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Real-time hybrid simulation (RTHS) is an enabling technology that has transformed engineering experimentation and helped researchers expand modeling capabilities. However, breakthroughs are necessary to expand the range of hybrid simulation methods and, thus, enable experiments with loading conditions representing multiple hazards. This paper discusses the development of a new thermomechanical RTHS framework and a systematic approach to determining RTHS control requirements. First, the framework is established using a representative finite element model of a layered structural system subjected to thermal loading. A complete two-layer system model serves as the reference system, and it is then partitioned into a numerical layer and an experimental layer that share interface conditions. Next, a thermal actuator is introduced to impose dynamic thermal loading on the experimental subsystem, serving as a transfer system. Finally, control and performance metrics are defined to evaluate the realization of interface boundary conditions and map this to the RTHS execution. Through an illustrative example considering the influence of temperature on a lunar habitat, we demonstrate how to establish controller requirements for RTHS and demonstrate that this approach can be used to conduct RTHS on structures with thermomechanical loading. [ABSTRACT FROM AUTHOR]

Collecting massive amounts of image data is a common way to record the postevent condition of buildings, to be used by engineers and researchers to learn from that event. Key information needed to interpret the image data collected during these reconnaissance missions is the location within the building where each image was taken. However, image localization is difficult in an indoor environment, as GPS is not generally available because of weak or broken signals. To support rapid, seamless data collection during a reconnaissance mission, we develop and validate a fully automated technique to provide robust indoor localization while requiring no prior information about the condition or spatial layout of an indoor environment. The technique is meant for large‐scale data collection across multiple floors within multiple buildings. A systematic method is designed to separate the reconnaissance data into individual buildings and individual floors. Then, for data within each floor, an optimization problem is formulated to automatically overlay the path onto the structural drawings providing robust results, and subsequently, yielding the image locations. The end‐to‐end technique only requires the data collector to wear an additional inexpensive motion camera, thus, it does not add time or effort to the current rapid reconnaissance protocol. As no prior information about the condition or spatial layout of the indoor environment is needed, this technique can be adapted to a large variety of building environments and does not require any type of preparation in the postevent settings. This technique is validated using data collected from several real buildings. [ABSTRACT FROM AUTHOR]

A select number of scientific communities have been quite successful in evolving the culture within their community to encourage publishing and to provide resources for re-using well-documented data. These data have great potential for analysis and knowledge generation beyond the purposes for which they were collected and intended. However, there are still barriers in this process. To explore this problem, we have developed a prototype tool: the Experiment Dashboard (ED), with the objective of demonstrating the ability and potential of enabling automated data ingestion from typical research laboratories. This innovative prototype was developed to create a novel system and artifact to explore the possibilities of allowing researchers in laboratories across the nation to link their data acquisition systems directly to structured data repositories for data and metadata ingestion. The prototype functions with commonly used data acquisition software at the data source and the HUBzero scientific gateway at the data sink. ED can be set up with minimal effort and expertise. In this paper, we describe the motivation and purposes for the prototype, the architecture we devised and functionality of this tool, and provide a demonstration of the tool for optical measurements in a structural engineering laboratory. The goal of this paper is to articulate and show through our prototype a vision for future cyberinfrastructure for empirical disciplines that rely on the rapid collection, analysis, and dissemination of valuable experimental data. We also discuss lessons learned that may be useful for others seeking to solve similar problems. [ABSTRACT FROM AUTHOR]

The development of damage identification methods that enable rapid implementation holds great promise for assessing structural integrity to avoid further damage or catastrophic failure. Here, an on-line model updating approach is proposed to rapidly and simultaneously identify the mass, stiffness, and damping properties of a structural model. The proposed approach facilitates identification of these unknown parameters using two steps: first, energy equilibrium equations are used to establish a relationship between structural energy and unknown parameters; second, the Kalman filter is adopted to obtain the unknown parameters in a short period of time. Numerical verification is conducted on a 158-degree-of-freedom truss model with 324 unknown parameters based on a real-world structure. The results indicate that the proposed approach can simultaneously identify the model parameters for the mass, stiffness, and damping of both the damaged and undamaged structures online, and also can effectively identify the stiffness damage and mass change, even in an environment with noise. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In conventional hybrid simulation (HS) and real time hybrid simulation (RTHS) applications, the information exchanged between the experimental substructure and numerical substructure is typically restricted to the interface boundary conditions (force, displacement, acceleration, etc.). With additional demands being placed on RTHS and recent advances in recursive system identification techniques, an opportunity arises to improve the fidelity by extracting information from the experimental substructure. Online model updating algorithms enable the numerical model of components (herein named the target model), that are similar to the physical specimen to be modified accordingly. This manuscript demonstrates the power of integrating a model updating algorithm into RTHS (RTHSMU) and explores the possible challenges of this approach through a practical simulation. Two Bouc–Wen models with varying levels of complexity are used as target models to validate the concept and evaluate the performance of this approach. The constrained unscented Kalman filter (CUKF) is selected for using in the model updating algorithm. The accuracy of RTHSMU is evaluated through an estimation output error indicator, a model updating output error indicator, and a system identification error indicator. The results illustrate that, under applicable constraints, by integrating model updating into RTHS, the global response accuracy can be improved when the target model is unknown. A discussion on model updating parameter sensitivity to updating accuracy is also presented to provide guidance for potential users. [ABSTRACT FROM AUTHOR]

Mobile bridges have been used for a broad range of applications including military transportation or disaster restoration. Because mobile bridges are rapidly deployed under a wide variety of conditions, often remaining in place for just minutes to hours, and have irregular usage patterns, a detailed record of usage history is important for ensuring structural safety. To facilitate usage data collection in mobile bridges, a new acceleration-based vehicle classification technique is proposed to automatically identify the class of each vehicle. Herein we present a new technique that is based on the premise that each class of vehicles produces distinctive dynamic patterns while crossing this mobile bridge, and those patterns can be extracted from the system's acceleration responses. Measured acceleration signals are converted to time-frequency images to extract two-dimensional patterns. The Viola-Jones object detection algorithm is applied here to extract and classify those patterns. The effectiveness of the technique is investigated and demonstrated using laboratory and full-scale mobile bridges by simulating realistic scenarios. [ABSTRACT FROM AUTHOR]

Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. Also, highly relying on an inspector's subjective or empirical knowledge induces false evaluation. To address these limitations, a vision-based visual inspection technique is proposed by automatically processing and analyzing a large volume of collected images. Images used in this technique are captured without controlling angles and positions of cameras and no need for preliminary calibration. As a pilot study, cracks near bolts on a steel structure are identified from images. Using images from many different angles and prior knowledge of the typical appearance and characteristics of this class of faults, the proposed technique can successfully detect cracks near bolts. [ABSTRACT FROM AUTHOR]

With the overwhelming number of older reinforced concrete buildings that need to be assessed for seismic vulnerability in a city, local governments face the question of how to assess their building inventory. By leveraging engineering drawings that are stored in a digital format, a well-established method for classification reinforced concrete buildings with respect to seismic vulnerability, and machine learning techniques, we have developed a technique to automatically extract quantitative information from the drawings to classify vulnerability. Using this technique, stakeholders will be able to rapidly classify buildings according to their seismic vulnerability and have access to information they need to prioritize a large building inventory. The approach has the potential to have significant impact on our ability to rapidly make decisions related to retrofit and improvements in our communities. In the Los Angeles County alone it is estimated that several thousand buildings of this type exist. The Hassan index is adopted here as the method for automation due to its simple application during the classification of the vulnerable reinforced concrete buildings. This paper will present the technique used for automating information extraction to compute the Hassan index for a large building inventory. [ABSTRACT FROM AUTHOR]

As magnetorheological (MR) control devices increase in scale for use in real-world civil engineering applications, sophisticated modeling and control techniques may be needed to exploit their unique characteristics. Here, a control algorithm that utilizes overdriving and backdriving current control to increase the efficacy of the control device is experimentally verified and evaluated at large scale. Real-time hybrid simulation (RTHS) is conducted to perform the verification experiments using the nees@Lehigh facility. The physical substructure of the RTHS is a 10-m tall planar steel frame equipped with a large-scale MR damper. Through RTHS, the test configuration is used to represent two code-compliant structures, and is evaluated under seismic excitation. The results from numerical simulation and RTHS are compared to verify the RTHS methodology. The global responses of the full system are used to assess the performance of each control algorithm. In each case, the reduction in peak and root mean square (RMS) responses (displacement, drift, acceleration, damper force, etc.) is examined. Beyond the verification tests, the robust performance of the damper controllers is also demonstrated using RTHS. [ABSTRACT FROM AUTHOR]

Abstract: The development of smart structures involves the use of various types of controllable damping devices by integrating them into structural systems for energy dissipation. An optimal feedback control law that can consider the nonlinearity embedded in the systems and the random characteristics of the external inputs is desirable to control such dissipation mechanism. This paper proposes an innovative numerical technique to obtain the optimal feedback control strategy of nonlinear stochastic systems. The systems under investigation are modeled as mechanical oscillators and a damping device. The numerical approach proposed to obtain the optimal control strategy involves solving a nonlinear partial differential equation—the Hamilton–Jacobi–Bellman equation. Because civil engineering structural systems may exhibit nonlinear hysteretic behavior under extreme loading conditions, the application of the obtained control strategy could provide an optimal feedback control law to reduce the system response under random excitations (such as earthquakes, wind load and sea waves). Several numerical examples are presented to verify optimality and demonstrate the efficacy of the proposed optimal control solution. First, a linear oscillator is used to verify that the obtained solution is indeed the optimal solution by comparing it to the closed-form optimal solution. Then the proposed method is applied to several nonlinear systems. In each nonlinear example, control optimality is demonstrated by comparing the system responses and control costs obtained using the proposed optimal controls with those obtained using corresponding linearized optimal controls. [Copyright &y& Elsevier]

SUMMARY Real-time hybrid structural testing is a novel method that is gaining acceptance for examination of civil engineering structures. This method facilitates testing of an unknown physical component coupled with an analytical model of the remainder of the structure. A primary challenge in performing real-time hybrid testing to accurately reproduce the behavior of the complete system is the appropriate compensation of actuator dynamics. Accounting for the magnitude roll-off while also reducing the time lags to a minimal value is critical to ensure both accuracy and stability of a real-time hybrid structural test. Here, a new compensation scheme is proposed that integrates the best features of two commonly used approaches. The proposed method, simply called integrated compensation, uses online delay estimation to improve on the performance of inverse compensation method for problems in which the effective time delay is not constant over the frequency band of interest. Two real-time experiments are conducted to examine the capabilities of the approach for achieving good displacement tracking. Both experiments focus on the use of a device as the physical component. One experiment uses a linear spring as the physical component, and one uses a controllable, nonlinear damping device. The actuator displacement tracking results are compared with the results using other compensation approaches. The results from these experiments demonstrate that the proposed integrated compensation method is very effective and suitable for real-time hybrid testing using a device as the physical component. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Model updating procedures are traditionally performed off-line. With the significant recent advances in embedded systems and the related real-time computing capabilities, online or real-time, model updating can be performed to inform decision making and controller actions. The applications for real-time model updating are mainly in the areas of (i) condition diagnosis and prognosis of engineering systems; and (ii) control systems that benefit from accurate modeling of the system plant. Herein, the development of a cyber-physical real-time model updating experimental platform, including real-time computing environment, model updating algorithm, hardware architecture and testbed, is described. Results from two challenging experimental implementations are presented to illustrate the performance of this cyber-physical platform in achieving the goal of updating nonlinear systems in real-time. The experiments consider typical nonlinear engineering systems that exhibit hysteresis. Among the available algorithms capable of identification of such complex nonlinearities, the unscented Kalman filter (UKF) is selected for these experiments as an effective method to update nonlinear dynamic system models under realistic conditions. The implementation of the platform is discussed for successful completion of these experiments, including required timing constraints and overall evaluation of the system. [Copyright &y& Elsevier]

