Estimation Of Structural Response Research Articles

Development of the compound intensity measure and seismic performance assessment for aqueduct structures considering fluid-structure interaction

The primary motivation of this investigation is to develop the compound ground motion intensity measure (IM) for the seismic performance assessment of aqueduct structures. To achieve this goal, taking the typical aqueduct structure in Southwestern China as a case study, a three-dimensional finite element model of aqueduct-water-foundation fully coupling system is established. Moreover, for the seismic hazard, a total of 300 as-recorded ground motions based on the design response spectrum are selected from PEER-NGA strong ground motion database, including near-field non-pulse (NFNP), near-field pulse-type (NFP) and far-field (FF). Then, the nonlinear correlation between single IMs is considered by Copula function, the compound IM is proposed that can comprehensively characterize the seismic performance of the aqueduct structure. Based on evaluation criteria, the applicability of the proposed compound IM is compared and verified. Finally, the conditional probability function and the unconditional probabilistic seismic risk analysis is employed to evaluate the seismic performance of aqueduct structures. It can be concluded that the compound IM considers the acceleration, velocity and displacement factors, which improves the correlation, efficiency and proficiency of probabilistic seismic demand analysis. Furthermore, the compound IM can help to improve the accuracy of the structural seismic performance assessment and reduce the uncertainty of structural response estimation. In summary, the findings of this study highlight the significance of proposing the compound IM and developing fragility curves when evaluating the seismic performance of aqueduct structures.

Estimation of structural dynamic response induced by individual sit-to-stand loading using acceleration response spectrum approach

Crowd sit-to-stand (STS) loading are always featured with pulse-like action, high crowd density and high synchronization rate among individuals, which may cause severe vibration comfort or even safety issues of supporting structures. However, related work in structural dynamics remains limited. As an early attempt, this study focuses on the individual STS loading and tries to develop an efficient tool to evaluate the potential dynamic response of the supporting structure when subjected to an individual STS action. Firstly, a comprehensive test campaign involving twenty participants was conducted by simultaneously footfall force measurement and 3D motion tracking of the human body. Secondly, the mathematical model of the individual STS loading was developed. Then a response spectrum framework that can give quick estimation of structural dynamic response induced by individual STS loading was proposed. The design response spectrum was formulated based on the measured force data. The effect of seat height, with or without armrests and the types of vibration assessment index on response spectrum was further discussed. The effectiveness of the method was finally verified on four full-scale structures having fundamental frequency from 2 Hz to 10 Hz subjected to individual STS actions.

Digital twin technology for wind turbine towers based on joint load–response estimation: A laboratory experimental study

An accurate estimation of dynamic loads and structural dynamic responses is deemed an indispensable prerequisite for developing trustworthy digital twin (DT) models of dynamically excited structures. Traditional joint load–response estimation (JLRE) algorithms necessitate a full-rank feedthrough matrix, implying that accelerometers are required at all degrees of freedom with input loads. If an unknown input is a bending moment or torque, the rotational acceleration must be measured, which is usually difficult, if not impractical, in real applications. Therefore, the applications of the traditional JLRE in wind turbines (WTs) are rather limited due to the complex excitation mechanism. However, the unified linear input and state estimator (ULISE) algorithm presented in this paper eliminates this constraint. This paper experimentally tested this algorithm on an operating 1:50 scaled WT model. The influences from blade rotation were considered. A comprehensive structural health monitoring (SHM) sensing system was deployed on the WT tower to measure the tower's dynamic responses. A camera system, which integrated digital image correlation (DIC) technology and binocular stereo vision technology, was also adopted. The unknown excitations and unmonitored dynamic responses of the WT tower were estimated simultaneously using the ULISE algorithm. The reconstructed strain and acceleration responses achieved high accuracy, with a normalized root mean square error less than 9%. The blade rotation had a notable impact on the WT tower, primarily manifested as a bending moment whose dominant frequency was corresponding to the blade's rotational speed. Such joint estimations could function as the virtual sensing of unknown inputs and responses in the DT model of the WT tower, which will enable DT-based online remote structural condition assessment and preventive maintenance in the future.

