Changes In Structural Stiffness Research Articles

Tuned two-mass dampers for vibration control of offshore platforms

A Tuned Two-Mass Damper (TTMD) for minimizing vibrations of an offshore platform is developed in this study. TTMD is a passive vibration absorber, and it consists of two parallel undamped tuned mass dampers connected by a dashpot. The influences of its parameters on the vibration absorption capacity are studied through numerical examples. Next, the optimum parameters of TTMD are found, and the performance and robustness of TTMD are determined. The results from this study show that, for absorbing the structural vibration, an optimum TTMD is more effective than a conventional TMD with the same weight as TTMD. In other words, using TTMD significantly reduces the added weight to the main structure compared with using TMD based on the same condition and obtained effectiveness. Moreover, while maintaining the same performance and weight, an optimum TTMD is much more robust than an optimum TMD for resisting the change in structural stiffness.

Damage detection of frame structure using a novel time-domain regression method

Shear structure model is the most frequently used to model for the damage detection of frame building structures. However, due to the existence of modelling error, using a shear structure model to perform damage detection of a complex frame structure often results in inaccurate detection results. In this paper, a novel reduced model for the frame is proposed, which converts a multi-story multi-bay plane frame into a beam-like model, having one translational and two rotational degrees-of-freedom for each floor. Based on the new model, a novel time-domain regression method (TDRM) was established using the spectral density function between the horizontal acceleration of the frame floor and the reference response to identify the equivalent layer stiffness and damping parameters. Finally, a five-story two-bay frame structure is used to demonstrate the efficacy of the proposed time-domain regression method of estimating structural parameters and identifying structural damage.The results show that this method can identify, locate, and quantify the structural stiffness changes accurately.

Collapse analysis and robustness assessment of dropped-story steel frame structures based on structural vulnerability theory

ABSTRACT This paper proposes an improved analysis method based on the theory of structural vulnerability, which incorporates structural redundancy and comprehensively analyzes structural vulnerability and robustness by considering the changes in structural stiffness and redundancy after damage to structural members under conventional loading. The improved method takes into account the changes in the strain energy of the structure after the damage of the structural members, which can be more in line with the actual situation of continuity collapse, and solves the complex process of traditional structural vulnerability theory that requires clustering and unclustering, greatly reducing the workload as well as solving the problem of not being able to find the critical members of the structure. The improved method is used to analyze the vulnerability of a 5-story dropped-story steel frame structure, to find the critical members of the structure based on the test data simulated by ABAQUS, to evaluate the robustness, and to propose improvement measures. The results show that the improved vulnerability assessment method is more reasonable and effective and can be applied to different steel frame structures.

Real-Time Structural Damage Detection Using EMI under Varying Load a and Temperature Conditions

Structural Health Monitoring (SHM) is a critical aspect of maintaining the safety and integrity of infrastructure. In recent years, there has been a significant shift towards adopting innovative techniques, and one of the most promising methods is Electromechanical Impedance (EMI). EMI involves the utilization of piezoelectric transducers to assess the health of structures in real-time by examining changes in their electrical characteristics. The presence of load causes a change in structural stiffness, which alters the resonant characteristics of the structure. Understanding how external factors like load and temperature influence the electrical impedance of these sensors is essential for its reliable application in damage detection. This article presents an experimental and numerical study to investigate the effects of load and temperature on the electrical impedance of a piezoelectric sensor used in the electromechanical impedance (EMI) technique. The experimental setup uses an impedance analyzer (Agilent 4294A model) to measure the in-situ EMI of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. The numerical model uses ANSYS software to simulate an aluminum beam at varying temperatures. The results show that the load and temperature have a significant effect on the impedance of the transducer. However, it is shown that it is still possible to detect damage using EMI even under varying load and temperature conditions. The results also show that the accuracy of EMI-based damage detection can be improved by using temperature and load compensation techniques.