ABSTRACT This study presents a smart system design to enhance the seismic performance of base-isolated buildings using modal LQG control strategies and smart devices, magnetorheological dampers. The advantages of applying the modal LQG approach for base isolation are discussed, including the explicit physical interpretation, simple weighting selection, and easy controller order reduction. 2DOF base isolation systems and the well-known, community-driven, benchmark base isolation problem focusing on structural control are employed to demonstrate the technique as realistic examples. This study analyzes the underlying interaction between structural dynamic properties and control forces that makes the modal approach effective and smart base isolation control possible and shows the seismic performance gain of the benchmark base isolation system offered by the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The detection of changes in vibrational behavior has long been applied as a potential means of damage detection. However, existing damage diagnosis techniques are primarily limited to linear models in which the stiffness of some of the elements is reduced to represent damage. Although these methods appear to be adequate for locating and quantifying the present damage in the structure, they are not sufficient for determining future performance of the structure, which can only be determined using nonlinear models. This paper focuses on the challenging task of updating nonlinear models for reinforced concrete civil engineering structures. A nonlinear hysteretic material model is applied to accurately portray the fundamental hysteretic behavior of concrete structures. A systematic methodology to perform damage detection and, more importantly, update the nonlinear model for prediction is proposed. This new method is designed to use low-level ambient vibration data to detect changes in the modal parameters. With the acquired modal information, the damage parameters that control the nonlinear material model are updated. The final updated nonlinear model may be utilized not only to evaluate the structure's current damage state but also to predict its future behavior. A concrete shear wall is analyzed numerically to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Highway sign support structures enhance traffic safety by allowing messages to be delivered to motorists related to directions and warning of hazards ahead, and facilitating the monitoring of traffic speed and flow. These structures are exposed to adverse environmental conditions while in service. Strong wind and vibration accelerate their deterioration. Typical damage to this type of structure includes local fatigue fractures and partial loosening of bolted connections. The occurrence of these types of damage can lead to a failure in large portions of the structure, jeopardizing the safety of passing traffic. Therefore, it is important to have effective damage detection approaches to ensure the integrity of these structures. In this study, an extension of the Angle-between-String-and-Horizon (ASH) flexibility-based approach is applied to locate damage in sign support truss structures at bay level. Ambient excitations (e.g. wind) can be considered as a significant source of vibration in these structures. Considering that ambient excitation is immeasurable, a pseudo ASH flexibility matrix constructed from output-only derived operational deflection shapes is proposed. A damage detection method based on the use of pseudo flexibility matrices is proposed to address several of the challenges posed in real-world applications. Tests are conducted on a 17.5-m long full-scale sign support truss structure to validate the effectiveness of the proposed method. Damage cases associated with loosened bolts and weld failures are considered. These cases are realistic for this type of structure. The results successfully demonstrate the efficacy of the proposed method to locate the two common forms of damage on sign support truss structures instrumented with a few accelerometers. [Copyright &y& Elsevier]

Because a structure’s modal parameters (natural frequencies and mode shapes) are affected by structural damage, finite- element model updating techniques are often applied to locate and quantify structural damage. However, the dynamic behavior of a structure can only be observed in a narrow knowledge space, which usually causes nonuniqueness and ill-posedness in the damage detection problem formulation. Thus, advanced optimization techniques are a necessary tool for solving such a complex inverse problem. Furthermore, a preselection process of the most significant damage parameters is helpful to improve the efficiency of the damage detection procedure. A new approach, which combines a parameter subset selection process with the application of damage functions is proposed herein to accomplish this task. Starting with a simple 1D beam, this paper first demonstrates several essential concepts related to the proposed model updating approach. A more advanced example considering a 2D model is then considered. To determine the capabilities of this approach for more complex structures, a trust region-based optimization method is adopted to solve the corresponding nonlinear minimization problem. The objective is to provide an improved robust solution to this challenging problem. [ABSTRACT FROM AUTHOR]

One challenge in the application of control systems to civil engineering structures is appropriate integration of a control system into a structure to achieve effective performance. Placement of control devices is strongly linked to the performance of a control system, and the most appropriate device placement scheme is strongly dependent on the performance objectives of the control system. Additionally, for the most effective control system, the placement scheme should be integrated with the design of the controller rather than sequential. This paper proposes an integrated technique to place devices and design controllers based on the use of genetic algorithms. The approach is flexible, allowing the designer to base the placement scheme on performance goals and/or system requirements. Active control devices are used and an H2/LQG controller based on acceleration feedback is selected for this study based on previous successes with this approach in civil engineering systems. To illustrate the proposed methodology, two numerical examples are considered. The first example considers a 40-story shear building, and the second is a full-scale, irregular, nine-story building. Control is achieved through the placement of one or more active control devices placed on various floors in an active bracing configuration. The improvements in the effectiveness of the proposed methodology as compared to previously developed techniques are demonstrated through comparative studies. [ABSTRACT FROM AUTHOR]

This paper considers the capabilities of semiactive control systems using magnetorheological dampers when applied to numerical models of full scale asymmetric buildings. Two full scale building models exhibiting coupled lateral and torsional motions are studied. The first case considered is a nine-story building with an asymmetric structural plan. The footprint of this building is rectangular, but the asymmetry is due to the distribution of shear walls. The second case considered is an L-shaped, eight-story building with additional vertical irregularity due to setbacks. Linear, lumped-parameter models of the buildings are employed herein to evaluate the potential of the control system to effectively reduce the responses of the buildings. In each case a device placement scheme based on genetic algorithms is used to place the control devices effectively. The proposed control systems are evaluated by simulating the responses of the models due to the El Centro 1940 and the Kobe 1995 earthquakes. In the second case, simulations are conducted using two-dimensional ground motions. The performance of the proposed semiactive control systems are compared to that of both ideal active control systems and passive control systems. [ABSTRACT FROM AUTHOR]

