the arrangement of the resulting band gaps. Subsequently finite lattice configurations are investigated in the frequency and time domain. In this work, previous results are corroborated and additionally expanded to the 2D case. The parametric study that was carried out reveals interesting properties, particularly for low external-to-internal stiffness ratios of the unit cells comprising the lattices. Further investigation is required for confirming the feasibility of application of the resulting setups in full scale.

Metamaterial inspired structures, or metastructures, are structural members that incorporate periodic or non-periodic inserts. Recently, a new class of metastructures has been introduced which feature chiral lattice inserts. It was found that this type of inserts has frequency bandgaps which can be tuned by altering the geometry of the chiral lattice. Previous studies have shown that inserting non-periodic chiral lattices inside a beam-like structure results in efficient vibration attenuation at low frequencies. In the study presented in this paper, a genetic algorithm based optimization technique is developed to automatically generate chiral lattices which are tuned to suppress vibration in a flexible beam-like structure. Several parameters are incorporated in the optimization process such as the radius of circular nodes and characteristic angle as well as the spacing and distribution of circular inserts. The efficiency of the proposed optimization technique is verified analytically by numerical simulations.

An assessment of the extent of vibration transmission, attributable to non-structural partitions, was made using experimental tests and numerical simulations, for a recently constructed multi-storey reinforced concrete building. Mechanical excitation was provided to one floor. The extent of vibration on the floor above was found to be as high as 65 % of that recorded on the excited floor. A numerical model was also used to simulate the response to a person walking along a corridor. The extent of transmission was 9 % with partitions and 1.8 % without them. These findings imply that the vibration level on any given floor level will be due to excitation on that particular floor and also the not insignificant level of vibration that may be transmitted through structural and non-structural components due to concurrent activity from floors above and below. This issue of vibration transmission between floor levels is not addressed in any current guidance of floor vibration serviceability assessment and requires further detailed investigation.

This paper presents a case study using hybrid time- and frequency-domain identifications in a synergistic manner to develop models of a full-scale experimental base-isolated structure. This four-story reinforced-concrete building on an isolation layer (of rubber bearings, elastic sliding bearings, passive metallic yielding dampers, and controllable oil dampers) was designed and constructed at the large-scale Japanese NIED E-Defense earthquake engineering laboratory. A variety of sensors, including accelerometers, were mounted within the structure to measure building response to shake table excitations. While the building was ultimately subjected to historical and synthetic ground motions, the recorded table and building accelerations during a number of random excitation tests are used to identify the structure’s natural frequencies, damping ratios and mode shapes. The substantial damping provided by the isolation layer necessitates adopting a hybrid time- and frequency-domain approach for identification. The modes of the structure are separated by frequency content wherein lower frequency modes are identified using time domain approaches from the subspace identification family of methods and higher frequency modes are identified using frequency response functions. Individually, neither approach is able to successfully identify all of the desired modes but, through their combination, the modal properties of the structure are successfully characterized.

Future headquarter of an insurance company, the Allianz Tower is a 202 m high, 52-storey building currently topped out and belonging to the large CityLife development in Milan, Italy. The architectural design concept, by Arata Isozaki, is that of a slender, streamlined machine-building, with exposed structural and functional systems. The very significant slenderness and the intrinsic damping properties of the Tower make lateral and torsional response due to wind actions not negligible for structural analysis and occupant comfort. To mitigate these effects eight external viscous dampers were designed, and located at the base of four steel “struts”, stemming out of the surrounding plaza and podium at the base of the tower and connected to its main cores at the 11th floor. The design of the dampers and of the struts required refined analyses to be carried out. At the end of construction, two sets of Operational Modal Analysis (OMA) tests were carried out on the building, with and without dampers, to validate the design and construction process. Moreover, given the importance of the building, the Tower was equipped with a state-of-the-art continuous monitoring system acquiring data on all of the relevant structural and functional parts, including the dampers. The present paper will present the main structural features of the Tower, the main features of the design of the damping devices for wind comfort, the test set up for the dynamic OMA tests, followed by a discussion of their results.

A High Strength Reduced Modulus High Performance Concrete (HSRM-HPC) is being developed for the construction of railroad ties. This new material has the potential to produce ties with a longer life span than those built from traditional concrete because the reduced Young’s modulus could avoid stress concentrations. Railroad ties are commonly fabricated as prestressed concrete elements. The transfer length is an important parameter on these structural elements because the rail is placed toward the ends of the element. This paper uses Bayesian model updating to infer the transfer length with limited experimental data from a test performed in one prestressed beam. The material characteristics, experimental setup, and transfer length model are described in detail. Results show histograms of the transfer length obtained from the models considered.

The approach used to estimate dynamic properties of civil engineering structures normally comprise numerical modelling and much less frequently—experimental modal analysis. Hence, the work presented in this paper focuses on the later approach. The multi-input multi-output (MIMO) modal test for civil engineering structures is not commonly used and is rarely presented in the literature due to its cost and practical difficulties. This paper aims to show the advantages of adapting this exercise in the identification of the modal properties of a full-scale floor system. The floor is located on the first level of a building undergoing major refurbishment. The results presented in this paper are for a test conducted on the bare floor where no modifications were done to the as-built structural layout. A forced vibration test was undertaken with three electrodynamic shakers and 14 accelerometers. To excite the floor, the shakers were driven by statistically uncorrelated random excitation signals with frequency spans of 0–50 Hz. The testing demonstrated that the floor has closely spaced modes of vibration. The procedure for identifying the estimated modal properties from this experimental modal analysis exercise will be discussed.