Experimental study on the mechanical properties of longitudinal joints of shield tunnels under large deformation conditions

Known to be the weakest part of the shield tunnel lining rings, the longitudinal joints render the lining rings prone to large deformation and related diverse defects under nonuniform pressure loading, thereby seriously compromising the safety, serviceability and durability of the tunnel structure. In light of the paucity of studies regarding the performance of joints under large deformation, this paper presents a series of experiments examining the damage mechanism of longitudinal joints under large deformation and investigating the effects of the bolt strength, initial joint configuration, and loading level on the mechanical properties of longitudinal joints. Moreover, a finite element model was developed for the joint, capable of simulating large deformation and bolt yielding. Results show that the specimen’s failure is characterized by concrete cracking and spalling at the compression side of the joint, and/or the thread stripping or fracture failure of the bolts. Besides, an increase in bolt strength can increase the bending capacity of the joint. The discontinuous joint configuration determines the compression height of the joint section cannot be continuously changed, which results in an abrupt change in structural stiffness and a four-stage change in bolt strain. Changes in the loading level not only affect the joint’s bending capacity to some extent, but also alter how cracks develop near the joint. Furthermore, the relationship between the joint opening angle and the segmental rotation angle proves the rigid body deformation characteristics of the segment before the bolts yielded. With the segmental rotation angle increased, the maximum joint opening width and mid-span deflection increased linearly, but the compression height decreased meantime. In addition, the relationship between the bolt strain and the segmental rotation angle exhibited a two-stage linearity after the bolt began to be stressed.

Substructure-level damage identification based on the spectrum-probability space of the transmissibility function

In structural health monitoring (SHM), the measured data is insufficient to accurately identify the potential damages when the measurements are contaminated by noise. In this study, a spectrum-probability space-based methodology is developed to manage the uncertainty in damage detection, integrated with the substructure division strategy to localize damage. The transmissibility function (TF), that is an output-only quantity and sensitive to changes in structural stiffness, is used to detect an anomaly. The variation of the probability-spectrum space of a TF is used as the damage indicator, and it is measured by a novel damage indicator, the spectrum-weighted Kullback-Leibler divergence. This indicator jointly considers the changes in spectrum density and statistics of a TF due to damage. The damaged sub-domain of a structure is subsequently identified out from all monitored sub-domains. Numerical and experimental models are tested to verify the approach in the ambient and impulse load conditions. The results demonstrate that the developed method can correctly detect anomalies and localize the damaged substructure with high sensitivity and robustness, and it is convenient to implement in practice.

Story drift and damage level estimation of buildings using relative acceleration responses with multi‐target deep learning models under seismic excitation

AbstractDamage detection is one of the primary purposes of structural health monitoring to inform catastrophic risks of structures right after extreme loadings such as earthquakes and hurricanes. In structural design codes, story drifts are considered as an indicator to estimate the damage states of structures. For instance, when the story drift ratios achieve 0.2‐0.4%, light damage may be present in a building. In addition, the remaining stiffness ratios can also reveal the damage levels of a structure. Previous studies have shown that structural stiffness changes can affect the frequency responses of structures, for example, changing the locations of poles in frequency response functions. In this research, two multi‐target neural network models are developed to concurrently estimate story drifts and remaining stiffness ratios using floor accelerations under seismic excitation. One of the multi‐target neural network models focuses on developing a physics‐guided loss function with a parallel model combination. Meanwhile, the other neural network model sequentially integrates two deep learning approaches by transfer learning. For example, the long short‐term memory units estimate story drift responses from floor accelerations. Then, the short‐time Fourier transform layers of floor accelerations yield the remaining stiffness ratio estimation. The proposed models are numerically investigated and experimentally verified. As a result, both models can estimate story drift and remaining stiffness ratio using the proposed neural network models.

Parametric design methods development for the comparison of American football faceguards using validated structural stiffness models

Recent advances in headgear design research have sought to inform athletes’ safety related decisions by ranking headgear systems according to impact performance. These rankings have provided athletes with greater agency in their safety-related decisions. Despite these improvements, little quantitative information exists to compare faceguard performance. Using validated structural stiffness finite element models, this study sought to develop a parametric design approach that could be consistently applied to faceguards of different qualitative categories and of different helmet-compatible series. The methods presented in this study detail the objective measurement techniques and parameters of interest used to fully define three common American football faceguards. The results of this study indicate an ability to define parameters consistently for faceguards of different qualitative categories and of different helmet-compatible series. The high degree of correlation between mass and structural stiffness indicates expected model performance – providing increased confidence in results. Intuitively, the greatest effect on mass and structural stiffness was the size of the diameter of the main bars. Increases in mass were achieved with minimal changes in structural stiffness. Conversely, increases in structural stiffness were achieved with minimal changes in mass. These results have implications for manufacturers as some faceguards, such as those classified as “overbuilt,” are banned – in part – for their weight. Future work should continue to compare manufacturers’ original designs and investigate other metrics to further quantify performance and safety for athletes. This tool may be used to improve new faceguard designs by comparing new models to faceguards allowed for use.

