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As the seismic hazard has been updated for the central U.S., state Departments of Transportation (DOTs) find an increasing need to assess the seismic vulnerability of their bridge network. Traditional methods to perform seismic assessment require developing dynamic models for each bridge. However, this approach requires specialized engineering knowledge and information from structural drawings, and is time-consuming. To streamline this important task, a simplified dynamic modeling procedure is described that leverages readily available information from DOTs’ asset management databases. With a minimal amount of additional data items, the asset management database can be used to identify vulnerable bridges rapidly and with sufficient accuracy for the prioritization of retrofit decisions. A detailed analysis of a 100-bridge sample set identified typical vulnerabilities and established corresponding capacity thresholds. The rapid seismic vulnerability assessment methodology is implemented as an Excel macro-enabled tool for bridge owners and asset managers to rapidly assess the vulnerability of each individual bridge based on current information in the database, and then classify the vulnerability of each individual bridge as low, medium, or high. Current DOT databases used for asset management in regions of low-to-moderate seismicity do require some data items be added for a robust assessment. These data items are identified here and leveraged to demonstrate the method. The rapid assessment methodology presented can be implemented to effectively identify the most vulnerable bridges in a bridge network, thus facilitating a rapid state bridge inventory network assessment to prioritize and inform actions such as maintenance and rehabilitation.

The objective of this study is to develop, simulate and verify experimentally a model of a nonlinear spring, based on the principle of a cantilevered beam with a mass on its tip, and whose overall lateral vibration is constrained by a specially shaped rigid boundary. The focus here is the use of this spring for vibration reduction applications. The modeling approach uses concepts of plane kinematics of rigid bodies, combined with quasi-static analysis to develop suitable equations of motion for a base-excited spring with a ninth-order geometric nonlinearity. In addition, a parametric identification procedure is implemented for obtaining the required coefficients for computational simulations. An approximated analytical solution to the model is completed with the aid of the method of harmonic balance and its stability is assessed through Floquet theory. Finally, the model is experimentally verified, with the use of two specimens, fabricated specifically for this study. The model, simulations and experimental measurements show the hardening and broadband behavior of the nonlinear spring.

Despite the wide range of possible scenarios in the aftermath of a disruptive event, each community can make choices to improve its resilience, or its ability to bounce back. A resilient community is one that has prepared for, and can thus absorb, recover from, and adapt to the disruptive event. One important aspect of the recovery phase is assessing the extent of the damage in the built environment through post-event building inspections. In this paper, we develop and demonstrate a resilience-based methodology intended to support rapid post-event decision-making about inspection priorities with limited information. The method uses the basic characteristics of the building stock in a community (floor area, number of stories, type of construction and configuration) to assign structure-specific fragility functions to each building. For an event with a given seismic intensity, the probability of each building reaching a particular damage state is determined, and is used to predict the actual building states and priorities for inspection. Losses are computed based on building usage category, estimated inspection costs, the consequences of erroneous decisions, and the potential for unnecessary restrictions in access. The aim is to provide a means for a community to make rapid cost-based decisions related to inspection of their building inventory. We pose the decision problem as an integer optimization problem that attempts to minimize the expected loss to the community. The advantages of this approach are that it: (i) is simple, (ii) requires minimal inventory data, (iii) is easily scalable, and (iv) does not require significant computing power. Use of this approach before the hazard event can also provide a community with the means to plan and allocate resources in advance of an event to achieve the desirable resiliency goals of the community.

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 pre-disaster 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 360o 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, Texas, the United States which experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual post-disaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event.

The dynamic interaction between a bridge and a moving train has been widely studied. However, there is a significant gap in our understanding of how the presence of isolation bearings influences the dynamic response, especially when an earthquake occurs. Here, we formulate a coupled model of a train-bridge-bearing system to examine the bearings’ dynamic effects on the system responses. In the analysis, the train is modeled as a moving oscillator, the bridge is a one span simply supported beam and one isolation bearing is installed under each support of the bridge. A mathematical model using fractional derivatives is used to capture the viscoelastic properties of the bearings. The vertical response is the focus of this investigation. Dynamic substructuring is used in modeling to efficiently capture the coupled dynamics of the entire system. Illustrative numerical simulations are carried out to examine the effects of the bearings. The results demonstrate that although the presence of bearings typically decreases the bridge seismic responses, there is a potential to increase the bridge response induced by the moving train.

