Prediction Of Structural Response Research Articles

Non‐linear time history analyses of a rigid block isolated with unbonded fiber‐reinforced elastomeric isolators (UFREIs): A comparison between 3D finite element and phenomenological models

AbstractNumerical modeling represents a pivotal tool in the seismic analysis and design of structural systems, enabling the detailed prediction and examination of structural responses under seismic loading. This research conducts a comparative analysis of two numerical modeling approaches aimed at simulating the seismic response of unbonded fiber‐reinforced elastomeric isolators (UFREIs). The research focuses on a finite element (FE) model developed using Abaqus and a developed phenomenological model implemented in OpenSees, outlining the development and calibration processes for each. The FE model is developed based on simple rubber material testing data, while the phenomenological model is calibrated using experimental results from cyclic shear tests conducted on the UFREI device and the FE model. The primary objective of this study is to assess the effectiveness of these modeling approaches in predicting UFREI behavior under seismic conditions. This evaluation entails comparing model predictions with experimental data obtained from unidirectional shake table tests performed on a rigid block isolated by two UFREIs. This paper highlights the distinct advantages and limitations of each model in simulating UFREI dynamic responses during seismic events. Furthermore, it provides insights into the modeling techniques and discusses the computational demands and data requirements of each model, thereby aiding in their application to various aspects of seismic analysis and design.

An adaptive physics-informed deep learning approach for structural nonlinear response prediction

An adaptive physics-informed deep learning approach for structural nonlinear response prediction

Attention-based hybrid network for structural nonlinear response prediction under long-period earthquake

To develop structures that can respond to severe seismic events, models and tools are essential for quickly and precisely assessing and controlling the structure's performance. Long-period ground motions may pose potentially significant hazards to the safety of high-rise buildings located in basins or alluvial plains. However, traditional regression-based methods and pure data-driven machine learning methods are insufficient to address this problem of response prediction, which is characterized by non-stationarity, nonlinearity, and scarcity of samples. To tackle this issue, we propose a novel semi-explicit physics-informed deep learning method. The method incorporates the physical information into the model, thereby achieving better prediction accuracy. Through numerical and experimental validations, including hysteresis experimental data, the actual seismic response record of a six-story hotel, as well as data from single-degree-of-freedom structures across various periods and multi-story buildings in Los Angeles, Seattle, and Boston, we verified the proposed method significantly outperforms traditional regression-based methods and data-driven machine learning methods in terms of accuracy, efficiency, and robustness.

State space model-based Runge–Kutta gated recurrent unit networks for structural response prediction

State space model-based Runge–Kutta gated recurrent unit networks for structural response prediction

E-PINN: A fast physics-informed neural network based on explicit time-domain method for dynamic response prediction of nonlinear structures

The physics-informed neural networks (PINNs), serving as the surrogate models, have emerged in the dynamic response prediction of nonlinear systems. However, in the conventional PINN model, the global differential equation of motion of the nonlinear system is directly integrated into the loss function, which requires the network to capture the intricate global dynamic evolution mechanism of the system and thus poses significant challenges for network learning. In this study, inspired by the explicit time-domain method (ETDM), a novel PINN based on ETDM, called E-PINN, is proposed to address the challenges arising from the network learning of the existing PINNs. A lightweight long short-term memory (LSTM) module is trained to learn the nonlinear evolution mechanism of restoring forces, while a single convolutional layer (SCL) module is utilized to reflect the linear evolution mechanism of the primary structure under the combined action of the external excitations and the nonlinear restoring forces. The weights of the SCL module can be directly retrieved from the discrete convolutional formulation of ETDM, and only the parameters in the LSTM module need to be optimized through a loss function solely based on the nonlinear restoring forces, thereby enabling easy network learning of the proposed E-PINN by decoupling the linear and nonlinear evolution mechanisms embedded in the nonlinear system. Three numerical examples are investigated by the present approach, and the results demonstrate the superior training efficiency and prediction accuracy of E-PINN in dynamic response prediction of nonlinear systems.