Welding sequence optimization using the strain direct boundary method based on the welding induced change in structural stiffness

Welding sequence optimization using the strain direct boundary method based on the welding induced change in structural stiffness

Comparison of measured strain and thermal effects during reactor building leakage rate tests of a nuclear power plant

With nuclear reactor power plants, Reactor Building (RB) Leak-Rate Tests (LRTs) are conducted at regular intervals to confirm the air leak tightness of a containment structure. These LRTs provide an excellent opportunity to study the containment structure’s behaviour when subjected to internal pressure, thereby evaluating the structural performance of the containment structure. However, significant time-dependent fluctuations are observed in the measured strains due to the contribution of thermal strain induced by the surrounding environment. In order to accurately study the mechanical behavior of the containment structure, the thermal strain must be separated from the measured total strain. In this study, environmental data was collected during an RB LRT. Finite element analyses were carried out by utilizing the environmental conditions to predict the thermal strain of the containment structure. The predicted thermal strain was then subtracted from the measured total strain, so the underlying mechanical strain induced by the internal pressure could be approximated. The mechanical behavior of a containment structure during LRTs over an eighteen-year period is compared to understand the overall aging effects of a CANDU®11CANDU (CANada Deuterium Uranium Reactor) is a registered trademark of the Atomic Energy of Canada Limited (AECL), used under exclusive license by Candu Energy Inc. containment structure. Only a slight change in structural stiffness was observed over eighteen years though further study is suggested to confirm this. This paper outlines how field data collected during LRTs can be used to provide insight into understanding the long-term behavior of a containment structure.

Damage localization for prefabricated bridges group using the area-ratio of the strain time-history curve

The prefabricated bridges group refer to several medium- and small-span prefabricated beam bridges locating in a continuous elevated corridor. It is usually challenging to accurately localize the damage in all bridges within one group due to the lack of monitoring data of bridges in the healthy state. To address this issue, a damage localization method for prefabricated bridges group is proposed based on the area-ratio of the strain time-history curve. First, using the definition of the bridge strain influence line, an area equation of the strain time-history curve is derived theoretically under a moving load for a simple beam bridge and a continuous beam bridge. Second, a damage localization index is established based on the area-ratio of the strain time-history curve by analyzing the constitutive characteristics of the area equation of the strain time-history curve at each measurement point. On this basis, a normalization method is presented for the damage localization index at all measurement points, and then the normalized index is implemented to accurately localize damage in all bridges. Finally, the effectiveness and anti-noise performance of the proposed method are demonstrated through both numerical examples and experiment. The proposed method does not need historical health data or a finite element model (FEM) as the reference and can transform strain time-history curves with different amplitudes from different positions along bridges into a unified normalized index that relates to structural stiffness changes only, which is especially suitable for structural damage localization of long prefabricated bridges group.

Improving Electromechanical Impedance Damage Detection Under Varying Temperature

The field of structural health monitoring has seen a fundamental shift in recent years, as researchers strive to replace conventional non-destructive evaluation techniques with smart material-based techniques. Perhaps the most promising of smart material techniques for developing structural health monitoring (SHM) systems is electromechanical impedance (EMI) which can be used for real-time structural damage assessment. In EMI, mechanical resonances of structure can be seen in electrical characteristics of piezoelectric transducers due to electromechanical coupling of transducer with the structure. Existence of damage will cause a structural stiffness change and therefore the resonant characteristics of the structure will be altered. This article presents an experimental and numerical study to investigate the effects of notch damage with temperature on the electrical impedance of the piezoelectric sensor used in the EMI technique. The practical implementation of the compact EMI method utilizes as its main apparatus an impedance analyser (Model Agilent 4294A) that reads the in-situ EMI of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. The finite element modelling used ANSYS software three-dimensional (3D) capability to simulate an aluminium beam at varying temperatures. Real-time monitoring of the structure is achieved based on harmonic measurements. The results conclusively showed that the proposed temperature compensation technique eliminates the results ambiguity and enabled the EMI system to detect small damages that were otherwise indiscernible.

RC Medium-Rise Building Damage Sensitivity with SSI Effect.