With the recent identification of the Wabash Valley Seismic Zone in addition to the New Madrid Seismic Zone, Indiana’s Department of Transportation (INDOT) has become concerned with ensuring the adequate seismic performance of their bridge network. While INDOT made an effort to reduce the seismic vulnerability of newly-constructed bridges, many less recent bridges still have the potential for vulnerability. Analyzing these bridges’ seismic vulnerability is a vital task. However, developing a detailed dynamic model for every bridge in the state using information from structural drawings is rather tedious and time-consuming. In this study, we develop a simplified dynamic assessment procedure using readily-available information from INDOT’s Bridge Asset Management Program (BIAS), to rapidly identify vulnerable bridges throughout the state. Eight additional data items are recommended to be added into BIAS to support the procedure. The procedure is applied in the Excel file to create a tool, which is able to automatically implement the simplified bridge seismic analysis procedure. The simplified dynamic assessment procedure and the Excel tool enable INDOT to perform seismic vulnerability assessment and identify bridges more frequently. INDOT can prioritize these bridges for seismic retrofits and efficiently ensure the adequate seismic performance of their assets.

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&rsquo, 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.

Base isolation is a powerful technique to prevent damage in low- and medium-rise structures during an earthquake. Nowadays, the extensive use of high-damping viscoelastic (VE) materials in ...

This paper focuses on characterization testing and validation of the dynamic model of a new type of high-damping density electromagnetic-based damping device (EMD) referred to as Magneto-Solid Damper (MSD) in which an eddy current damping mechanism acts in parallel with a solid friction mechanism to dissipate the input kinetic energy. The proposed MSD consists of a ferromagnetic plate, two copper plates placed on two sides of the ferromagnetic plate in parallel, and a planar array of permanent magnets (PMs) placed between the ferromagnetic plate and each of the two copper plates. The PMs arrays are attached to the ferromagnetic plate through non-magnetic friction pads. The friction force is developed between these friction pads and the ferromagnetic plate when the PMs arrays move relative to the ferromagnetic plate. The motion of the PMs arrays relative to the copper plates, on the other hand, generates the eddy current damping. A laboratory prototype of the proposed MSD has been designed and fabricated for the purpose of characterization testing. Finite element and simplified analytical models of this damper have also been developed to analyze the magnetic interaction between the PMs arrays and the ferromagnetic and copper plates. A dynamic model has been developed to characterize the force–displacement behavior of the proposed MSD. The parameters of this dynamic model are identified through a series of characterization tests on the prototype MSD under harmonic excitations of different frequencies. The identified dynamic model is validated by subjecting the prototype MSD to real ground motion records. It is concluded that that the proposed dynamic model is capable of describing the force–displacement behavior of the proposed MSD for a wide range of operating conditions.

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.

Post-event building reconnaissance teams have a clear mission. These teams of trained professional engineers, academic researchers and graduate students are charged with collecting perishable data to be used for learning from disasters. A tremendous amount of perishable visual data can be generated in just a few days. However, only a small portion of the data collected is annotated and used for scientific purposes due to the tedious and time-consuming processes needed to sift through and analyze them. This crucial process still significantly relies on trained human operators. To distill such information in an efficient manner, we introduce a novel and powerful method for post-disaster evaluation by processing and analyzing big visual data in an autonomous manner. Recent convolutional neural network (CNN) algorithms are implemented to extract visual content of interest automatically from the collected images. Image classification and object detection are incorporated into the procedures to achieve accurate extraction of target contents of interest. As an illustration of the computational technique and its capabilities, collapse classification and spalling detection in concrete structures are demonstrated using a large volume of images gathered from past earthquake disasters.