Application of a novel machine learning model for the prediction and optimization of anti-blast performance of sandwich honeycomb blast walls

Sandwich honeycomb blast wall with the superior energy absorption characteristics could be applied to anti-blast protection of offshore oil and gas platform. Recently, machine learning technique, as an alternative to traditional Finite Element Analysis (FEA), is becoming a powerful tool in understanding structural performance. The aim of this study is to propose a novel data-driven model for fast predictions of structure response of sandwich blast wall under explosion, including the maximum deformation δmax, the maximum internal energy Emax and the energy absorption ratio RE. FEA was firstly conducted to generate sample data for the proposed model by using LS-DYNA software. An improved artificial electric field algorithm (IAEFA) was combined with artificial neural network (ANN) to develop an IAEFA-ANN model. In case studies, the prediction results of IAEFA-ANN approximate the results of FEA well, and comparison results of 9 different ANN models demonstrate that IAEFA-ANN model has the best performance. Then, the effects of explosion parameters (including explosive charge mass Me, stand-off distance d and concave angles θ) on structural response prediction results were investigated by using the proposed model. Moreover, the IAEFA-ANN model was applied to conducting optimal design of the blast wall to obtain optimum anti-blast performance.

An efficient method based on shear models for structural seismic response prediction considering hysteretic characteristics

An efficient method based on shear models for structural seismic response prediction considering hysteretic characteristics

A nonlinear structural pulse-like seismic response prediction method based on pulse-like identification and decomposition learning

Abstract Accurate prediction of structural responses under seismic action is important for the performance evaluation of structures. However, depending on the differences in the characteristics of ground motions, particularly whether they contain impulses, the seismic-response characteristics of structures may differ significantly. Hence, a new method, i.e., WD–AttDL, is proposed in this study to predict structural response under pulse-like ground motions. This method innovatively combines a wavelet decomposition-based velocity pulse-identification method with decomposition learning, where decomposed pulses and high-frequency features are used as inputs to the neural-network model, thus simplifying the identification of pulse features for the model. The decomposition learning module integrates different types of submodules, such as CNN feature-extraction, LSTM temporal-learning, and self-attention mechanism submodules. To reduce the nonlinear time-range analysis calculations required to generate training data, a screening method for impulsive ground motions based on time-series feature clustering is proposed. The accuracy and applicability of the proposed method are verified by numerically simulating three different cycles of reinforced-concrete frame structures and a three-dimensional masonry structure. Compared with existing structural seismic-response models, WD–AttDL synergistically integrates different modules and thus offers a higher prediction accuracy. In particular, it reduces the peak error of the predicted response, which is important for the evaluation of structural performance. Additionally, based on the response predicted by WD–AttDL, a vulnerability analysis of the structure under pulse-like seismic effects can be performed rapidly and accurately.

D’Alembert–Lagrange Principle in Symmetry of Advanced Dynamics of Systems

The D’Alembert–Lagrange principle is a fundamental concept in analytical mechanics that simplifies the analysis of multi-degree-of-freedom mechanical systems, facilitates the dynamic response prediction of structures under various loads, and enhances the control algorithms in robotics. It is essential for solving complex problems in engineering and robotics. This theoretical study aims to highlight the advantages of using acceleration energy to obtain the differential equations of motion and the generalized driving forces, compared to the classical approach based on the Lagrange equations of the second kind. It was considered a mechanical structure with two degrees of freedom (DOF), namely, a planar robot consisting of two homogeneous rods connected by rotational joints. Both the classical Lagrange approach and the acceleration energy model were applied. It was noticed that while both approaches yielded the same results, using acceleration energy requires only a single differentiation operation, whereas the classical approach involves three such operations to achieve the same results. Thus, applying the acceleration energy method involves fewer mathematical steps and simplifies the calculations. This demonstrates the efficiency and effectiveness of using acceleration energy in dynamic system analysis. By incorporating acceleration energy into the model, enhanced robustness and accuracy in predicting system behavior are achieved.