This paper addresses the third-generation benchmark problem on structural control, and focuses on the control of a full-scale, nonlinear, seismically excited, 20-story building. A semiactive design is developed in which magnetorheological (MR) dampers are applied to reduce the structural responses of the benchmark building. Control input determination is based on a clipped-optimal control algorithm which employs absolute acceleration feedback. A phenomenological model of an MR damper, based on a Bouc–Wen element, is employed in the analysis. The semiactive system using the MR damper is compared to the performance of an active system and an ideal semiactive system, which are based on the same nominal controller as is used in the MR damper control algorithm. The results demonstrate that the MR damper is effective, and achieves similar performance to the active and ideal semiactive system, while requiring very little power. [ABSTRACT FROM AUTHOR]

A benchmark study in structural health monitoring based on simulated structural response data was developed by the joint IASC–ASCE Task Group on Structural Health Monitoring. This benchmark study was created to facilitate a comparison of various methods employed for the health monitoring of structures. The focus of the problem is simulated acceleration response data from an analytical model of an existing physical structure. Noise in the sensors is simulated in the benchmark problem by adding a stationary, broadband signal to the responses. A structural health monitoring method for determining the location and severity of damage is developed and implemented herein. The method uses the natural excitation technique in conjunction with the eigensystem realization algorithm for identification of modal parameters, and a least squares optimization to estimate the stiffness parameters. Applying this method to both undamaged and damaged response data, a comparison of results gives indication of the location and extent of damage. This method is then applied using the structural response data generated with two different models, different excitations, and various damage patterns. The proposed method is shown to be effective for damage identification. Additionally the method is found to be relatively insensitive to the simulated sensor noise. [ABSTRACT FROM AUTHOR]

Presents a study that investigated the capabilities of multiple magnetorheological devices for seismic control of civil engineering structures. Background on studies of smart damping devices as a means of mitigating the dynamic effects of seismic loads on civil engineering structures; Methodology; Results and discussion.

Focuses on a study that evaluated the semiactive control algorithms for magnetorheological (MR) dampers. Details on MR dampers; Numerical example for algorithm evaluation; Conclusions.

Summary: The tuned mass damper (TMD) is widely used to mitigate vortex‐induced vibration (VIV) that approximates harmonic vibration in bridges. Robust performance is, however, still a significant concern in the VIV mitigation of bridges, especially when using a TMD with a small mass ratio. This study presents a study to apply an active tuned mass damper (ATMD) to control VIV in bridges. The control algorithm is independent of bridge parameters and can achieve real‐time tuning to the bridge motion by a few measurements. An adaptive TMD, obeying the parameter‐independent algorithm, is developed to improve its robust performance when exposed to uncertainty in the natural frequency of the bridge, and then, the ATMD is used to implement such an adaptive TMD. The ATMD control using the LQG algorithm is used as a comparison to illustrate the advantage of the parameter‐independent algorithm in the VIV mitigation of bridges. The effect of the control‐structure interaction on the control effect is also discussed. Demonstration of the method on a numerical simulation of a long‐span box‐girder bridge confirms the potential superiority of the ATMD control using the parameter‐independent algorithm in the VIV mitigation of bridges. [ABSTRACT FROM AUTHOR]

This research examines how ductility affects the durability of lunar surface structures against recurring disturbances like moonquakes, micrometeorite impacts, and thermal cycles over an extended period. The structural performance at various levels of ductility was determined by adjusting material parameters and the thickness of a reference multilayered dome structure. Moonquake and micrometeorite impact-induced lateral displacements were estimated using a reduced-order model under a control-oriented dynamic computational modeling framework. The study considered the degradation of the metallic dome's strength properties over time due to thermal cycles. Fragility curves were generated by assessing the likelihood of reaching three predefined damage levels as a result of multiple hazards. Additionally, a discounted cash flow analysis was conducted to incorporate a financial aspect into the performance comparison. The findings revealed that structures with sufficient ductility capacity have a lower probability of sustaining severe damage or collapsing within a shorter time frame. Hence, having ductile structures in lunar environments is advantageous as it allows the postponement of maintenance and repair actions, thereby conserving scarce resources for more urgent tasks. Moreover, the financial analysis demonstrated that lunar habitats with higher ductile capacities result in larger net present values, offering a higher return on the initial investment. [ABSTRACT FROM AUTHOR]

Benchmark problems have been especially useful in enabling a community of researchers to leap forward on a given topic, distill the lessons learned, and identify the capabilities and limitations of various approaches. Next, Palacio-Betancur and Gutierrez Soto implement on-line parameter estimation to inform an adaptive time series controller for compensation of actuator dynamics. A sliding mode controller is developed by Xu et al. to deal with the uncertainties that exist in the physical components of the RTHS setup and paired with an improved adaptive polynomial-based forward prediction algorithm to compensate for actuator dynamics. [Extracted from the article]

Reconnaissance teams collect perishable data after each disaster to learn about building performance. However, often these large image sets are not adequately curated, nor do they have sufficient metadata (e.g., GPS), hindering any chance to identify images from the same building when collected by different reconnaissance teams. In this study, Siamese convolutional neural networks (S‐CNN) are implemented and repurposed to establish a building search capability suitable for post‐disaster imagery. This method can automatically rank and retrieve corresponding building images in response to a single query using an image. In the demonstration, we utilize real‐world images collected from 174 reinforced‐concrete buildings affected by the 2016 Southern Taiwan and the 2017 Pohang (South Korea) earthquake events. A quantitative performance evaluation is conducted by examining two metrics introduced for this application: Similarity Score (SS) and Similarity Rank (SR). [ABSTRACT FROM AUTHOR]