Hybrid simulation has been demonstrated to be a powerful method to evaluate the system-level dynamic performance of structure. With the numerical substructure analyzed with finite element software and the difficult-to-model components tested with an experimental substructure, complex structures with sophisticated behaviors can readily be examined through a hybrid simulation. To coordinate and synchronize the substructures in hybrid simulation, software is required. In recent studies, model updating has been integrated into hybrid simulation to improve testing accuracy by updating the numerical model during the analysis. However, online model updating scheme requires some modifications in the typical hybrid simulation testing procedure, and this greater complexity is entailed in its implementation regarding the collaboration of identification algorithms with existing hybrid simulation software. To address this issue and broaden the utilization of hybrid simulation with model updating, an existing platform named HyTest originally for conventional hybrid simulation is extended for this purpose. This version of HyTest facilitates the online identification of material constitutive parameters using experimental measurements in its finite element based identification module. It also includes a data center with a uniform data transmission protocol to incorporate different substructures and modules. A numerical example is used to demonstrate the online identification of material parameters for concrete and steel models in a reinforced column, and to verify the accuracy of the identification module. Lastly the effectiveness of HyTest in conducting hybrid simulation with model updating is validated using actual hybrid tests on a steel frame.

Detecting the source of activities in buildings is of interest to enable the potentials for Smart Buildings in industrial facilities, public infrastructure, hospitals, and commercial or residential buildings. This problem has been addressed using intrusive techniques, including vision-based methods. Recent studies have instead demonstrated that unobtrusive approaches could provide comparable results to the intrusive methods. These methods have the capability to identify and classify atypical floor vibration signals to specific sources, e.g., human fall detection or rotary machines fault detection. Most successful applications of vibration-based monitoring use traditional feature learning methods. These methods extract the temporal and spectral features from the vibration signals, and human experts are involved in selecting the features for classification. However, they typically require a labor-intensive process, which may add uncertainty and bias to the results. Several promising alternatives to these approaches have recently been proposed which take advantage of the time-dependent frequency spectrum of the vibration signals (i.e., spectogram). These representations are then fed into a deep convolutional neural network (CNN) architecture for image-like classification, training an accurate model. However, activty source detection in buildings requires the identification of numerous possible sources (classes), which may lead to low accuracy and confidence in the results. Thus, in this study, we incorporate prior knowledge of a target application, called a hierarchical structure of activity sources (classes). We develop a hierarchical CNN-based technique, which has a relatively easy setup, is modifiable, and has an extendable structure, to use floor vibration signatures to detect the source of activities in a building. We validate the method on a recently released benchmark dataset for human activity identification by implementing a flat numerous-class CNN-based approach, and the proposed hierarchical CNN-based approach. The results indicate that the hierarchical CNN-based approach outperforms other methods.

This paper presents a real-time hybrid simulation (RTHS) strategy where the numerical and experimental substructures are executed at two different rates to optimize computational resources while maintaining an effective actuator control. The concept is referred to here as multi-rate real-time hybrid simulation (mrRTHS), and this approach is intended to enable low-cost RTHS by facilitating testing in the case of limited computational resources. Operated on a Laboratory Virtual Engineering Workshop (LabVIEW) real-time target, the mrRTHS concept is demonstrated through both a single- and multipledegree-of-freedom (SDOF) and (MDOF) mass-spring-damper system. The numerical substructure generates a displacement signal with a coarse time step of Δt. Using the current and three previous displacement data points, a finer control signal is defined with a time step of δt, using a third-order polynomial algorithm–referred to here as the polynomial fitting extrapolator. Both the numerical substructure and polynomial fitting extrapolator is executed with a sampling rate of Δt by an on-board single-core processor–referred to here as the digital signal processor (DSP). Through a field-programmable gate array (FPGA) the control signal is compensated and transmitted to the transfer system through an I/O module with a sampling rate of 1 kHz (i.e. δt = 0.001 sec). The ratio between Δt and δt are an integer–referred to here as the execution ratio. For an execution ratio of 1:5 and 1:10 the system performance is evaluated against a numerical model of the emulated structure–referred to here as the reference structure. For both the SDOF and MDOF system, a good correlation between the mrRTHS and reference is achieved with execution ratios of 1:5 and 1:10. When changing the execution ratio from 1:5 to 1:10, approximately 50% reduction of the required computational resources on the DSP is achieved.

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.

A large volume of images is collected during postdisaster building reconnaissance. For both older and new buildings, the structural drawings are an essential record of the structural inform...