Ideal-Pulse-Based Strong-Motion Duration for Multi-Pulse Near-Fault Records

ABSTRACT Serious damage to near-fault structures is caused mainly by velocity pulses in a ground motion, hence, the necessity to develop a pulse-related definition of strong-motion duration to adequately describe the intensity of near-fault ground motion. For a multipulse record, an ideal-pulse-based strong-motion duration, which is defined as the time interval between the beginning of the first pulse and the end of the last pulse in an ideal-pulse signal, is proposed. This strong-motion duration corresponds with the time interval of the occurrence of strong vibrations and energy accumulations in the original record. For near-fault records, pulselike characteristics can be completely presented within this duration. Meanwhile, the ideal-pulse-based strong-motion duration shows good performance in the estimation of structural responses. The elastic and inelastic displacement spectra of the original records agree well with those of the records shortened according to the proposed duration. The validity of applying the proposed duration to structural analysis is further verified by the nonlinear time history analyses of 3-, 9-, and 20-story steel moment-resisting frame structures. The proposed strong-motion duration accurately estimates nonlinear structural responses. It is proven that the ideal-pulse-based strong-motion duration can adequately describe the intensity of near-fault ground motions, both from the perspective of the energy accumulation process in a ground-motion record and that of the structural analyses subjected to these ground motions.

ASCE/SEI 7‐compliant site‐specific evaluation of the seismic demand posed to reinforced concrete buildings with real and simulated ground motions

AbstractState‐of‐the‐art seismic design and assessment methodologies rely on the utilization of ground‐motion records scaled to site‐specific risk‐targeted spectra to perform nonlinear time‐history analyses and estimate mean structural demands. Motivated by the discrepancies in structural response estimates resulting from different selection and scaling methods, this study assesses the implications of utilizing site‐specific simulated ground motions from 3‐D physics‐based wave‐propagation models as opposed to historical records from worldwide catalogs in ASCE/SEI7‐compliant procedures for seismic performance evaluations. A suite of validated realizations of an M7 Hayward Fault earthquake in the San Francisco Bay Area and two modern reinforced concrete moment‐resisting frame buildings are utilized as a case study. The 2014 USGS earthquake hazard and probability maps are employed for the hazard calculations, and the PEER NGA‐West2 database is used for the selection of the real ground motions. The building models are coupled with the simulated and real ground motions to perform a total of 30,552 nonlinear time‐history analyses. Structural demands obtained from real and simulated motions are examined and compared at the regional‐scale in terms of peak interstory drift ratio median and dispersion, and localization along the building height. The correlations between observed structural responses and ground‐motion features are discussed to potentially inform current code‐compliant methodologies for ground‐motion selection. Results show that utilizing site‐specific simulated ground motions that incorporate path, fault geometry, and site‐condition effects as opposed to historical ground‐motion records may lead to differences in the structural demands above 50%. Such differences are highly spatially variable throughout the region and difficult to predict.

Exploring the impact of excitation and structural response/performance modeling fidelity in the design of seismic protective devices

The design of seismic protective devices (SPDs), such as fluid viscous dampers (VDs), and inertial vibration absorbers (IVAs), requires the adoption of appropriate models for: (i) the earthquake excitation description (e.g. stochastic stationary/non-stationary versus recorded ground motions); (ii) the seismic structural response estimation (e.g. linear versus nonlinear/hysteretic); and (iii) the seismic performance quantification (e.g. average response versus risk-based performance description). This paper pursues a novel, detailed investigation of the impact of modeling fidelity in the design process of SPDs, by examining different combinations of models with different levels of sophistication for each of the aforementioned aspects. In this manner, a large model hierarchy is established, resulting in multiple SPD design variants. A bi-objective optimal design formulation is adopted, considering the structural vibration suppression (building performance) and device control forces as distinct, competing performance objectives (POs). Comprehensive comparisons are reported for a 3-storey and a 9-storey steel benchmark building, equipped with distributed VDs in all floors and with different types of single-device IVAs including the tuned-inerter-damper (TID), the tuned-mass-damper-inerter (TMDI) and the tuned-mass-damper (TMD). An innovative methodological approach is established to gauge the impact of the model fidelity by examining the deviation of POs achieved by lower fidelity SPD designs versus the Pareto-optimal fronts corresponding to POs consistent with the higher fidelity assumptions. The study overall stresses that that the use of lower fidelity models may provide sub-optimal performance in certain settings, and that comparison across the model hierarchy can be leveraged to obtain key insights of the SPD behavior. Additional key findings pertain to the robustness characteristics of the different type of SPDs to the modeling assumptions utilized for the device design.

