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Real-time Hybrid Simulation (RTHS) is a technique wherein a structural system is divided into an analytical and an experimental substructure. The former is modeled numerically while the latter is physically present in the laboratory. The two substructures are kinematically linked together at their interface degrees of freedom (DOFs) and the equations of motion are solved in real-time to determine the structure’s response. One of the main challenges of RTHS is to include the effects of soil–foundation–structure interaction (SFSI), which can have a substantial effect on the overall response. The soil domain cannot be modeled experimentally due to the large payload size. On the other hand, modeling the soil domain numerically, using a continuum-based approach, in real-time is challenging due to the associated computational cost. To address these issues, this paper presents a framework for seismic RTHS of SFSI systems using a Neural Network (NN)-based macroelement model of the soil–foundation system. A coupled SFSI model is used to train the NN model and the loss function is based on dynamic equilibrium at the interface between the foundation and the structure. The framework is demonstrated using a three-story building with the lateral load resisting system comprised of moment resisting and damped brace frames. The proposed framework ensures a stable and accurate RTHS, accounting for SFSI by incorporating: (a) spring elements at the output DOFs of the NN model to remove rigid body modes; (b) dashpot elements at the output DOFs of the NN model to mitigate spurious higher frequencies of vibration; and (c) regularization in the NN model’s architecture with data augmentation to reduce overfitting. 

Natural hazards, including hurricanes and earthquakes, can escalate into catastrophic societal events due to the destruction of the built environment. To minimize the impact of such hazards on vulnerable communities, civil infrastructure must be designed with performance criteria that prioritize public safety and ensure continuous operation. The National Science Foundation funded Natural Hazards Engineering Research Infrastructure (NHERI) program focuses on advancing the development of resilient infrastructure. The NHERI Lehigh Real-time Multi-directional Simulation Experimental Facility (EF) is one of the facilities within this program. The facility serves as an open-access research hub, offering advanced technologies and engineering tools to develop innovative solutions for natural hazard mitigation. It is uniquely equipped to perform large-scale, multi-directional structural testing in real-time using a cyber-physical simulation technique known as real-time hybrid simulation. This technique enables researchers to model entire systems subjected to dynamic loads at a full scale, allowing for realistic assessments of infrastructure responses to specific hazard scenarios and the development of effective mitigation strategies. This paper explores how cyber-physical simulation has revolutionized research in natural hazards engineering and its influence on engineering practices. It highlights several ongoing projects at the NHERI Lehigh EF aimed at enhancing community resilience in hazard-prone regions. The paper also discusses the planned expansion of the EF, which aims to broaden its focus to include a wider range of natural hazards, and infrastructure systems. This expansion will incorporate both physical and computational resources to enhance the understanding of fluid interactions in combined natural hazards and climate change impacts on coastal and offshore infrastructure. The NHERI Lehigh EF represents a transformative facility that is reshaping natural hazards research and will continue to play a pivotal role in the development of risk management strategies for more resilient communities. 

Abstract Real‐time hybrid simulation (RTHS) involves dividing a structural system into numerical and experimental substructures. The experimental substructure is challenging to model analytically and is therefore modeled physically in the laboratory. Analytical substructures are conventionally modeled using the finite element method. The two substructures are kinematically linked, and the governing equations of motion are solved in real‐time. Thus, the state determination of the analytical substructure needs to occur within the timestep, which is of the order of a few milliseconds. All structural systems are supported by a soil‐foundation system and any evaluation of the efficacy of response modification devices placed in the structure should consider soil‐foundation structure interaction (SFSI) effects. SFSI adds compliance to a structural system, thereby altering the natural frequencies. Additionally, nonlinear behavior in the soil can result in residual deformations in the foundation and structure, as well as provide added damping. These effects can occur under both wind and earthquake loading. To overcome the barrier of the large computational effort required to model SFSI effects in real‐time using the conventional finite element approach, a neural network (NN) model is combined with an explicit‐based analytical substructure and experimental substructure with dampers to create a framework for performing RTHS with SFSI effects. The framework includes a block of long‐short term memory (LSTM) layers that is combined with a parallel rectified linear unit (ReLU) to form a NN model of the soil‐foundation system. RTHS of a tall 40‐story steel building equipped with nonlinear viscous dampers and subjected to a windstorm are performed to illustrate the framework. It was found that a number of factors have an effect on the quality of RTHS results. These include: (i) the discretization of the wind loading into bins of basic wind speed; (ii) the extent of the NN model training as determined by the root mean square error (RMSE); (iii) noise in the restoring forces produced by the NN model and its interaction with the integration algorithm; and, (iv) the bounding of outliers of the NN model's output. Guidelines for extending the framework for the RTHS of structures subjected to seismic loading are provided. 