Future permanent habitats on the Moon may facilitate space exploration by serving as an outpost for manned missions to other planets. Safety and resilience of those habitats are the main concerns, especially given the existing dangerous conditions and hazards such as temperature fluctuation, radiation, seismic activity, and meteorite impacts. Underground habitats in the form of “lava tubes” are good candidates for permanent human shelters because they provide immediate protection from such hazards. Evidence for their existence under the surface of the Moon is provided by GRAIL, SELENE spacecraft, and the LRO. Data from GRAIL suggests that the width of the lava tubes can be as large as 1–2 km. However, the size and geometry configurations under which the lava tubes are stable are not well-defined and there are no well-established criteria for their stability. The paper provides analytical and numerical solutions to estimate the size and stability of lunar lava tubes. Analytical solutions use the flow characteristics of molten lava and the limit equilibrium analysis of the collapsed pits to estimate the size of the tubes. Numerical analyses are conducted for different geometries and for different material properties of the lava. Different sizes are considered, from 300 m, up to 4000 m in width. The results show that the key parameters for the stability of the tubes are the roof thicknesses and the strength of the lava, in particular, its tensile strength. As the lava tube gets wider, a larger tensile strength and roof thicknesses are needed for the tube to remain stable. The analyses show that 1 km wide lava tubes are likely to exist and remain stable, which agree with GRAIL observations. For larger widths, minimum roof thicknesses of few hundred meters might be necessary to preserve their stability.

Changes occur gradually in a structure over its lifecycle. Periodic inspection of each structure is needed to prevent sudden failures and determine repair priorities. Human observation, the predominant mechanism for such assessments, is time-consuming and expensive, and often lacks consistency. Crowdsourcing provides a new opportunity to gather numerous photos of certain structures from various viewpoints and at frequent intervals, potentially enabling remote visual assessment. In this study, we exploit state-of-art computer vision techniques to streamline structural inspection and support lifecycle assessment by using visual data collected from ordinary citizens. One major inherent challenge in the use of such data is that they include a significant amount of irrelevant information because they are not captured intended for inspection purposes. To address this challenge, we develop an automated method to filter out unnecessary portions of the images and extract highly relevant regions-of-interest for reliable inspection. The technical approach is demonstrated using a regional landmark structure, the Bell Tower in the Purdue University in the United States. A large volume of images is collected in a crowdsourcing manner in six periods over two years. Then, the method successfully localizes the crowdsourcing images and extracts the image areas corresponding to three target inspection regions in the structure.

Wireless sensor networks (WSNs) have emerged as a novel solution to many of the challenges of structural health monitoring (SHM) in civil engineering structures. While research projects using WSNs are ongoing worldwide, implementations of WSNs on full-scale structures are limited. In this study, a WSN is deployed on a full-scale 17.3m-long, 11-bay highway sign support structure to investigate the ability to use vibration response data to detect damage induced in the structure. A multi-level damage detection strategy is employed for this structure: the Angle-between-String-and-Horizon (ASH) flexibility-based algorithm as the Level I and the Axial Strain (AS) flexibility-based algorithm as the Level II. For the proposed multi-level damage detection strategy, a coarse resolution Level I damage detection will be conducted first to detect the damaged region(s). Subsequently, a fine resolution Level II damage detection will be conducted in the damaged region(s) to locate the damaged element(s). Several damage cases are created on the full-scale highway sign support structure to validate the multi-level detection strategy. The multi-level damage detection strategy is shown to be successful in detecting damage in the structure in these cases.

The initiation and propagation of damage such as cracks in an engineering structure under dynamic loadings is a nonlinear process. Strictly speaking, conventional eigenvalue and eigenvector extraction-based damage identification approaches are suitable for linear systems only. Due to the unique nonlinearities associated with each civil engineering structure, it would be inefficient to attempt to express the nonlinear restoring force (NRF) of an engineering structure such as a reinforced concrete structure in a parametric form. Consequently, it is highly desirable to develop a general nonlinear identification approach to achieve structural damage detection in both qualitative and quantitative ways without the assumption on the parametric model of the hysteretic behavior. In this paper, based on a double Chebyshev polynomial function, a time-domain identification approach is proposed for identifying both the structural NRF and the mass distribution for multi-degree-of-freedom (MDOF) structures under incomplete dynamic loadings. As a typical nonlinear material, shape memory alloy (SMA) is introduced to a MDOF structural model to mimic the nonlinear behavior under dynamic loadings. The feasibility and robustness of the proposed approach is validated via (1) a numerical MDOF structure model equipped with a SMA damper whose restoring force is described by a double-flag-shaped nonlinear model, and (2) an experimental study on a four-story shear building structure model equipped with an in-house design of a SMA damper used to mimic the structural NRF under incomplete impact loadings. The identified NRF is compared with the theoretical values and the measurements in experiment. Both numerical and experimental results show that the proposed approach is capable of identifying both the mass distribution and structural nonlinearity under dynamic loadings, and could be potentially used for damage initiation and propagation monitoring during vibration of engineering structures under dynamic excitations.

