International Journal of Structural Stability and Dynamics

This research aims to investigate the feasibility of using ambient vibration testing for system identification of an elevated water tank. To identify the natural dynamic properties, the experimental study is carried out on an elevated steel water tank located in Tehran. The tank is instrumented with a sensitive velocimeter sensor (microtremor), and the ambient velocity of the tank is recorded for 30min in three orthogonal axes. Employing the peak-picking method in the frequency domain, the fundamental frequency of the tank is determined as about 1.9Hz. Then, the numerical model of the tank is generated and calibrated based on the obtained data. In the primary modeling, the values of natural frequencies of the tank are in good agreement with the results of the ambient vibration data. This finding is judged to be reasonable considering no clear sign of corrosion in the steel material.

‘Que-Ti’ is an important component in typical Tibetan heritage timber buildings and it performs similar to corbel brackets connecting beam and column in modern structures. It transfers shear, compression and bending moment by slippage and deformation of components as well as limited joint rotation. A rigorous analytical model of ‘Que-Ti’ is needed for predicting the behavior of a timber structure under extreme loadings. Few researches have been done on this topic, particularly with the parameters describing the performances of this connection subjected to external loads. In this paper, a new temperature-driven multimodel approach is proposed to identify the stiffness parameters of a ‘Que-Ti’ connection in its operating environment. Models with nonlinear compression and rotational springs have been developed to take into account the change of mechanical behavior of the ‘Que-Ti’ affected by the temperature variation in typical heritage Tibetan buildings. The column–beam connection is modeled as two nonlinear rotational springs and one nonlinear compressive spring. Ambient temperature variation is treated as a force function in the input (temperature)–output (local mechanical strains) relationship, and stiffness identification is conducted iteratively via correlating the calculated strain responses with measured data. The nonlinear model of the joint is reproduced with a number of linear local models in different deformation scenarios that are corresponding to different temperature ranges. The stiffness parameters can be identified using a multimodel approach. Numerical results show that the method is effective and reliable to identify the nonlinear connection stiffness of the ‘Que-Ti’ accurately with the temperature change even with 10% noise in measurements. The monitoring data from a long-term monitoring system installed in a typical heritage Tibetan building is used to further verify the method. The experimental results show that the identified stiffness by the proposed method with nonlinear connection stiffness model can get better results than that obtained from the linear connection stiffness model.

Wood utility poles are widely applied in power transmission and telecommunication systems in Australia. Because of a variety of external influence factors, such as fungi, termite and environmental conditions, failure of poles due to the wood degradation with time is of common occurrence with high degree uncertainty. The pole failure may result in serious consequences including both economic and public safety. Therefore, accurately and timely identifying the health condition of the utility poles is of great significance for economic and safe operation of electricity and communication networks. In this paper, a novel non-destructive evaluation (NDE) framework with advanced signal processing and artificial intelligence (AI) techniques is developed to diagnose the condition of utility pole in field. To begin with, the guided waves (GWs) generated within the pole is measured using multi-sensing technique, avoiding difficult interpretation of various wave modes which cannot be detected by only one sensor. Then, empirical mode decomposition (EMD) and principal component analysis (PCA) are employed to extract and select damage-sensitive features from the captured GW signals. Additionally, the up-to-date machine learning (ML) techniques are adopted to diagnose the health condition of the pole based on selected signal patterns. Eventually, the performance of the developed NDE framework is evaluated using the field testing data from 15 new and 24 decommissioned utility poles at the pole yard in Sydney.

Structural health monitoring data has been widely acknowledged as a significant source for evaluating the performance and health conditions of structures. However, a holistic framework that efficiently incorporates monitored data into structural identification and, in turn, provides a realistic life-cycle performance assessment of structures is yet to be established. There are different sources of uncertainty, such as structural parameters, computer model bias and measurement errors. Neglecting to account for these factors results in unreliable structural identifications, consequent financial losses, and a threat to the safety of structures and human lives. This paper proposes a new framework for structural performance assessment that integrates a comprehensive probabilistic finite element model updating approach, which deals with various structural identification uncertainties and structural reliability analysis. In this framework, Gaussian process surrogate models are replaced with a finite element model and its associate discrepancy function to provide a computationally efficient and all-round uncertainty quantification. Herein, the structural parameters that are most sensitive to measured structural dynamic characteristics are investigated and used to update the numerical model. Sequentially, the updated model is applied to compute the structural capacity with respect to loading demand to evaluate its as-is performance. The proposed framework’s feasibility is investigated and validated on a large lab-scale box girder bridge in two different health states, undamaged and damaged, with the latter state representing changes in structural parameters resulted from overloading actions. The results from the box girder bridge indicate a reduced structural performance evidenced by a significant drop in the structural reliability index and an increased probability of failure in the damaged state. The results also demonstrate that the proposed methodology contributes to more reliable judgment about structural safety, which in turn enables more informed maintenance decisions to be made.