A yearning for visually appealing bridges that are longer and more slender is driving advances in construction materials and techniques, alongside the necessary improvements in design guidelines and standards. However, lighter and more slender structures are also more susceptible to vibration. Certain pedestrian structures do frequently exhibit excessive vibrations when they are dynamically excited by walking pedestrians. To improve pedestrian-structure design procedures, uncertainties in the human biodynamic subsystem should be systematically included, and should represent a suitable range of possible body characteristics. Variations in the representation of the human body are intended to capture differences in physical characteristics while that human is walking on a lively surface. Here these variations are described as parametric uncertainties, while the structure is assumed to be known and deterministic. A time-varying and coupled model of the pedestrian-structure system is adopted, and the corresponding perturbations are defined as a set of structured uncertainties describing imprecise biodynamic parameters that can adopt any value within their proposed intervals. This parametric model with structured uncertainty is then used to obtain the range of biodynamic parameters for a variety of pedestrians by comparing the predicted structural responses against the experimental data. Experimental results are used to determined a set of intervals of biodynamic parameters such as mass, damping, stiffness, and pace frequency for a walking pedestrian. The simulation results show that the developed human-structure model with associated parameter intervals is able to obtain realistic serviceability demands by predicting the range of structural responses for a wide range of pedestrians. [ABSTRACT FROM AUTHOR]

• This technique supports engineers by providing automated capabilities to make decisions in the field. • Improving consistency and accelerating decisions, an imperative after a major disruptive event. • Automated post-event data collection to facilitate the data analysis process and reduce bias in the results. In post-event reconnaissance missions, engineers and researchers collect perishable information about damaged buildings in the affected geographical region to learn from the consequences of the event. A typical post-event reconnaissance mission is conducted by first doing a preliminary survey, followed by a detailed survey. The objective of the preliminary survey is to develop an understanding of the overall situation in the field, and use that information to plan the detailed survey. The preliminary survey is typically conducted by driving slowly along a pre-determined route, observing the damage, and noting where further detailed data should be collected. This involves several manual, time-consuming steps that can be accelerated by exploiting recent advances in computer vision and artificial intelligence. The objective of this work is to develop and validate an automated technique to support post-event reconnaissance teams in the rapid collection of reliable and sufficiently comprehensive data, for planning the detailed survey. The focus here is on residential buildings. The technique incorporates several methods designed to automate the process of categorizing buildings based on their key physical attributes, and rapidly assessing their post-event structural condition. It is divided into pre-event and post-event streams, each intending to first extract all possible information about the target buildings using both pre-event and post-event images. Algorithms based on convolutional neural networks (CNNs) are implemented for scene (image) classification. A probabilistic approach is developed to fuse the results obtained from analyzing several images to yield a robust decision regarding the attributes and condition of a target building. We validate the technique using post-event images captured during reconnaissance missions that took place after hurricanes Harvey and Irma. The validation data were collected by a structural wind and coastal engineering reconnaissance team, the National Science Foundation (NSF) funded Structural Extreme Events Reconnaissance (StEER) Network. [ABSTRACT FROM AUTHOR]

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images' locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building. [ABSTRACT FROM AUTHOR]

• Self-validating implementations of the UKF over-fit to the identification signal. • Cross-validating implementations of the UKF robustly identify the system model. • Global sensitivity analysis techniques can inform the identification process. Nonlinear energy sink (NES) devices have recently been introduced as a means of passive structural control and have been shown to effectively dissipate energy from structural systems during extreme vibrations. Due to their essential geometric nonlinearities, time domain based methods are often applied for identifying their system parameters, which is a challenging task. The unscented Kalman filter (UKF) has been shown in numerical studies to be robust to highly nonlinear systems with noisy data and therefore presents a promising option for identification. In this study, the UKF is used to determine the model parameters of an experimental NES device whose behavior is governed by a geometric nonlinearity in its stiffness and a friction-based nonlinearity in its damping. The standard implementation of the UKF is compared with two implementation methods developed by the authors, which vary in their use of experimental responses to train the NES device model. The impact of choosing different prior distributions on the parameters is also analyzed through Latin hypercube sampling to enhance the quality of the identification for practical implementation, where the prior distribution on the parameters is often ill-defined. The identified models generated using one of the proposed UKF implementation methods is shown to provide a robust model of the NES, demonstrating that the UKF can be used for parameter identification with this class of devices. [ABSTRACT FROM AUTHOR]

This paper proposes a novel real-time hybrid simulation (RTHS) method for advancing active mass driver (AMD) control techniques. A comprehensive derivation of the equations of motion are developed, where the tracking problem of absolute acceleration is addressed by transforming it into a displacement tracking problem, within the RTHS control loop. The simulated and experimental results demonstrate acceptable acceleration tracking using this methodology, enabling a seamless and effective option for controlling the shake table transfer system, within the RTHS framework. To demonstrate and validate the effectiveness of the proposed techniques, complete AMD shake table, and AMD-RTHS experiments are conducted. To address the effects of uncertainty, a series of tests are conducted to form an uncertainty response envelope. Comparisons between RTHS and shake table tests show that the responses lie within RTHS uncertainty regions, demonstrating that acceleration can be successfully tracked. This study shows that an AMD control experiment can be realized using RTHS techniques, thus offering an option to advance the development of AMD control techniques. Finally, an RTHS framework is proposed for performance evaluation of structural control methods using AMDs. • Tracking control for RTHS using estimated states from acceleration measurements. • Active mass damper system for structural control, realized using RTHS techniques. • Physics-based models including uncertainties like noise and unknown dynamics. • Virtual RTHS simulation using MATLAB/Simulink real-time functionality. • Experimental verification of the model and validation of the proposed methodology. [ABSTRACT FROM AUTHOR]