Summary 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. Copyright © 2015 John Wiley & Sons, Ltd.

The visual inspection of bridges demands long inspection time and also makes it difficult to access all areas of the bridge. This paper presents a visual-based crack detection technique for the automatic inspection of bridges. The technique collects images from an aerial camera to identify the presence of damage to the structure. The images are captured without controlling angles or positioning of cameras so there is no need for calibration. This allows the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors. The images can detect cracks regardless of the size or the possibility of not being visible. The effectiveness of this technique can be used to successfully detect cracks near bolts.

Hybrid simulation is increasingly being recognized as a powerful technique for laboratory testing. It offers the opportunity for global system evaluation of civil infrastructure systems subject to extreme dynamic loading, often with a significant reduction in time and cost. In this approach, a reference structure/system is partitioned into two or more substructures. The portion of the structural system designated as \'physical\' or \'experimental\' is tested in the laboratory, while other portions are replaced with a computational model. Many researchers have quite effectively used hybrid simulation (HS) and real-time hybrid simulation (RTHS) methods for examination and verification of existing and new design concepts and proposed structural systems or devices. This paper provides a detailed perspective of the enabling role that HS and RTHS methods have played in advancing the practice of earthquake engineering. Herein, our focus is on investigations related to earthquake engineering, those with CURATED data available in their entirety in the NEES Data Repository.

In research on damage identification, conventional methods usually face difficulties in converging globally and rapidly. Therefore, a fast in-time damage identification approach based on the Kalman filter and energy equilibrium theory is proposed to obtain the structural stiffness, find the locations of damage, and quantify its intensity. The proposed approach establishes a relationship between the structural stiffness and acceleration response by means of energy equilibrium theory. After importing the structural energy into the Kalman filter algorithm, unknown parameters of the structure can be obtained by comparing the predicted energy and the measured energy in each time step. Numerical verification on a highway sign support truss with and without damage indicates that the updated Young’s modulus can converge to the true value rapidly, even under the effects of external noise excitation. In addition, the calculation time taken for each step of the approach is considerably shorter than the sampling period (1/256 s), which means that, this approach can be implemented in-time and on-line.

Visual assessment is a process to understand the state of a structure based on evaluations originating from visual data. Low-cost, high-performance vision sensors are providing new avenues for overcoming the spatial and temporal limitation in current human-based visual assessment when used in conjunction with aerial sensing platforms. However, past implementations are limited in their ability to deal with a high volume of images while only a small fraction of them are important for actual inspection. Such difficulty induces an unwanted high rate of false-positive and negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome this challenge, we develop and validate a novel automated image localization and classification technique to extract regions-of-interest (ROIs) on each of images, which contain the target region of the structure for visual evaluation (TRI). First, ROIs are extracted based on the geometric relationship between the collected images and the TRIs using structure-from-motion algorithm. Second, unwanted ROIs corrupted by occlusion and image blur are effectively filtered by a robust image classification technique, called convolutional neural network. Then, a damage detection technique is applied only on such highly relevant and localized ROI images. The capability of the technique is demonstrated using a full-scale highway sign structure for the case of crack detection on weld connections.