Influence of interfacial bond between rebar and concrete on the lateral resistance of RC columns under different loading rates

The interfacial bond between rebar and concrete is a critical factor that influences the performance of reinforced concrete (RC) structures because it affects the effective force transfer between concrete and rebar and hence the composite actions. In finite element modelling of RC structures, the bond-slip relation between rebar and concrete is generally neglected due to the increased model complexity by considering debonding behaviours. Limited studies show the debonding performance is loading rate dependent, neglecting it in numerical modelling may lead to inaccurate structural response predictions, but there is no systematic investigation of bonding performance on responses of structures under load of different loading rates. The influences of neglecting debonding in structural response analysis are therefore not properly defined yet. In this study, the finite element model was established and validated by the available impact test data. The lateral resistance and damage modes of the column considering and neglecting bond-slip behaviour between rebar and concrete were examined under various loading rates ranging from 50 kN/s to 1.0 × 106 kN/s. It is found that neglecting bond-slip relation in finite element modelling of RC columns leads to underprediction of the peak force and damage. The assumption of perfect bond has a significant influence on predicting the peak force and damage of column under low loading rates, but its influence reduces with the increase in loading rate, especially when the loading rate exceeds 1.0 × 105 kN/s. The maximum relative difference in peak force between considering and neglecting bond-slip at 50 kN/s is 18.63 %. Parametric studies were conducted to investigate the effects of bonding performance on columns of different width, depth, height, and concrete strength. Based on the numerical results, an analytical formula is developed to quantitatively predict the errors in peak force predictions by neglecting debonding between concrete and rebars of RC columns. The results highlight the importance of considering bond-slip in finite element modelling of RC structures subjected to quasi-static and low-rate dynamic loadings, and demonstrate it is reasonable to neglect debonding in structural response analysis under high-rate dynamic loadings.

Time history seismic response prediction of multiple homogeneous building structures using only one deep learning‐based Structure Temporal Fusion Network

AbstractStructural response prediction under earthquakes is crucial for evaluating the structural performance and subsequent functional restoration. Deep learning provides the potential to rapidly obtain the responses by skipping the time‐consuming nonlinear finite element analysis. However, a single deep learning network may only predict the time history responses of one specific structure, resulting in redundancy and resource waste when building multiple networks for modeling different structures. Thus, this study proposes a Structure Temporal Fusion Network (STFN) that can predict responses of various homogeneous structures using a single network. The key concept is that the seismic waves and the structural characteristics, such as story numbers, are fused together to predict diverse time history responses. Two numeric experiments are conducted, including predicting responses of ideal single‐degree‐of‐freedom (SDOF) structures and regular multistory reinforced concrete frames. Furthermore, a series of ablation analyses are carried out to validate the network architecture. The results indicate that STFN can predict nonlinear time history responses of different structures with mean square errors in the magnitude of and for two experiments, respectively. The solutions also highlight the importance of fusing static characteristics for the modeling of various structures with only one network. The STFN presents a promising solution for time history response prediction across multiple structures in regions.

Machine-Learning Applications in Structural Response Prediction: A Review

Machine-Learning Applications in Structural Response Prediction: A Review

Structural nonlinear seismic time-history response prediction of urban-scale reinforced concrete frames based on deep learning

Efficiently predicting the seismic response of urban building clusters is essential for preemptively identifying potential seismic hazards prior to an earthquake and optimizing resource allocation post-event. However, complete information of buildings at a city scale is generally un-accessible or non-existent. Existing methods struggle to reconcile low information demands, high computational accuracy, and computational efficiency. This paper proposes a fast prediction method for structural seismic time-history responses that combines deep learning methods with easy-getting structural parameters at an urban scale. An end-to-end network with adaptive multilevel fusion output is designed, which incorporates the autoencoder concept for predicting the structural seismic time-history responses based on ground motions records and five easy-getting structural parameters. The models are compared and optimized considering the training hyperparameters and network architecture, resulting in an optimized model with low complexity that provides valuable reference values for structural seismic response. Besides, the proposed model is applied to four actual buildings with different construction time, occupancy types, and floor sizes, demonstrating its good prediction performance and significant computational advantages comparing to the universally used MDOF method.