An efficient approach for statistical moments estimation of structural response based on a novel adaptive hybrid dimension-reduction method

Statistical moments estimation is one of the main topics for the analysis of a stochastic system, but the balance among the accuracy, efficiency, and versatility for different methods of statistical moments estimation still remains a challenge. In this paper, a novel point estimate method (PEM) based on a new adaptive hybrid dimension-reduction method (AH-DRM) is proposed. Firstly, the adaptive cut-high-dimensional model representation (cut-HDMR) is briefly reviewed, and a novel AH-DRM is developed, where the high-order component functions of the adaptive cut-HDMR are further approximated by multiplicative forms of the low-order component functions. Secondly, a new point estimation method (PEM) based on the AH-DRM is proposed for statistical moments estimation. Finally, several examples are investigated to demonstrate the performance of the proposed PEM. The results show the proposed PEM has fairly high accuracy and good versatility for statistical moments estimation.

A closer look at hazard-consistent ground motion record selection for building-specific risk assessment: Effect of soil characteristics and accelerograms’ scaling

Conditional spectrum (CS) record selection is a standard tool for the selection of ground motion recordings consistent with the hazard at a site of interest. For many important applications the seismic hazard is computed for reference rock conditions and the effects of local site conditions are investigated downstream via soil dynamics. Ideally, when selecting for rock conditions, the recordings should be real, recorded on rock sites, and strong enough to push the structure into its severe nonlinear range. However, rock-recorded ground motions are often scarce and the analyst is usually left with the suboptimal option of augmenting the set with scaled motions and motions recorded at soil stations. We investigated how this practice can affect the structural response estimates by means of nonlinear dynamic analyses of a comprehensive set of single-degree-of-freedom (SDOF) systems. We first performed site and structure-specific CS record selection using ground motion databases of only soil-recorded motions versus databases of only rock recorded ones. In addition, we performed the CS based on extreme cases of using ground motions scaled by either only small or only large factors. Our focus was on the statistical comparison of the common intensity measures (IMs) of the selected records and, more importantly, of the structural response estimates when these extreme opposite selection choices are made. We show no significant statistical differences in the distributions of the IMs and structural responses, while the fragility and response hazard remain almost identical, for all practical purposes. These results imply that, as long as hazard consistency is accurately enforced in the ground motion record selection, using CS, selecting soil-recorded motions or record scaled up to 10, will not bias the response estimates. Finally, repeating the above procedure for response prediction of one multiple degrees of freedom (MDOF) system suggests that our conclusions are applicable also to more complex structural systems.

Study on strategies for reducing training samples for accurate estimation of wind-induced structural response of LSTM networks

Long short-term memory (LSTM) neural network is an efficient method to analyze the nonlinear dynamical response of structures. However, once it is applied to a long-duration response prediction task, the demand on the computational resources will become very severe. In order to address this limitation, various strategies based on sample truncation and down-sampling were proposed in this study to reduce the sample size for predicting wind-induced structural response, i.e., to intercept representative short-duration samples from long-duration samples or down sample the long-duration sample to train the LSTM neural network. These novel strategies can achieve the goal of reducing computational burdens without loss of accuracy compared to the long-duration training samples. Based on a single-degree-of-freedom (SDOF) system, the advantages in terms of the time efficiency of the proposed methods on the training phase of LSTM neural networks have been studied, and the accuracy of the predicted wind-induced structural response was investigated. It showed that the Duhamel integral-based approach (DI-BA) of sample truncation presented the best comprehensive performance in time and accuracy. Furthermore, the DI-BA was applied to a four-degree-of-freedom (4DOF) system with different structural parameters to demonstrate its good performance in more general applications involving multiple degrees of freedom.