Real-time hybrid simulation (RTHS) is a promising cyber-physical technique used in the experimental evaluation of civil infrastructure systems subject to dynamic loading. In RTHS, the response of a structural system is simulated by partitioning it into physical and numerical substructures, and coupling at the interface is achieved by enforcing equilibrium and compatibility in real-time. The choice of partitioning parameters will influence the overall success of the experiment. In addition, due to the dynamics of the transfer system, communication and computation delays, the feedback force signals are dependent on the system state subject to delay. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In light of this, guidelines should be established to facilitate successful RTHS and clearly specify: (i) the minimum requirements of the transfer system control, (ii) the minimum required sampling frequency, and (iii) the most effective ways to stabilize an unstable simulation due to the limitations of the available transfer system. The objective of this paper is to establish a stability switch criterion due to systematic experimental errors. The RTHS stability switch criterion will provide a basis for the partitioning and design of successful RTHS.

The objective of this paper is to showcase the progress of the earthquake engineering community during a decade-long effort supported by the National Science Foundation in the George E. Brown Jr., Network for Earthquake Engineering Simulation (NEES). During the four years that NEES network operations have been headquartered at Purdue University, the NEEScomm management team has facilitated an unprecedented cultural change in the ways research is performed in earthquake engineering. NEES has not only played a major role in advancing the cyberinfrastructure required for transformative engineering research, but NEES research outcomes are making an impact by contributing to safer structures throughout the USA and abroad. This paper reflects on some of the developments and initiatives that helped instil change in the ways that the earthquake engineering and tsunami community share and reuse data and collaborate in general.

Summary In this paper, we propose a new actuator control algorithm that achieves the design flexibility, robustness, and tracking accuracy to give real-time hybrid-simulation users the power to achieve highly accurate and robust actuator control. The robust integrated actuator control (RIAC) strategy integrates three key control components: loop shaping feedback control based on H∞ optimization, a linear-quadratic-estimation block for minimizing noise effect, and a feed-forward block that reduces small residual delay/lag. The combination of these components provides flexible controller design to accommodate setup limits while preserving the stability of the H∞ algorithm. The efficacy of the proposed strategy is demonstrated through two illustrative case studies: one using large capacity but relatively slow actuator of 2500 kN and the second using a small-scale fast actuator. Actuator tracking results in both cases demonstrate that the RIAC algorithm is effective and applicable for different setups. Real-time hybrid-simulation validation is implemented using a three-DOF building frame equipped with a magneto-rheological damper on both setups. Results using the two very different physical setups illustrate that RIAC is efficient and accurate. Copyright © 2014 John Wiley & Sons, Ltd.

In a typical Real-time Hybrid Simulation (RTHS) setup, servo-hydraulic actuators serve as interfaces between the computational and physical substructures. Time delay introduced by actuator dynamics and complex interaction between the actuators and the specimen has detrimental effects on the stability and accuracy of RTHS. Therefore, a good understanding of servo-hydraulic actuator dynamics is a prerequisite for controller design and computational simulation of RTHS. This paper presents an easy-to-use parametric identification procedure for RTHS users to obtain re-useable actuator parameters for a range of payloads. The critical parameters in a linearized servo-hydraulic actuator model are optimally obtained from genetic algorithms (GA) based on experimental data collected from various specimen mass/stiffness combinations loaded to the target actuator. The actuator parameters demonstrate convincing convergence trend in GA. A key feature of this parametric modeling procedure is its re-usability under different testing scenarios, including different specimen mechanical properties and actuator inner-loop control gains. The models match well with experimental results. The benefit of the proposed parametric identification procedure has been demonstrated by (1) designing an H ∞ controller with the identified system parameters that significantly improves RTHS performance; and (2) establishing an analysis and computational simulation of a servo-hydraulic system that help researchers interpret system instability and improve design of experiments.

SUMMARY Real-time hybrid simulation (RTHS) is increasingly being recognized as a powerful cyber-physical technique that offers the opportunity for system evaluation of civil structures subject to extreme dynamic loading. Advances in this field are enabling researchers to evaluate new structural components/systems in cost-effective and efficient ways, under more realistic conditions. For RTHS, performance metric clearly needs to be developed to predict and evaluate the accuracy of various partitioning choices while incorporating the dynamics of the transfer system, and computational/communication delays. In addition, because of the dynamics of the transfer system, communication delays, and computation delays, the RTHS equilibrium force at the interface between numerical and physical substructures is subject to phase discrepancy. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In this paper, a new performance indicator, predictive performance indicator (PPI), is proposed to assess the sensitivity of an RTHS configuration to any phase discrepancy resulting from transfer system dynamics and computational/communication delays. The predictive performance indicator provides a structural engineer with two sets of information as follows: (i) in the absence of a reference response, what is the level of fidelity of the RTHS response? and (ii) if needed, what partitioning adjustments can be made to effectively enhance the fidelity of the response? Moreover, along with the RTHS stability switch criterion, this performance metric may be used as an acceptance criteria for conducting single-degree-of-freedom RTHS. Copyright © 2014 John Wiley & Sons, Ltd.