Real-time hybrid simulation (RTHS) divides a structural system into analytical and experimental substructures that are coupled through their common degrees of freedom. This paper introduces a framework to enable RTHS to be performed on 3D nonlinear models of tall buildings with rate dependent nonlinear response modification devices, where the structure is subjected to multi-directional wind and earthquake natural hazards. A 40-story tall building prototype with damped outriggers is selected as a case study. The analytical substructure for the RTHS consists of a 3-D nonlinear model of the structure, where each member in the building is discretely modeled in conjunction with the use of a super element. The experimental substructure for the RTHS consists of a full-scale rate-dependent nonlinear viscous damper that is physically tested in the lab, with the remaining dampers in the outrigger system modeled analytically. The analytically modeled dampers use a stable explicit non-iterative element with an online model updating algorithm, by which the covariance matrix of the damper model’s state variables does not become ill-conditioned. The damper model parameters can thereby be updated in real-time using measured data from the experimental substructure. The explicit MKR-α method is optimized and used in conjunction with the super element to efficiently integrate the condensed equations of motion of a large complex model having more than 1000 nonlinear elements, thus enabling multi-axis earthquake and wind hybrid nonlinear simulations to be performed in real-time. An adaptive servo-hydraulic actuator control scheme is used to enable precise real-time actuator displacements in the experimental substructure to be achieved that match the target displacements during a RTHS. The IT real-time architecture for integrating the components of the framework is described. To assess the framework, 3D RTHS of the 40-story structure were performed involving multi-axis translational and torsional response to multi-directional earthquake and wind natural hazards. The RTHS technique was applied to perform half-power tests to experimentally determine the amount of supplemental damping provided by the damped outrigger system for translational and torsional modes of vibration of the building. The results from the study presented herein demonstrate that RTHS can be applied to large nonlinear large structural systems involving multi-axis response to multi-directional excitation. 

Modeling dry friction is a challenging task. Accurate models must incorporate hysteretic rise of force across displacement and non-linearity from the Stribeck effect. Though sufficiently accurate models have been proposed for simple friction systems where these two effects dominate, certain rotational friction systems introduce self-energizing and accompanying backlash effects. These systems are termed self-energizing systems. In these systems, the friction force is amplified by a mechanical advantage which is charged through motion and released during reversing the direction of travel. This produces energized and backlash regimes within which the friction device follows different dynamic behaviors. This paper examines self-energizing rotational friction, and proposes a combined physics and machine learning approach to produce a unified model for energized and backlash regimes. In this multi-process information fusion methodology, a classical LuGre friction model is augmented to allow state-dependent parameterization provided by a machine learning model. The method for training the model from experimental data is given, and demonstrated with a 20 kN banded rotary friction device used for structural control. Source code replicating the methodology is provided. Results demonstrate that the combined model is capable of reproducing the backlash effect and reduces error compared to the standard LuGre model by a cumulative 32.8%; in terms modeling the tested banded rotary friction device. In these experimental tests, realistic pre-defined displacements inputs are used to validate the damper. The output of the machine learning model is analyzed and found to align with the physical understanding of the banded rotary friction device. 

This dataset contains data from E-Defense shake-table tests of a full-scale, steel moment-resisting frame (MRF) supplemented with spines. Herein, the spines were pin-based columns with sufficient stiffness and strength to distribute plastic deformation evenly over the height of the MRF. The specimen was tested under two configurations: first, with the spine rigidly connected to the MRF; and second, with the spine connected to the MRF through Force-Limiting Connections (FLCs). The two structural systems were subjected to two ground motions adjusted to two different scales. The tests highlighted the expected benefits of spines as well as their drawbacks of inducing large floor acceleration in the MRF and large shear forces in the spines themselves. The tests also highlighted how the FLCs can mitigate such drawbacks of spines. The data may be used, for example, to reproduce the observations presented by the authors, to compare the dynamic response of the specimen with building specimens tested in other shake-table test programs, to validate numerical models against the measured specimen response, or to formulate classroom exercises on system identification of linear and nonlinear systems. 

The central difference is a popular algorithm used to integrate the equations of motion, yet suffers from two drawbacks: (1) it is only conditionally stable and requires a small-time step to maintain numerical stability; (2) it is nondissipative, and high-frequency spurious oscillations may appear and compromise the accuracy of the solution. These drawbacks are detrimental to applying the algorithm to the real-time hybrid simulation of large, complex nonlinear structural systems. In this paper, the conventional central difference algorithm is modified to overcome these drawbacks, and the modified algorithm is applied to the real-time hybrid simulation of complex structural systems. 

This paper presents an experimental study on the multidirectional cyclic lateral-load response of post-tensioned self-centering (SC) cross-laminated timber (CLT) shear walls. SC-CLT shear wall damage states are introduced and qualitatively defined in terms of the repairs needed to restore the lateral-load response of the SC-CLT wall. A comparison between SC-CLT wall damage states under unidirectional (in-plane) and multidirectional (in-plane and out-of-plane) lateral loading is presented. The experimental results show that the initiation of SC-CLT wall damage occurs at smaller story drifts under multidirectional loading compared to unidirectional loading. Engineering demand parameters (EDPs) are used to quantify the SC-CLT wall damage states. Uncertainty in the EDP value when a damage state occurs is considered and quantified. Using the experimental results, component (i.e., a CLT wall panel corner) and system (i.e., an entire SC-CLT wall) fragility functions are developed and presented. 

Mid-rise moment resisting frames (MRF) which utilize supplemental pinned-base spines (spine) to prevent the formation of story mechanisms experience higher mode accelerations at near elastic spectral values. Force Limiting Connections (FLC) can be introduced to reduce the floor accelerations from the higher mode responses while having small impact on first-mode response and maintaining the story mechanism prevention from the spine. Results from nonlinear response history analysis (NRHA) of a 4-story MRF-Spine system show how floor accelerations for higher modes are reduced with the addition of FLC placed between the MRF and spine. Peak effective pseudo accelerations are utilized to show how pseudo spectral accelerations are reduced by the introduction of FLC. Full-scale testing of the 4-storyMRF-Spine structure supports the numerical results of theMRF-Spine andMRF-Spine-FLC numerical analyses. These results show the potential benefits of adding FLC to MRF-Spine systems.