Simultaneous estimation of both the location and force history of an impact applied on a lattice truss core sandwich panel is inversely carried out utilizing velocity signals collected by means of a scanning laser Doppler vibrometer. The algorithm assumes that several impact forces are exerted concurrently on a number of specified locations on a panel, provided that the magnitude of all impact forces but one is actually equal to zero. This condition equates to a scenario where an impact occurs at only one location. The purpose is therefore to detect the actual impact location among all potential locations, together with its force history, through minimizing error functions. Two algorithms, the one-to-one (even-determined) approach and the superposition approach, are considered. The one-to-one approach solves the reconstruction problem independently for each pair of impact and measurement points. However, in the superposition approach, the impact forces at all potential locations are concurrently reconstructed through a single matrix equation. It is shown that the one-to-one approach fails to detect the true impact location while the superposition approach recognizes the actual impact location based on some qualitative evaluating criteria. Adopting the superposition approach, for a problem with four possible impact locations, two scenarios one with four and one with 12 measurement points, are investigated. It is observed that the additional measurement points do not necessarily enhance the efficiency and accuracy of the proposed method. It is found that different arrangements of measuring points lead to identification of the location and the magnitude of the impact force, though the use of four evenly distributed measurement points seems to be most effective in simultaneous identification of the location and magnitude of the impact force. Further, a quantitative index based on the concept of similarity search for time-series using wavelet transformation is proposed and it is demonstrated that the index can successfully identify the true impact force location in a fully automated way.

This study proposes a time-domain spectral finite element (SFE) method for simulating the second harmonic generation (SHG) of nonlinear guided wave due to material, geometric and contact nonlinearities in beams. The time-domain SFE method is developed based on the Mindlin–Hermann rod and Timoshenko beam theory. The material and geometric nonlinearities are modeled by adapting the constitutive relation between stress and strain using a second-order approximation. The contact nonlinearity induced by breathing crack is simulated by bilinear crack mechanism. The material and geometric nonlinearities of the SFE model are validated analytically and the contact nonlinearity is verified numerically using three-dimensional (3D) finite element (FE) simulation. There is good agreement between the analytical, numerical and SFE results, demonstrating the accuracy of the proposed method. Numerical case studies are conducted to investigate the influence of number of cycles and amplitude of the excitation signal on the SHG and its performance in damage detection. The results show that the amplitude of the SHG increases with the numbers of cycles and amplitude of the excitation signal. The amplitudes of the SHG due to material and geometric nonlinearities are also compared with the contact nonlinearity when a breathing crack exists in the beam. It shows that the material and geometric nonlinearities have much less contribution to the SHG than the contact nonlinearity. In addition, the SHG can accurately determine the crack location without using the reference data. Overall, the findings of this study help further advance the use of SHG for damage detection.

Drive-by bridge inspection using acceleration responses of a passing vehicle has great potential for bridge structural health monitoring. It is, however, known that the road surface roughness is a big challenge for the practical application of this indirect approach. This paper presents a new two-step method for the bridge damage identification from only the dynamic responses of a passing vehicle without the road surface roughness information. A state-space equation of the vehicle model is derived based on the Newmark-$\beta$ method. In the first step, the road surface roughness is estimated from the dynamic responses of a passing vehicle using the dual Kalman filter (DKF). In the second step, the bridge damage is identified based on the interaction force sensitivity analysis with Tikhonov regularization. A vehicle–bridge interaction model with a wireless monitoring system has been built in the laboratory. Experimental investigation has been carried out for the interaction force and bridge surface roughness identification. Results show that the proposed method is effective and reliable to identify the interaction force and bridge surface roughness. Numerical simulations have also been conducted to study the effectiveness of the proposed method for bridge damage detection. The vehicle is modeled as a 4-degrees-of-freedom half-car and the bridge is modeled as a simply-supported beam. The local bridge damage is simulated as an elemental flexural stiffness reduction. Effects of measurement noise, surface roughness and vehicle speed on the identification are discussed.The results show that the proposed drive-by inspection strategy is efficient and accurate for a quick review on the bridge conditions.