The inspection of highway bridge structures in the US is a task critical to the national transportation system. Inspection images contain abundant visual information that can be exploited to streamline bridge assessment and management tasks. However, historical inspection images often go unused in subsequent assessments because they tend to be disorganized and unlabeled. Further, due to the lack of global positioning system (GPS) metadata and visual ambiguity, it is often difficult for other inspectors to identify the location on the bridge where past images were taken. Although many approaches are being considered toward fully automated or semiautomated methods for bridge inspection, there are research opportunities to develop practical tools for inspectors to make use of those images already in a database. In this study, a deep learning–based image similarity technique is developed and combined with image geolocation data to localize and retrieve historical inspection images based on a current query image. A Siamese convolutional neural network (SCNN) is trained and validated on a gathered data set of over 1,000 real world bridge deck images collected by the Indiana Department of Transportation. A composite similarity (CS) metric is created for effective image ranking, and the overall method is validated on a subset of eight bridge's images. The results show promise for implementation into existing databases and for other similar structural inspections, showing up to an 11-fold improvement in successful image retrieval when compared with random image selection. [ABSTRACT FROM AUTHOR]

The establishment of secure earth-independent long-term lunar habitats has been envisioned by numerous government agencies and private companies. Recent advancements in assessing seismic hazards caused by shallow moonquakes have highlighted the importance of incorporating this phenomenon into the design of robust and resilient lunar structures. However, further research is required to explore lunar habitat design that considers seismic loads. This paper proposes assessing the structural response of a lunar habitat made of sulfur concrete covered with a regolith layer. The numerical model of the structure is subjected to gravitational, internal pressure and seismic loads. The seismic analysis of the structure is carried out using spectral and nonlinear time history methods. Conditional mean spectra for shallow moonquakes with return periods of 75, 475, 970 and 2475 years are used in the seismic analysis. The records used for the temporal analyses were ground motions that agree with a preliminary seismic hazard on the Moon. The results of temporal analyses reveal that shallow moonquakes with return periods greater than 475 years can lead to the loss of the global stability of the structure. Consequently, the findings imply that seismic loads have the potential to impose unacceptable demands on lunar structures constructed from in-situ materials like sulfur concrete. Hence, it is imperative to incorporate seismic considerations in the design process for developing resilient and long-term lunar habitats. • Seismic analysis of a lunar structure was carried out using spectral and temporal methods. • A methodology was adapted to scale moonquake records to different hazard spectra obtained from probabilistic methods. • It was demonstrated that seismic demand might generate unacceptable deformations in lunar structures built with in-situ materials. [ABSTRACT FROM AUTHOR]

After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many cases, the images collected are captured without sufficient spatial context. When damage is severe, it may be quite difficult to even recognize the building. Accessing images of the predisaster condition of those buildings is required to accurately identify the cause of the failure or the actual loss in the building. Here, to address this issue, we develop a method to automatically extract pre‐event building images from 360° panorama images (panoramas). By providing a geotagged image collected near the target building as the input, panoramas close to the input image location are automatically downloaded through street view services (e.g., Google or Bing in the United States). By computing the geometric relationship between the panoramas and the target building, the most suitable projection direction for each panorama is identified to generate high‐quality 2D images of the building. Region‐based convolutional neural networks are exploited to recognize the building within those 2D images. Several panoramas are used so that the detected building images provide various viewpoints of the building. To demonstrate the capability of the technique, we consider residential buildings in Holiday Beach in Rockport, Texas, United States, that experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual postdisaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event. [ABSTRACT FROM AUTHOR]

After a disaster strikes an urban area, damage to the façades of a building may produce dangerous falling hazards that jeopardize pedestrians and vehicles. Thus, building façades must be rapidly inspected to prevent potential loss of life and property damage. Harnessing the capacity to use new vision sensors and associated sensing platforms, such as unmanned aerial vehicles (UAVs) would expedite this process and alleviate spatial and temporal limitations typically associated with human-based inspection in high-rise buildings. In this paper, we have developed an approach to perform rapid and accurate visual inspection of building façades using images collected from UAVs. An orthophoto corresponding to any reasonably flat region on the building (e.g., a façade or building side) is automatically constructed using a structure-from-motion (SfM) technique, followed by image stitching and blending. Based on the geometric relationship between the collected images and the constructed orthophoto, high-resolution region-of-interest are automatically extracted from the collected images, enabling efficient visual inspection. We successfully demonstrate the capabilities of the technique using an abandoned building of which a façade has damaged building components (e.g., window panes or external drainage pipes). [ABSTRACT FROM AUTHOR]

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state's databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities. [ABSTRACT FROM AUTHOR]

Hydraulic actuators play a key role in experimental structural dynamics. In a previous study, a physics-based model for a servo-hydraulic actuator coupled with a nonlinear physical system was developed. Later, this dynamical model was transformed into controllable canonical form for position tracking control purposes. For this study, a nonlinear device is designed and fabricated to exhibit various nonlinear force-displacement profiles depending on the initial condition and the type of materials used as replaceable coupons. Using this nonlinear system, the controllable canonical dynamical model is experimentally validated for a servo-hydraulic actuator coupled with a nonlinear physical system. [ABSTRACT FROM AUTHOR]

Hydraulic actuators have been widely used to experimentally examine structural behavior at multiple scales. Real-time hybrid simulation (RTHS) is one innovative testing method that largely relies on such servo-hydraulic actuators. In RTHS, interface conditions must be enforced in real time, and controllers are often used to achieve tracking of the desired displacements. Thus, neglecting the dynamics of hydraulic transfer system may result either in system instability or sub-optimal performance. Herein, we propose a nonlinear dynamical model for a servo-hydraulic actuator (a.k.a. hydraulic transfer system) coupled with a nonlinear physical specimen. The nonlinear dynamical model is transformed into controllable canonical form for further tracking control design purposes. Through a number of experiments, the controllable canonical model is validated. [ABSTRACT FROM AUTHOR]