Comparative study of sinus earthquake forces and ground motion on structure behavioral response using linear time history analysis method

This study aimed to calculate the design earthquake with a harmonic sine wave approach at a frequency of 1.5 Hz; 2.5 Hz; 3.5 Hz; 4;5 Hz, as well as Loma Prieta, Northridge, and Kobe ground motion. In addition, a structural response review was also carried out based on a comparison of the effects of the ground motion and sine wave earthquake forces. This study used an experimental method of modelling an apartment building with a scale of 1: 50. The case study was located in Mantrijeron, Yogyakarta, which has a seismic category in the medium-size class. The analysis phase began with material definition, element dimension estimation, modelling by analysis software, loading estimation, structural analysis, and comparison of structural responses based on the deviation. The results indicate that the building model could withstand dynamic loads from harmonic waves up to a frequency of 5.5 Hz for one minute of vibration. The most significant deviation is shown at a frequency of 4.5 Hz with an x-axis direction of 0.110 and a y-direction of 0.160. The structural response resulting from ground motion loading shows that the highest deviation occurred due to the influence of the Kobe earthquake, with a deviation of 0.063 in the x-axis direction and 0.054 in the y-axis direction. Based on these results, the effect of harmonic sine waves is greater than the ground motion loading on the response of the building structure, so it is used as an experimental loading through a vibrating table with the actual residual deviation results showing a value of 0.9 mm in the y-axis direction. The difference in structural response results could be caused by the supports and connections modelling in planning through analysis software which could not precisely represent the actual implementation of the building model.

Estimation of unmeasured structural responses of submerged floating tunnels using pattern model trained via long short-term memory

Structural conditions are typically investigated using a significant amount of structural response data. Compared with conventional infrastructures such as long-span bridges, submerged floating tunnels in deep-water offshore fields are restrictive in terms of sensor installation and data acquisition owing to their structural and environmental characteristics. Hence, we propose an effective method for estimating unmeasured structural responses using a structural-pattern model. The pattern model, which is used to estimate the responses, is established by a deep learning algorithm using structural response data obtained via hydrodynamics-based simulation. The proposed method is validated numerically. For the deep learning algorithm, long short-term memory (LSTM) is used to reflect the sequential characteristics of the structural behavior. In this study, we establish the input, hidden, and output layers of the LSTM. Additionally, we specify the tether tension and bending moments of the tunnel as responses that should be estimated and the acceleration as the response that could be measured. The proposed method is evaluated based on structural behaviors under various waves with different wave directions, heights, and periods. The result shows that the proposed method can precisely estimate the tether tension and bending moment of the tunnels using only the tunnel acceleration. In particular, the method can effectively estimate the responses under various directional waves without requiring wave information in advance.

Site dependent response estimation by holistic record selection and bagging algorithm

An efficient two-step method is proposed for site-specific structural response estimation of buildings at multiple sites with different seismicity. In the first step, holistic record sets that cover a range of seismic characteristics are constructed for a few intensity measure levels. Next, using the holistic sets and their associated responses, any required response measure (e.g., structural collapse) is calculated using a bagging algorithm. The proposed method has two advantages to many similar suggested processes in this regard. Because the proposed process works directly with record selection methods and a computational method, it can consider the effect of different ground motion intensity measures and works for all engineering demand parameters. The performance of the proposed method is investigated using two 4 and 8-story steel moment-resisting frames in six cities with different seismicity. The collapse fragility curve, maximum inter-story drift ratio (MIDR), and peak floor acceleration (PFA) are considered for performance assessment. It was observed that when the number of selected records is about twice the common required number of records for response assessment, the proposed method, on average, leads to equal or better efficiency for collapse fragility estimation compared to conventional single site-building analysis. Also, the results indicate an efficiency similar to utilizing about 10–20 ground motion records at each IM level for MIDR and PFA response estimation.