Using In-Building Observations of Small-to-Large Earthquakes to Predict the Seismic Response of Structures

ABSTRACT The main goal of this study is to evaluate the potential value of data from weak-to-moderate earthquakes for structural response analysis. Data recorded over 18 yr by the seismic network installed in the 12-stories Grenoble City Hall Building (France) is considered. The building response is analyzed in terms of intensity measures and engineering demand parameters, and then compared with strong earthquake data recorded in Japanese buildings. The uncertainties of structural response prediction are estimated and defined in terms of “within-building” and “between-building” components in the same way as the components of the ground-motion model. Data complementarity in the response model is observed between the weak-to-moderate (France) and the moderate-to-strong (Japan) earthquake datasets, disclosing nonlinear processes (associated with resonance period elongation) that are activated in buildings during low-to-strong motion. For example, fundamental frequency shifts are triggered at low values of both total structural drift amplitudes and equivalent strain rates (i.e., time derivative of structural drift). In addition, strain rate thresholds from 10−11 s−1 to 10−5 s−1 representing different structural conditions from undamaged to severely damaged buildings are observed to activate nonlinearities. This confirms the link between loading rates and structural conditions. Our results highlight the interest in instrumentation in buildings located in regions of weak-to-moderate seismicity, for (1) the development and calibration of realistic models for predicting the seismic response of structures, (2) for improving our understanding of the components of uncertainties in the risk assessment of existing buildings, and (3) to investigate physical processes activated in structures during seismic loading that influence their response.

Amplitude‐dependent modal viscous damping for distributed stick–slip systems

AbstractAn accurate damping model serves as the foundation for reliable structural analysis. One of the primary sources of structural damping is the stick–slip mechanism caused by interfacial friction. Structural damping is usually represented by the modal damping ratio, and the modal equivalent viscous damping ratio (EVDR) of a stick–slip system is crucial in structural dynamic modeling. To date, an accurate yet simple prediction method for modal amplitude‐dependent EVDR that considers the distribution characteristics of the stick–slip mechanism remains unavailable. In the present study, the modal EVDRs of a distributed stick–slip system (DSSS) extended from a single degree‐of‐freedom stick–slip system were calculated using the modal energy equivalence method. The correlation between the stick–slip parameters and the modal damping behavior was investigated at various amplitude levels on the basis of the assumption of equal stick–slip parameters. A two‐parameter damping model represented by a single stick–slip damping model was developed to capture the amplitude dependence of the frictional EVDR. Furthermore, a practical prediction method based on the stick–slip parameters was proposed for the modal EVDR of the fundamental and higher‐order modes. The seismic analysis of two illustrative structures with nonuniform and uniform interstory stiffness demonstrated the effectiveness and applicability of the proposed method in predicting the modal EVDR of the DSSS. The proposed equivalent linear viscous damping system is suitable for the quick prediction of structural dynamic responses.

Adaptive GN block-based model for seismic response prediction of train-bridge coupled systems

Deep learning (DL) methods has found extensive application in predicting structural responses. However, conventional neural networks often lack the capacity to effectively capture inter-data relationships, relying solely on training data for end-to-end prediction. Consequently, these methods may struggle with generalization, particularly when faced with new structural configurations absent from the training dataset. To overcome this limitation, this study adopts a graph-based framework, representing structures as graphs facilitates the transfer of features between different components, mimicking the transfer of forces in the real world. Subsequently, a Graph Neural Network Blocks (GN Blocks) model is introduced. Leveraging the holistic inference structure inherent in graph representations, the GN Block-based model as an adaptive model exhibits superior generalization performance in response analysis tasks involving unknown structural forms. Illustrated through the application to train-bridge coupled (TBC) systems, the GN Block-based model demonstrates high prediction accuracy and robust generalization performance, underscoring its suitability for tackling complex structural response prediction.

Seismic fragility analysis of RC frame structures based on IDA analysis and machine learning

This study introduces a machine learning-based methodology designed to rapidly predict the seismic responses of reinforced concrete (RC) frame structures. The research focuses on three types of RC frame structures: low-rise, multi-story, and small high-rise buildings. Ground shaking records are selected according to the conditional mean spectrum (CMS). A sample database, constructed via Incremental Dynamic Analysis (IDA), facilitates the prediction of structural responses using ground shaking intensity and structural details as inputs. Concurrently, the study performs a feature importance analysis of the model. Machine learning algorithms, including integrated learning and neural networks, are utilized to predict the seismic responses of the RC frame structures. This methodology also assists in evaluating the seismic fragility of these structures. The results show that the discrepancy between the neural network-based seismic fragility assessments and the IDA results is minimal, indicating a high degree of accuracy in the proposed methodology. Among the characteristic parameters, Average Spectral Acceleration (AvgSa) is identified as the most significant. This methodology serves as a valuable tool for the rapid prediction of seismic responses in RC frame structures, demonstrating substantial practical value.