Our deteriorating civil infrastructure faces the critical challenge of long-term structural health monitoring for damage detection and localization. In contrast to existing research that often separates the designs of wireless sensor networks and structural engineering algorithms, this paper proposes a cyber-physical codesign approach to structural health monitoring based on wireless sensor networks. Our approach closely integrates 1) flexibility-based damage localization methods that allow a tradeoff between the number of sensors and the resolution of damage localization, and 2) an energy-efficient, multilevel computing architecture specifically designed to leverage the multiresolution feature of the flexibility-based approach. The proposed approach has been implemented on the Intel Imote2 platform. Experiments on a simulated truss structure and a real full-scale truss structure demonstrate the system's efficacy in damage localization and energy efficiency.

This paper presents comparisons of the performances of three semiactive control algorithms for use with multiple magnetorheological (MR) dampers. The three controllers are (1) the clipped-optimal controller, (2) the decentralized output feedback polynomial controller, and (3) the simple passive controller. These controllers use different types of inputs to calculate control signals for the MR dampers, based on different control mechanisms. To investigate the advantages of each controller, a three-degree-of-freedom steel moment-resisting frame, designed using a performance-based design methodology, was developed. The performance was investigated by using four different earthquakes utilized during the design of the building frame. Comparisons of the controllers’ performance were carried out in terms of reduction in the maximum interstory drifts, displacements, absolute accelerations, and control forces. Real-time hybrid tests were carried out to validate these comparisons.

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.

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.

Currently, construction tendencies tend to design flexible structures, such as footbridges, slabs and grandstands, due to the increased resistance of new materials and the regulation gap in building codes. These flexible structures are susceptible of excessive vibrations induced by human activities, producing serviceability failures and, sometimes, collapse. It is essential to improve the understanding of the effect of anthropic loads over the structures to mitigate serviceability problems and the risk of structural damage and, therefore, economic and human loses.

This study investigated the safety and stability of light-rail trains that run on multispan simply supported bridges (MSSBs) and have experienced some settling and girder deformation caused by both foundation settlement and girder deflection/camber. A three-dimensional dynamic nonlinear finite-element model for a light-rail train track–MSSB interaction system, which took into account rail irregularities, girder deformation, wheel–rail contact, and coupling effects, was developed. A case study analyzed a four-car light-rail train rolling on a MSSB, and the relationships between the girder deformation and running performance under various speeds were obtained. The results indicate that speed is the dominant factor that contributes to the safety and stability of the light-rail train when girder deformation is present. In addition, the influence of girder deformation was found to be greater on safety than on stability. In real applications, it is recommended to pursue advanced monitoring and evaluatio...

Scientific research has experienced a transformation over the last several decades. To tackle today’s grand challenges in science and engineering, researchers today must leverage and integrate the skills and knowledge of team members with computational resources and infrastructure that include data, computing, communications, and high performance computing resources.

Summary Structural control systems based on wireless sensors offer a convenient, flexible and cost-effective alternative to their wired counterparts. Although wireless control systems (WCSs) have several attractive features, some challenges do remain related to the persistent presence of network-induced time delays, and potential for sensor data losses and in extreme cases, sensor failures. The consequences of these challenges should be investigated, and solutions should be developed to achieve highly effective and robust control systems. The availability of such solutions will also encourage the adoption of WCSs in real structures. Here, an estimator switching method intended to minimize the influence of potential faults is developed and validated for WCSs. In this method, the switching gains are pre-calculated to enable real-time implementation. The proposed method is verified through numerical simulations of a seismically excited, three-story structure considering various sensor data loss and sensor failure scenarios. The robustness of this estimation method in the presence of measurement noise and modeling uncertainty is also investigated. In addition, the estimation switching method is incorporated into a closed-loop WCS in experiment. The results demonstrate the effectiveness of the proposed state estimation method in mitigating the impact of sensor data loss and sensor failure. Copyright © 2016 John Wiley & Sons, Ltd.