Time-dependent structural response estimation method for concrete structures using time information and convolutional neural networks

In this study, a structural response estimation method for evaluating the long-term strain, which can serve as the basis of assessing structural safety of concrete structures, is presented. Characteristics of short-term deformation caused by environmental influences and long-term inelastic deformation caused by combination of rheological effects and environmental influences are identified. To reflect these time-dependent short- and long-term deformation characteristics in the proposed method, the strain and corresponding time information collected from a structural health monitoring (SHM) are used. In the proposed method, the relationship between a strain response measured from a concrete structure and the corresponding time information including the year, month, day, and hour is defined by a convolutional neural network (CNN), which is a deep learning technique. The method assumes that the main sources of the strain in the structure are environmental influences, such as temperature and humidity, which have daily and seasonal periodicity, and rheological effects in concrete, such as creep and shrinkage, but it also assumes that the quantitative information on these sources is not available (e.g., environmental temperature and humidity, and rheological strain were not reliably measured). Thus, the CNN method developed in this study is trained only with strain and time data collected over several years, and used to estimate strain values within a specific timeframe, e.g., when the safety evaluation of the concrete structure is required or in case of SHM system failure or data loss. The presented method was developed and validated using SHM data from a real structure instrumented with fiber optic strain sensors. In addition, exploration of the constitution of the input of the CNN identified the type of time information that is the most effective in the long-term strain estimation of the developed method.

Numerical Analysis of Unidirectional GFRP Composite Mechanical Response Subjected to Tension Load using Finite Element Method

Composite material is a well-known structural material which is increasingly adopted as an engineering structure material. Glass fiber reinforced polymer offers the lightweight and high strength characteristics that is required for the modern industry, such as aviation, automotive, wind power, and marine technology. One of the important mechanical characteristics of the composite materials are the tensile properties, because it is well known as the material strength. Therefore, the investigation of mechanical response on the glass fiber reinforced polymer (GFRP) tensile test using numerical analysis is important for the estimation of structural response of the GFRP complex structure, such as boat construction. The objective of this research is to assess and estimate the mechanical response of the GFRP composite material subjected to tension load using finite element method. The linear transversely isotropic model is developed to estimate the unidirectional glass fiber GFRP with the configuration of fiber orientation angles of 0°, 30°, 45°, 60° and 90°. The results show that FE simulation are capable to detect the specimen response during the tensile test. The maximum discrepancy of the estimated stress strain diagram is about 16.5% to 32% compared to experimental data. The larger orientation angle has shown the larger discrepancy value. It is found that the increment of discrepancy value is generated by the nonlinearity behavior of the material due to the domination of polymer material behavior on the large orientation angle. Otherwise, the FE models have estimated accurately the ultimate strength, maximum displacement and fracture load. It can be concluded that the linear transversely isotropic model is adequately accepted as the estimation method of the GFRP composite structure response.

Effect of time-variant structural modal parameters on accurate estimation of wind-induced dynamic responses of high-rise buildings during typhoons

Structural modal parameters of high-rise buildings during severe winds generally exhibit time-variant features, which conflicts with the assumption adopted in the wind-resistant design procedure that the modal parameters are time-invariant values. This may lead to inaccurate estimation of wind-induced structural dynamic responses, thus deteriorating the reliability of structural design of high-rise buildings. To address this issue, this paper first evaluates the time-variant modal parameters of a 600-m-high skyscraper based on building accelerations measured during Super Typhoon Mangkhut. Then, the identified time-variant modal parameters are combined with the wind loads estimated from wind tunnel test on a 1:500 scaled model of the skyscraper to reproduce the typhoon-induced building accelerations. The results show that the reproduced accelerations based on the time-variant modal parameters agree well with the field-measured ones. Moreover, a comparative study between adoption of the time-variant modal parameters and time-invariant modal parameters is conducted, which shows that the former case provides more accurate estimation of the building dynamic responses during the extreme typhoon event. This paper aims to explore an effective means for precise evaluation of typhoon-induced dynamic responses of high-rise buildings.