Time history response prediction of stochastic SDOF structures through Kriging-NARX modeling and adaptive real-time hybrid simulation

Emulating time history response is a critical benchmark for evaluating the seismic resilience of structures and assessing their reliability in the face of uncertainties. Traditional computational simulations, while commonly favored, often fall short in accurately describing the behavior of intricate structural components. Real-time hybrid simulation (RTHS) offers an effective and efficient experimental technique for replicating the responses of deterministic systems in a cyber-physical manner but encounters challenges when dealing with stochastic systems. This study proposes an innovative application of RTHS for time history response prediction of nonlinear stochastic structures. A cross-validation (CV)-Voronoi sampling strategy is applied to sequentially select uncertain parameter samples for RTHS. Nonlinear autoregressive with exogenous input (NARX) model is identified and updated from RTHS results in a sequential manner. Coefficients of final NARX model are surrogated using the Kriging meta-models to account for given uncertainties. Laboratory tests on single-degree-of-freedom (SDOF) structures with self-centering viscous dampers (SC-VD) were conducted to demonstrate the effectiveness of the proposed approach. Predicted time history responses from the final Kriging-NARX model are further compared and validated. It is demonstrated that the proposed approach provides an effective and efficient method for time history response prediction of stochastic systems especially when accurate numerical models are not available.

Finite element model updating and response prediction of a frame structure based on optimal sensor placement

This paper proposes an approach for finite element (FE) model updating and response prediction of frame structures based on optimal sensor placement (OSP), which integrates sensor placement optimization, mode expansion, and model updating techniques. Firstly, sensor optimization layout and modal testing analysis are conducted on a bolted laboratory frame structure. The covariance-driven stochastic subspace identification (SSI-COV) method identifies the real-world structure’s natural frequencies, modal damping, and mode shapes. Secondly, the complete mode shapes are expanded using the measured incomplete modal data from the limited number of sensors. Thirdly, a multi-objective function based on frequency and mode shapes is established to adjust the parameters of the FE model. This ensures that the updated model accurately represents the dynamic properties of the actual structure within a specific frequency range. Finally, the Rayleigh damping of the frame structure is estimated, and the damping matrix is assembled to enhance the accuracy of dynamic response prediction in the updated model. By comparing the response prediction results of the updated FE model with and without considering the updated damping effects to the measurement data of the real-world structure, it is demonstrated that the proposed method considering updated damping effects can more effectively predict the structural response.

Deep learning-based prediction of structural responses of RC slabs subjected to blast loading

Considering the risk of explosion, studying the dynamic structural behavior of reinforced concrete (RC) slabs subjected to blast loads is crucial for enhancing their resistance. Conventional approaches, including costly experimental tests, highly hypothetical analytical methods, and time-consuming numerical simulations, have their limitations. This study presents two deep learning-based models for rapid and accurate prediction of explosion-induced responses in RC slabs. Available literature data and supplemented numerical simulation data are used for training and testing. First, a multi-layer perceptron (MLP) model is established to predict the maximum displacement of RC slabs subjected to explosions. The input features include 10 parameters. The training results show that the MLP model exhibits good prediction performance. Second, a one-dimensional convolutional neural network (1D-CNN) model is constructed to predict the failure modes of RC slabs under explosive loads. The input features of the model are consistent with those of the MLP model. The 1D-CNN model outperforms the other five conventional machine learning models in classification. Furthermore, a permutation feature importance analysis is conducted to determine the effects of the 10 input features on the predicted outcomes. This analysis makes the predictions interpretable, thereby bolstering the credibility of the models. Results show that the proposed deep learning models offer an efficient and reliable method to predict the structural response of RC slabs under explosion loads.