Hybrid simulation combines numerical simulation and physical testing, and is thus considered to be an efficient alternative to traditional testing methodologies in the evaluation of global performance of large or complex structures. Real-time hybrid simulation (RTHS) is performed when it is important to fully capture rate-dependent behaviors in the physical substructure. Although the demand to test more complex systems grows, not every laboratory has the right combination of computational and equipment resources available to perform largescale experiments. Distributed real-time hybrid simulation (dRTHS) facilitates testing that is to be conducted at multiple geographically distributed laboratories while utilizing the Internet to couple the substructures. One major challenge in dRTHS is to accommodate the unpredictable communication time delays between the various distributed sites that occur as a result of Internet congestion. Herein, a dRTHS framework is proposed where a modified Smith predictor is adopted to accommodate such communication delays. To examine and demonstrate the sensitivity of the proposed framework to communication delays and to modeling errors, parametric analytical case studies are presented. Additionally, the effectiveness of this dRTHS framework is verified through successful execution of multisite experiments. The results demonstrate that this framework provides a new option for researchers to evaluate the global response of structural systems in a distributed real-time environment. [ABSTRACT FROM AUTHOR]

This paper reviews the conceptual and technical advances in multi-actuator dynamic loading in modern structural testing. In particular, a focus is given to the developments and challenges in multi-axial hybrid simulation (maHS) and multi-axial real-time hybrid simulation (maRTHS), where a specimen is subjected to multi-directional dynamic loading by interacting with a numerical simulation of its surrounding structural subsystems and components. This review introduces the general framework for maHS and maRTHS, describing substructuring techniques, loading equipment, and nonlinear kinematics. In particular, the process of dynamic compensation for multi-actuator loading assemblies in maRTHS is explored. Different compensation architectures in the task (Cartesian) and joint (actuator) spaces are covered, and each alternative is assessed on its own merits for the dynamic synchronization of multi-actuator loading platforms. Finally, current challenges in maHS and maRTHS testing are identified, with recommendations for future research endeavors for the scientific community. • This article organizes the research available in multi-actuator hybrid simulations. • Multiple actuators are required for realistic and cost-effective structural testing. • A unified general framework for multi-actuator hybrid simulation is described. • Multi-actuator testing platforms around the world are summarized. • Current challenges and opportunities in this domain are presented. [ABSTRACT FROM AUTHOR]

The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must operate under continuous disruptive conditions arising from extreme environments like meteoroid impacts, extreme temperature fluctuations, galactic cosmic rays, destructive dust, and seismic events. Loss of air or atmospheric leakage from a habitat poses safety challenges that demand proper attention. Such leakage may arise from micro-meteoroid impacts, crack growth, bolt/rivet loosening, and seal deterioration. In this paper, leakage estimation in deep space habitats is posed as an inverse problem. A forward pressure-based dynamical model is formulated for atmospheric leakage. Experiments are performed on a small-scaled pressure chamber where different leakage scenarios are emulated and corresponding pressure values are measured. An exponentially-weighted adaptively-refined search (EWARS) algorithm is developed and validated for the inverse problem of real-time leakage estimation. It is demonstrated that the proposed methodology can achieve real-time estimation and tracking of constant and variable leaks with accuracy. • Leakage estimation in deep space habitats is proposed as an inverse problem. • A novel exponential-weighted adaptively-refined search (EWARS) scheme is implemented. • EWARS is a real-time model calibration framework. • EWARS estimates are fast and less noisy as compared to full brute force search. [ABSTRACT FROM AUTHOR]

The fractional-order derivative Zener (FDZ) model can reasonably predict the frequency dependence behavior of rubber-like materials. However, its capability to capture nonlinear behaviors is limited. In our previous work, we built a horizontal shear model of a multidimensional rubber-like base isolation bearing, modifying the FDZ model to compensate for its inability to reproduce the nonlinear behaviors of amplitude dependence and slow stabilization. In this paper, the tension-compression behavior of the same bearing in the vertical direction is studied, where strain-stiffening nonlinear behavior is exhibited. A different strategy is used to establishing the modified tension-compression FDZ (CFDZ) model, which is shown to have good agreement with experimental results. State-space representation of the CFDZ model is presented and included in modelling a train-bridge-bearing system for dynamic analysis. Simulation results demonstrate that the proposed bearing achieves a high base isolation performance in the vertical direction. [ABSTRACT FROM AUTHOR]

Data are one of the main assets of earthquake engineering. Laboratory experiments can be extremely expensive and time consuming to replicate and, therefore, long-term preservation of experimental data and sharing the data with users has become one of the disciplinary priorities. There is a growing demand for international partnerships, which creates a need for data sharing, in an attempt to maximise research impact and to tackle experimental set-ups that could not be realised otherwise. However, there is a patent lack of interoperability between the institutions forming the earthquake engineering community, which inhibits efficient collaboration between them. In this paper, we discuss a vision about the directions that experimental data should take in the coming years, focusing on two aspects: enhanced international collaborations and implementation of open data access. We also describe the progress that has been made towards this vision, by establishing an open platform for the integration of earthquake hazard mitigation resources called Celestina. Celestina is supported by Semantic Web technologies, and uses an ontology as its integration data model. A prototype of the platform has been developed and tested between NEES (Purdue University, in the US), the University of Oxford (in UK) and EUCENTRE (in Italy), and a small proof of concept has enabled integrated experimental data from Oxford and EUCENTRE through the NEES cyberenvironment. This demonstration provides an example which has the potential to catalyze a new generation of research progress enabled by international data sharing. [ABSTRACT FROM AUTHOR]