Many existing damage identification or quantification methods can be employed only if the internal and external mass changes are negligible when tested at two different states of a structure. This paper presents a new Modal Kinetic Energy (MKE)-based method to detect and quantify damage using modal properties of structures, which can be employed even in situations when mass change is not more than a certain extent. A new damage sensitivity parameter has been developed using measured modal characteristics of baseline structure. The MKE change (MKEC) concept is then employed to locate damage and to estimate relative perturbation at each element. The relative damage extent vector is estimated by searching the best correlation between the analytical and experimental MKEC vectors with the help of genetic algorithm optimization tool. The extent of damage is calculated after computing damage scaling coefficient using measured eigenvalue change vector. A numerical study is carried out on a simply supported single span beam to confirm its performance under various test conditions. The robustness of the proposed MKE method and the significance of mass variation in the damage detection approach are evaluated by comparing the damage quantification results with a traditional approach. Finally, the proposed damage detection method is applied on a two-span simply supported beam for single and multiple damage scenarios by extracting the modal properties experimentally. The results revealed that the proposed approach is capable of detecting and estimating single and multiple damages with reasonable accuracy even in moderate noise contaminated and mass change environments.

This paper presents an enhanced method to locate and quantify damage in beam-like structures using changes in deflections estimated from modal flexibility (MF) matrices. The method is developed from explicit relationship between a series of MF-based deflection change vectors and the damage characteristics. Based on this, three damage locating criteria are defined and used to detect and locate damage. Once the damage is located, its severity is estimated conveniently from a closed-form function. The capability of the proposed method is examined through numerical and experimental verifications on a steel beam model. The result shows that the method accurately locates and quantifies damage under various scenarios using a few modes of vibration, with satisfactory or even better results compared to those obtained from traditional static deflection-based method. The performance of the proposed method is also compared with three well-known vibration-based damage detection methods using changes in MF and modal strain energy. It is found that the proposed method outperformed the other three methods, especially for multiple damage cases. As beams can represent various structural components, the proposed method provides a promising damage identification tool targeting the application to real-life structures.

Nonlinear characteristics in the dynamic behaviors of civil structures degrade the performance of damage detection of the linear theory based traditional time- and frequency-domain methods. To overcome this challenge, this paper proposes a damage detection approach for nonlinear structures based on Variational Mode Decomposition (VMD). In this approach, the measured dynamic responses from nonlinear structures under earthquake excitations are adaptively decomposed into a finite number of monocomponents by using VMD. Each decomposed mono-component represents an amplitude modulated and frequency modulated (AMFM) signal with a limited frequency bandwidth. Hilbert transform is then employed to identify the instantaneous modal parameters of the decomposed monomodes, including instantaneous frequencies and mode shapes. Based on the identified modal parameters from the decomposed structural dynamic responses, two damage indices are defined to identify the location and severity of structural damage, respectively. To validate the effectiveness and accuracy of the proposed approach, a nonlinear seven-storey shear building model with four different damage cases under earthquake excitations is used in the numerical studies. In experimental verifications, data from shake table tests on a 12-storey scaled reinforced concrete frame structure with different earthquake excitations are analyzed with the proposed approach. The results in both numerical studies and experimental validations demonstrate that the proposed approach can be successfully applied for nonlinear structural damage identification.