Global vibration-based methods in the field of structural health monitoring are intended to capture structural stiffness changes of buildings or other civil engineering structures. Natural frequencies of buildings or bridges are commonly used parameters to monitor these stiffness changes. Therefore, it is essential to clarify the limit at which this method is no longer sensitive enough to be useful for structural health monitoring purposes. This paper numerically investigates the effect of structural damage and soil–structure interaction on cellular-type reinforced concrete buildings’ natural frequencies. These buildings are a common housing stock of Eastern Europe but are rarely investigated in this context. Comparisons with a reinforced concrete frame and infill structure building are made. Finite element models representing three structural system types of nine-story reinforced concrete buildings were used for the numerical simulations. Furthermore, a five-story finite element model was used for a damage sensitivity comparison. It is established that, for cellular-type structure buildings to detect damage comparable to that investigated in the paper, structural health (fixed base model frequency) should be monitored directly. Then, a statistical significance level for frequency changes of no more than 0.1% should be adopted. Conversely, the rocking frequency is a very sensitive parameter to monitor building base condition changes. These changes are often a cause of the cracking of building elements.

Field experiments and numerical simulations for two types of steel tube lining structures under high internal pressure

This paper presents the mechanical behavior of two types of steel-tube lining structures in hydraulic shield tunnels. Field experiments were carried out based on the Pearl River Delta Water Resources Allocation Project. Accordingly, 3D finite element models were established to study the ultimate bearing capacity and failure mechanism of the structures. The experimental and numerical results indicated that self-compacted concrete cracking will cause structural stiffness changes and stress redistribution. In the superimposed steel tube lining, the internal pressure is shared by the segments and steel tube, while in the detached steel tube lining, the internal pressure is mainly borne by the steel tube due to the membrane. Both lining structures remained safe under the designed internal pressure, and the segment bolt stress was the final control index to judge the failure of the system. With increasing surrounding rock strength, the ultimate bearing capacity of the structure also increased.

Damage detection for bridge structures under vehicle loads based on frequency decay induced by breathing cracks

Concrete cracking is one of the most common types of damage in bridges. It is essential to detect the damage early to avoid irreparable accident. In order to resolve the issue, a rapid damage detection method without base-line data is proposed for bridge structures. Vehicles passing across the damaged area of the bridge will lead to the opening of partial concrete cracks which closed without vehicle loads. The opening of cracks will cause a change of structural stiffness, which is reflected in decay of bridge frequency in a short time. Firstly, the influence factors of bridge frequency are introduced, and the influence of vehicle on the nth frequency of the bridge in vehicle-bridge interaction system (VBI) is deduced theoretically. Then, the damage index D based on the frequency decay induced by breathing cracks and the flowchart of damage detection are proposed, and the efficacy of the method is verified numerically. Finally, the proposed method is validated by experiments and a parametric analysis is carried out. Results show that breathing crack type damage in concrete bridges can be identified using this method, and the damage index is positively correlated with the extent of damage.

Seismic fragility analysis of nuclear power plants based on the substructure method

This paper focuses on seismic fragility analysis of the AP1000 Nuclear Power Plant (NPP) with a substructure (SUB) model. To improve the efficiency of the analysis and calculation, this study treats the steel vessel containment (SVC) and equivalent equipment as a substructure and connects them with the residual structure to form an SUB model. The SUB model is verified to determine its rationality. Based on the verified model, 160 working conditions are calculated, and an incremental dynamic analysis (IDA) is performed. The AP1000 NPP is divided into 11 zones and 6 layers to investigate the seismic fragility analysis in detail. The results illustrate that (i) the calculation time of the SUB model is reduced by 1/4 compared to that of the full finite element model of the AP1000 NPP (FULL model) under the premise of ensuring calculation accuracy; (ii) the divided zones provide an accurate judgment basis for the failure mode and damage state directly; (iii) for the reinforced concrete shield building (RCSB), the damage gradually expands upon a sudden change in structural stiffness at the upper junction of the reinforced concrete auxiliary building (RCAB); (iv) seismic fragility analysis of the RCAB presents the form of gradual reduction of damage from the upper layer to the lower layer.