After hazard events, large numbers of images are collected by reconnaissance teams to document the post-event state of structures, and to assess their performance and improve design procedures and codes. The majority of these data are captured as images and manually labeled. This highly repetitive task requires considerable domain expertise and time. Advances in deep learning have enabled researchers to rapidly classify reconnaissance images. Thus far, these classification methods are limited to a simple classification schema in which the classes are all either mutually exclusive or independent. To date, an efficient classification system of a complex schema containing many classes arranged in a multi-level hierarchical structure is not available to support earthquake reconnaissance. To address this gap, this paper introduces a comprehensive classification schema and a multi-output deep convolutional neural network (DCNN) model for rapid postearthquake image classification. In contrast to past work, herein a single multi-output DCNN classification model with a hierarchy-aware prediction was trained to enable the rapid organization of images. The performance of the proposed multi-output model was validated through comparisons with multi-label and multi-class models using an F1-score. As result, the multi-output model outperformed other models. Then, the multi-output model was deployed to a web-based platform called the Automated Reconnaissance Image Organizer, which can be used to easily organize earthquake reconnaissance images. [ABSTRACT FROM AUTHOR]

Structural control systems offer an attractive approach to protect civil infrastructures from natural hazards such as earthquakes. Wireless structural control systems that utilize wireless sensors for sensing, communication, and control have drawn increased attention because of the flexible installation, rapid deployment, and reduced cost. Although there are studies of wireless control systems for civil structures, a benchmark problem that captures not only the dynamics of the plant but also the realistic features of a wireless network has not been available. In this paper, a benchmark model for an active mass driver system with a wireless sensor network is presented. This wireless control benchmark model combines a seismically excited building benchmark model developed with Simulink (Matlab, MathWorks, Inc., Natick, MA, USA) and a wireless sensor network implemented in simulation using a state-of-the-art wireless simulator TOSSIM (UC Berkeley, Berkeley, CA, USA). Wireless signal and noise traces collected from a real-world multistory building are used as inputs to TOSSIM to realistically simulate the wireless sensor network. Wireless control design issues such as network-induced delay, data loss, available sensor measurements, measurement noises, and control constraints can be studied with this benchmark model. A sample controller is provided to illustrate the wireless control design. Evaluation criteria have been provided to examine resources and control performances. [ABSTRACT FROM AUTHOR]

Real-time hybrid simulation (RTHS) is gaining acceptance as an efficient and cost-effective method for realistic structural evaluation. Advances in real-time computing and control methods have enabled research in the development of this novel methodology to progress rapidly. However, to explore effectiveness and accuracy, and thus build broader confidence in the use of this method as an alternative to shake table testing, there is a need to better understand and address the key features that determine the success of an RTHS. Here we discuss the design and analysis of a SDOF RTHS case study conducted in Purdue University’s Intelligent Infrastructure Systems Lab (IISL). We examine the key factors that determine the success, through configuration of the test using predictive indicators, design of an appropriately effective actuator controller, and a thorough comparison with shake table testing. The reference structure chosen for this case study is a single story, moment resisting frame structure. This particular specimen is of lab scale and well-known component properties, making it a suitable choice for such an investigation. However, noise, control–structure interaction and damping introduce numerous challenges typically faced in establishing an effective RTHS configuration. We investigate two key issues that lead to the design of a successful RTHS, specifically the partitioning between numerical and physical substructure for stability and performance, and the actuator motion control algorithm. Predictive indicators are demonstrated to be particularly helpful for properly configuring an RTHS experiment to meet a researcher’s specified objectives. Furthermore a direct comparison is conducted to examine the ability of RTHS to replicate a shake table test. The results demonstrate that with proper partitioning and actuator control design, successful RTHS can be implemented despite unfavorable transfer system properties. [ABSTRACT FROM AUTHOR]

Stability in Real Time Hybrid Simulation (RTHS) has been shown to be largely affected by system dynamics and associated phase lags. This lag typically originates in the physical components and considerable research has been conducted to compensate for it. Within the computational component of RTHS, different time integration algorithms are employed to achieve a more stable and accurate solution, mostly focusing on dissipating the high frequency content in the model. However, in RTHS, an inherent computational delay exists in the force measurement due to the sequential nature of communication between the numerical and experimental sub- structures. In this article, it is demonstrated that this computational delay affects performance and stability of closed loop RTHS even when no other delays or phase lags are present. This finding is validated through theoretical derivation and simulation results. A modified Runge-Kutta (MRK) integration algorithm is proposed to reduce the effect of computational delay. The MRK integration involves a three-stage computation: (1) the pseudo response is calculated using the delayed force measurement; (2) feedback force from the physical component for the next step is predicted using the pseudo response; and (3) the corrected structural response is then computed using the predicted feedback force. Both analytical and simulation results confirm that the MRK integration scheme is stable and accurate for a wide range time steps and is robust with respect to modeling error and nonlinearity in the experimental substructure. A moment-resisting frame is used as the experimental substructure in different cases of RTHS to validate the MRK integration method. This approach can also be adapted to other existing numerical integration schemes by applying the proposed three-stage computation process approach. [ABSTRACT FROM AUTHOR]

Real-time hybrid simulation (RTHS) offers an economical and reliable methodology for testing integrated structural systems with rate-dependent behaviors.Within a RTHS implementation, critical components of the structural system under evaluation are physically tested, while more predictable components are replaced with computational models under a one-to-one timescale execution. As a result, RTHS implementations provide a more economical and versatile alternate approach to evaluating structural/rate-dependent systems under actual dynamic and inertial conditions, without the need for full-scale structural testing. One significant challenge in RTHS is the accurate representation of the physical complexities within the computational counterparts. For RTHS, the requirement for computational environments with reliable modeling and real-time execution capabilities is critical. Additionally, the need of a flexible environment for implementation and easy integration of such platforms with remaining RTHS components has also been established. An open-source computational platform, RT-Frame2D, for the RTHS of dynamically excited steel frame structures has been developed to satisfy these demands. The computational platform includes both adequate modeling capabilities for the nonlinear dynamic analysis of steel frame structures under real-time execution, and a versatile design to allow its efficient integration within a RTHS framework. Comparisons of RT-Frame2D modeling capabilities with those of a well-known simulation tool, in addition to challenging experimental implementations based on several RTHS scenarios, are performed herein to verify the accuracy, stability, and real-time execution performance of the proposed computational platform. [ABSTRACT FROM AUTHOR]