A combined finite element and deep learning network for structural dynamic response estimation on concrete gravity dam subjected to blast loads

Social infrastructures such as dams are likely to be exposed to high risk of terrorist and military attacks, leading to increasing attentions on their vulnerability and catastrophic consequences under such events. This paper tries to develop advanced deep learning approaches for structural dynamic response prediction and dam health diagnosis. At first, the improved long short-term memory (LSTM) networks are proposed for data-driven structural dynamic response analysis with the data generated by a single degree of freedom (SDOF) and the finite numerical simulation, due to the unavailability of abundant practical structural response data of concrete gravity dam under blast events. Three kinds of LSTM-based models are discussed with the various cases of noise-contaminated signals, and the results prove that LSTM-based models have the potential for quick structural response estimation under blast loads. Furthermore, the damage indicators (i.e., peak vibration velocity and domain frequency) are extracted from the predicted velocity histories, and their relationship with the dam damage status from the numerical simulation is established. This study provides a deep-learning based structural health monitoring (SHM) framework for quick assessment of dam experienced underwater explosions through blast-induced monitoring data.

Joint Estimation of Multi-Scale Structural Responses and Unknown Loadings Based on Modal Kalman Filter Without Using Collocated Acceleration Observations

It has been proved that the usage of multi-type observations including global and local information for structural health monitoring (SHM) outperforms that of solo-type measurement. Kalman filter (KF) is a simple but powerful tool for the online state estimation. However, external force is required for the classic KF. Moreover, direct implementation of KF in time domain may be time consuming especially for large structures with many degrees-of-freedoms (DOFs) involved. From these points of view, the idea of modal Kalman filter (MKF) is introduced. An MKF-based approach is then proposed for joint estimation of multi-scale responses and unknown loadings with data fusion of multi-type observations. By using a projection matrix and the selected several modes, a modified observation equation in modal domain is derived. Limited multi-type measurements are fused together for online estimating multi-scale responses of structure at its critical locations. The unknown excitation is simultaneously identified by least squares estimation (LSE). The drift problem of the real time estimation of structural responses and unknown loads is avoided. The effectiveness of the proposed approach is numerically demonstrated via several examples. Results show that the proposed approach is capable of satisfactorily estimating the unmeasured multi-scale responses and identifying the unknown loadings.

Structural Response Estimation Based on Kalman Filtering with Known Frequency Component of External Excitation and Multitype Measurements for Beam-Type Structure

AbstractStructural health monitoring can be performed through long-term monitoring of key structural parts to obtain the structural response information of civil engineering structures in real serv...

A framework for fast estimation of structural seismic responses using ensemble machine learning model

While recognized as most rigorous procedure leading to 'exact' structural seismic responses, nonlinear time history analysis is usually time consuming and computational demanding, especially when numerous structures remain to be analyzed. This paper proposes a framework to improve the time efficiency in evaluating the structural seismic demands, using ensemble machine learning models based on 'classification-regression' philosophy. Typical tall pier bridges widely located in southwest China are employed as illustrative examples to validate the efficiency and performance of this proposed framework. The results and discussion show that with properly selected input variables, the proposed ensemble model (ORF-ANN herein) performs better in predicting seismic demands than other single learning algorithms (i.e., ANN and ORF), while the time efficiency is improved over 90%. This proposed model could drastically improve the efficiency for determining structural parameters in preliminary design process, and thus reduce the iterations of trail analysis. Additionally, the model constructed from proposed framework is believed especially favored for evaluating the post-earthquake states/resilience of a region and/or highway network, where thousands of structures might be contained, and conducting nonlinear time history analysis for each one would be prohibitively time consuming and delay the rescue operations.

Structural Response Estimation Using Gated Recurrent Unit

Recent tragedies have demonstrated that natural disasters, such as earthquakes and typhoons, can wreak havoc on society. Numerical models and simulations are used for predicting the structural response and damage caused by disasters. However, some structures do not have any design drawings or numerical models, and thus, problems are encountered when conducting numerical simulations. Furthermore, even if the model exists, the response predicted through numerical simulation may be different from the response of the actual structure. Although effort has been made to resolve this issue using model-updating techniques, these methods are laborious for developing a new model that reflects the current state of the structure. Therefore, the aim of this study is to develop a new method that automatically predicts the time-series response of structures using a deep learning technique. The gated recurrent unit), based on the recurrent neural network, was used to predict the structural response. Simulation-based validation tests were conducted to verify the performance of the proposed method. The proposed method could estimate the response of the structure with a root-mean-square error of 13.59%.