Vibration-based methods can be used to detect damage in a structure as its vibration characteristics change with physical changes in the structure. Arch bridge is a popular type of bridge with rather complex vibration characteristics which pose a challenge for using existing vibration-based methods to detect damage in the bridge. Further, its particular geometry with a curved arch rib and vertical members (either in compression or tension) to support the horizontal deck makes the process of damage quantification using vibration-based methods harder and challenging. This paper develops and presents a vibration-based method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to locate and quantify damage in the rib of deck-type arch bridge, which is the most important load bearing component in the bridge. Principal component analysis, which is performed to reduce the dimension of original FRF data series and to obtain limited principal component analysis (PCA)-compressed FRF data is used in the development of the proposed method. FRF change, which is the difference in the FRF data between the intact and the damaged structure, is compressed to a few principal components and fed to ANNs to predict the location and severity of structural damage. The process and the hierarchy of developed ANN systems are presented, including the “fusion network” concept, which individually analyses FRF-based damage indicators separated by sensor locations. Finally, results obtained for many tested damage cases (inverse problems) are presented, which demonstrate the applicability of the proposed method for locating and quantifying damage in the rib of deck type arch bridge.

Constructing a damage-sensitive factor (DSF) is one of the key steps in structural damage detection. In this paper, innovation series extracted from the auto-regressive conditional heteroscedasticity (ARCH) model are proposed to construct a DSF, which is defined as the standard deviation of innovation (SDI). A three-story shear building structure is used to demonstrate and verify the performance of the proposed method, and the results are compared with the standard deviation of the residuals (SDR) based on an auto-regressive (AR) model. In the proposed method, the AR model is established using the acceleration responses obtained from the reference and test states. The residual series are then extracted for fitting the SDR. Subsequently, the ARCH model is constructed based on the residual series from the AR model, and a new DSF of SDI is defined. This study focuses on analyzing the accuracy of fitting AR model and ARCH model to vibration response data via the normal probability distribution, and identifying the characteristics of the residual and innovation series. The mean squared error (MSE) is used as the loss function to calculate the loss on residual and innovation series from the AR model and ARCH model, respectively. The results demonstrate that the SDR can be used for nonlinear damage detection. However, the proposed SDI can provide more accurate nonlinear damage identification and is robust to varying environmental condition and small damages. Thus, the innovation series developed based on ARCH model are promising for expressing and constructing nonlinear DSFs.

An enhanced method to determine the best-fit auto-regressive model (AR model) for structural damage identification is proposed in this paper. Whereby, two parameters of the model, including the number of model order and the window size of data, are analyzed simultaneously in order to accomplish the optimized values by means of Akaike’s Information Criterion (AIC) algorithm. The damage condition of structures can be detected by defined damage indicators obtained from the first three AR coefficients of the best-fit AR models. The ability of the proposed damage identification method is compared with the process that only utilizes conventional AR models without concern of parameter selection. The proposed method is verified using experimental data previously collected from a large-size bridge structure in the Structural Laboratory at the University of Central Florida. The results indicate that this method can detect and locate damage more effectively.

Irrespective to how well structures were built, they all deteriorate. Herein, deterioration is defined as a slow and continuous reduction of structural performance, which if prolonged can lead to damage. Deterioration occurs due to different factors such as ageing, environmental and operational (E&O) variations including those due to service loads. Structural performance can be defined as load-carrying capacity, deformation capacity, service life and so on. This paper aims to develop an effective method to detect and locate deterioration in the presence of E&O variations and high measurement noise content. For this reason, a novel vibration-based deterioration assessment method is developed. Since deterioration alters the unique vibration characteristics of a structure, it can be identified by tracking the changes in the vibration characteristics. This study uses enhanced autoregressive (AR) time-series models to fit the vibration response data of a structure. Then, the statistical hypotheses of chi-square variance test and two-sample $t$-test are applied to the model residuals. To precisely evaluate changes in the vibration characteristics, an integrated deterioration identification (DI) is defined using the calculated statistical hypotheses and a Hampel filter is used to detect and remove false positive and negative results. Model residual is the difference between the predicted signal from the time series model and the actual measured response data at each time interval. The response data of two numerically simulated case studies of 3-storey and 20-storey reinforced concrete (RC) shear frames contaminated with different noise contents demonstrate the efficacy of the proposed method. Multiple deterioration and damage locations, as well as preventive maintenance actions, are also considered in these case studies. Furthermore, the method was successfully verified utilizing measured data from an experiment carried out on a box-girder bridge (BGB) structure.