Wireless sensor networks (WSNs) have become an increasingly compelling platform for structural health monitoring (SHM) applications, since they can be installed relatively inexpensively onto existing infrastructure. Existing approaches to SHM in WSNs typically address computing system issues or structural engineering techniques, but not both in conjunction. In this paper, we propose a holistic approach to SHM that integrates a decentralized computing architecture with the damage localization assurance criterion algorithm. In contrast to centralized approaches that require transporting large amounts of sensor data to a base station, our system pushes the execution of portions of the damage localization algorithm onto the sensor nodes, reducing communication costs by an order of magnitude in exchange for moderate additional processing on each sensor. We present a prototype implementation of this system built using the TinyOS operating system running on the Intel Imote2 sensor network platform. Experiments conducted using two different physical structures demonstrate our system's ability to accurately localize structural damage. We also demonstrate that our decentralized approach reduces latency by 64.8% and energy consumption by 69.5% compared to a typical centralized solution, achieving a projected lifetime of 191 days using three standard AAA batteries. Our work demonstrates the advantages of a holistic approach to cyber-physical systems that closely integrates the design of computing systems and physical engineering techniques.

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.

SUMMARY Real-time hybrid simulation (RTHS) has increasingly been recognized as a powerful methodology to evaluate structural components and systems under realistic operating conditions. It is a cost effective approach compared with large scale shake table testing. Furthermore, it can maximally preserve rate dependency and nonlinear characteristics of physically tested (non)structural components. Although conceptually very attractive, challenges do exist that require comprehensive validation before RTHS should be employed to assess complicated physical phenomena. One of the most important issues that governs the stability and accuracy of an RTHS is the ability to achieve synchronization of boundary conditions between the computational and physical substructures. The objective of this study is to propose and validate an H∞ loop shaping design for actuator motion control in RTHS. Controller performance is evaluated in the laboratory using a worst-case substructure proportioning scheme. A modular, one-bay, one-story steel moment resisting frame specimen is tested experimentally. Its deformation is kept within the linear range for ready comparison with the reference closed-form solution. Both system analysis and experimental results show that the proposed H∞ strategy can significantly improve both the stability limit and test accuracy compared with several existing strategies. Another key feature of the proposed strategy is its robust performance in terms of unmodeled dynamics and uncertainties, which inevitably exist in any physical system. This feature is essential to enhance test quality for specimens with nonlinear dynamic behavior, thus ensuring the validity of the proposed approach for more complex RTHS implementations. Copyright © 2012 John Wiley & Sons, Ltd.

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 [32] 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.

System identification is becoming more important in structural dynamic applications including structural model update, damage detection primarily due to the rapid increase in the number of damaged or deteriorated engineering structures, and load identification for remaining service life forecasting. Time-domain identification techniques based on measured vibration data, e.g. the least-squares estimation (LSE), have been studied for a relatively long time and shown to be useful. However, the traditional least-squares techniques require that all the external excitation measurements should be available, which may not be the case for many practical applications. In this paper, by introducing a weighted positive definite matrix to the objective function and a learning coefficient using the information in the previous iterations in the identification approach to improve the convergence performance, an alternative iterative approach for both structural parameters and dynamic loading identification, referred to as weighted adaptive iterative least-squares estimation with incomplete measured excitations (WAILSE-IME), was proposed. The accuracy, convergence, and robustness of the proposed approach was demonstrated via numerical simulation on a six-story shear building model with noise-free and different levels of noise-polluted structural dynamic response measurements. The effects of the positive weight matrix, the learning coefficient, and the sampling duration on the convergence and the accuracy of the proposed approach were discussed and the results were compared with available related literature results. Results show that the proposed approach can simultaneously identify structural parameters and unknown excitations within very limited iterations with high accuracy and shows its robustness even noise-polluted dynamic response measurements are utilized. Furthermore, the approach was validated via available experimental measurements for a four-story frame structure excited by impact excitations. The results show that the proposed approach is capable of identifying both structural parameters and the unmeasured excitations with acceptable accuracy and improved convergence.

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.