Robust improvement of the asymmetric post-buckling behavior of a composite panel by perturbing fiber paths

The buckling behavior of structures is highly sensitive to imperfections, i.e., deviations from the geometry and material properties of the ideal structure. In this paper, an approach is presented in which the effects of spatially varying fiber misalignments in composite structures are assessed through random field analysis and are subsequently used to improve the structure while simultaneously making it more robust to fiber misalignments. Effects of misalignments are quantified by applying random fields on the structure, which represent fiber misalignments. Using analyses of the effect of the random local stiffness changes due to fiber misalignments, a pattern of the relative influence these local changes have on the buckling load is created. By applying a small change to local fiber orientation corresponding to this pattern to the original structure, the performance of the design is improved. Additional stochastic analyses are performed using the improved design, reanalyzing the effects local fiber misalignments have on the structural performance and the subsequent changes in robustness. Stochastic results show an overall increase in the mean buckling load and a reduction in the coefficient of variation in the analysis of the perturbed structure. The approach is applied to a composite panel exhibiting asymmetric post-buckling behavior, i.e., having an unstable post-buckling branch and an (initially) stable branch. Results show that perturbations in the fiber path can nudge a structure into a more stable post-buckling path by promoting a post-buckling path using local changes in structural stiffness. The robustness of improved designs can also increase, making structures less susceptible to local fiber misalignments.

Implementing bridge model updating for operation and maintenance purposes: examination based on UK practitioners’ views

There has been a vision of creating bridge digital twins as virtual simulation models of bridge assets to facilitate remote management. Bridge model updating is one digital twin technology which can enable the continuous updating of the structural model as new monitoring data is collected. This paper examines why there is currently little industry uptake of monitoring, modelling and model updating for the operation and maintenance of bridges despite over two decades of research in these fields. The study analyses the findings from a series of semi-structured industry interviews with expert bridge professionals in the U.K. and from an extensive literature survey of bridge model updating studies to examine the disconnects between research and practice and the practical issues of implementing bridge model updating. In particular, the study found that localised damage resulting in local reduction in structural stiffness, a key assumption made in the majority of research, is subject to question by practitioners as many common types of bridge damage may not induce noticeable change in structural stiffness that existing model updating techniques would identify. Key recommendations for future research are proposed to drive adoption of bridge monitoring, modelling and model updating and thus realise their industrial value.

Shaking Table Test of Seismic Response of Immersed Tunnels under the Influence of Multiple Factors

To explore the dynamic characteristics and influencing factors of immersed tunnels under the action of earthquakes, 5 groups of shaking table model tests were carried out. Three different site conditions (unsaturated sand site, homogeneous saturated sand site, and nonuniform site), structural stiffness, and seismic wave input direction were considered. By comparing the above influencing factors, the seismic response law affecting the immersed tunnels was obtained. The test results show that, under the action of horizontal earthquakes, the liquefaction of sand and the larger tunnel stiffness may influence the acceleration development of the soil layers; seismic wave input directions affect the excellent frequency and frequency range of the soil layers, and the liquefaction of sand and large structural stiffness change the shape of the Fourier spectrum curve of the soil layers; site conditions, structural stiffness, and seismic wave input direction have a significant effect on the internal forces of tunnels. Normally, the strain in the heterogeneous soil layer under the horizontal seismic wave input is the largest, and the peak strain of the upper side of the tunnel side wall and center column is larger than the lower part, while the mechanism of structural damage caused by vertical earthquakes is different.

Novel sparseness-inducing dual Kalman filter and its application to tracking time-varying spatially-sparse structural stiffness changes and inputs

While the theory of Bayesian system identification provides a probabilistic means for reliably and robustly inferring models of a dynamic system and their parameters based on measured dynamic response, exploiting sparseness during online tracking of changing model parameters is not well understood. The focus in this study is to implement the dual Kalman filter for real-time Bayesian sequential state and parameter identification based on noisy sensor signals while incorporating sparse Bayesian learning to impose sparse model parameter changes from their initial reference values. We also want out model to be able to capture the evolution of sparse model parameter changes between two successive time instants. To this end, we present a hierarchical Bayesian model for tracking the joint posterior distribution of the state and model parameter vectors for a monitored dynamical system, where the two afore-mentioned sparseness constraints (sparse changes from reference values and with time) are also effectively incorporated for each time. We show our stochastic model of the structural dynamical system can be represented as a coupled conditionally-linear Gaussian state space model for the state and model parameter vectors, leading to some interesting analytical properties of the method that allow quantities of interest to be calculated in real time by using Kalman filtering equations. The parameters for the measurement and state prediction errors are learned solely from the available data up to the current time and so our method resolves the well-known instability problem in Kalman filtering due to arbitrary assignment of the error-distribution parameters. Finally, two illustrative applications are presented, one for identification of stiffness degradation and the other for input time–history identification where both are based on noisy dynamic response measurements from a structure.