In this research, ultrasonic tests were performed on a prestressed concrete box-girder model to identify the prestressed force according to the acoustoelastic theory. During the tests, the ultrasonic wave was generated using piezoelectric transducers and emitted to the prestressed concrete bridge model. The concrete bridge model was subjected to three different levels of prestress force (PF), which were limited to about 30%, 50% and 80% of the ultimate tensile strength. The experimental results showed the increase in prestress force level leads to an increase in the relative change in the wave velocity and the amplitude energy of the ultrasonic wave which proved the acoustoelastic effect theory. This study contributes to the knowledge of the acoustoelastic behavior of the prestressed concrete and presents the capability of the ultrasonic system in evaluating the stress state in the prestressed concrete bridge.

The composite action between the layers of steel and concrete is governed by the shear connection. Because of the complicated interconnection behavior of these composite layers, it is difficult to detect damage in the composite structures, especially, the interfacial integrity of the two layers. In this paper, anovel method has been developed for structural damage identification of composite structures based on a steel-concrete composite beam element with bonding interface. In displacement-based finite element (FE) formulation, three damage indicators have been embedded into stiffness matrix of the composite beam that are defined as a stiffness reduction in the concrete, steel and interface layers. An algorithm-based on recursive quadratic programming has been proposed to identify structural damage in the composite beam from static measurements. The analytical FE model is validated by adapting its static responses in undamaged state with those obtained from an equal experimental model as well as a FE model developed in commercial software ABAQUS. A convergence study is conducted to determine the number of the composite beam FEs. To verify the proposed method, the static responses of the FE model with different damage cases at a given loading are calculated, and the measurements are simulated by adding different levels of white noise. Then, the proposed algorithm is applied to identify damage of the composite beam. The effects of measurement noise, loading location and amplitude, measurement numbers and the sizes of FE mesh on the identified results have been investigated. The numerical results show that this method is efficient and accurate to separately identify small damage in the concrete slab, and the steel girder and bonding interface of the composite beam.

Offshore wind turbines (OWTs) have emerged as a reliable source of renewable energy, witnessing massive deployment across the world. While there is a wide range of support foundations for these structures, the monopile and jacket are most utilized so far; their deployment is largely informed by water depths and turbine ratings. However, the recommended water depth ranges are often violated, leading to cross-deployment of the two foundation types. This study first investigates the dynamic implication of this practice to incorporate the findings into future analysis and design of these structures. Detailed finite element (FE) models of Monopile and Jacket supported OWTs are developed in the commercial software, ANSYS. Nonlinear soil springs are used to simulate the soil-structure interactions (SSI) and the group effects of the jacket piles are considered by using the relevant modification factors. Modal analyzes of the fixed and flexible-base cases are carried out, and natural frequencies are chosen as the comparison parameters throughout the study. Second, this study constructs a few-parameters SSI model for the two FE models developed above, which aims to use fewer variables in the FE model updating process without compromising its simulation quality. Maximum lateral soil resistance and soil depths are related using polynomial equations, this replaces the standard nonlinear soil spring model. The numerical results show that for the same turbine rating and total height, jacket supported OWTs generally have higher first-order natural frequencies than the monopile supported OWTs, while the reverse is true for the second-order vibration modes, for both fixed and flexible foundations. This contributes to future design considerations of OWTs. On the other hand, with only two parameters, the proposed SSI model has achieved the same accuracy as that using the standard model with seven parameters. It has the potential to become a new SSI model, especially for the identification of soil properties through the model updating process.

This paper proposes a real-time monitoring framework for a landslide susceptibility area based on wireless sensor network using multiple Unmanned Aerial Vehicles (UAVs). Many researchers have considered building a landslide susceptibility map to distinguish different levels of landslide susceptible zones. However, to prevent damage from landslides, it is more important for the disaster control center to identify the time and location of the landslide occurrence in those highly susceptible areas. Hence, a rain-triggered landslide monitoring system is proposed herein for local mountain areas. First, a wireless sensor network framework is constructed to inform the control center as immediately as possible when landslides occur. Second, multiple UAV sensors will be responsible for collecting the stereo images of the slope in highly sensitive zones on schedule. Based on the stereo images and the binocular model, in-depth information can be obtained. With the depth information and Speeded Up Robust Features (SURF) detection, the key point characteristic information is constructed as the input data for Support Vector Machine (SVM). An SVM algorithm is designed with Python program language and executed in real time. Using this algorithm, the real-time images collected by UAVs and the landslide warning information will be sent to the control center for further analysis. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method.