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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.

Pedestrian structures regularly exhibit excessive vibrations when they are dynamically excited by human activities. Traditional modeling approaches neglect, due to the lack of suitable data, the temporal and spatial kinematic changes (such as the step length, step width, pace frequency and horizontal speed) when pedestrians are walking on a vibrating surface in the vertical direction such as slabs, footbridges, or stairs. Although several experimental studies have quantified the variability of human gait, most of these were conducted overground. These procedures do not fully account for the interaction between the pedestrian and an oscillating surface, compromising the comfort in service conditions for their occupants. In this paper, the spatio-temporal gait parameters were measured when six adults were instructed to walk on the beam and on the ground. The kinematic parameters were compared among the individual subjects to evaluate their variability associated with the vertical acceleration influence. Two gait modes were examined in this study, metronomic walk and self-selected pace. The results show that the walking locomotion parameters such as step length, step width, pace frequency, and pedestrian horizontal speed were found to have substantial differences on the ground as compared with the structural vibration under different walking modes. The findings here clearly demonstrate that gait and bridge kinematics interact, and continued research is warranted with larger numbers of participants under a wider range of structural conditions.

Control is a critical element in many applications and research such as experimental testing in real-time. Linear approaches for control and estimation have been widely applied to real-time hybrid simulation (RTHS) techniques in tracking the physical domain (plant). However, nonlinearities and highly uncertainties of the plant impose challenges that must be properly addressed using nonlinear control procedures. In this study, a controller is developed for such an uncertain nonlinear system by integrating a robust control approach with a nonlinear Bayesian estimator. A sliding mode control methodology synthesizes the nonlinear control law to provide stability and accurate tracking performance, and a particle filter algorithm estimates the full state of the plant using measured signals such as displacement. The Hybrid Simulation Management (HSM) code is developed to implement dynamic systems and the improved nonlinear robust controller. The HSM is integrated in a novel run-time substrate named CyberMech, which is a platform developed to enhance the performance of real-time cyber-physical experiments that supports parallel execution. A set of experiments with a highly uncertain nonlinear dynamic system demonstrates that the combination of advanced control techniques and high performance computation enhances the quality of real-time experimentation and potentially expands RTHS techniques capabilities.

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.

Bayesian inference techniques have been used extensively in recent years for parameter estimation in nonlinear systems. Despite the many advances made in the field, highly nonlinear systems can still be challenging to identify. Of key interest is the challenge in establishing the identifiability of the model with respect to various excitation signals and, in particular, doing so prior to the collection of experimental data. Global sensitivity analysis techniques provide a perspective on this problem that is well-suited to informing the design of identification experiments for use with Bayesian inference techniques. These methods quantify the relative importance of the parameters to the model response by decomposing the variance of the response into contributions from the respective parameters. The sensitivities obtained provide a valuable indication of the information available for parameter estimation in the response of a system to a particular excitation. In this study, nonlinear model parameters are identified based on experimental responses from a nonlinear energy sink device with the unscented Kalman filter. The experimental identification results are compared with those of a Sobol’ sensitivity analysis on the system model to demonstrate how global sensitivity analysis can be used as a method to preselect experimental excitations for use with Bayesian inference techniques.

An Earth-independent permanent extraterrestrial habitat system must function as intended under continuous disruptive conditions, and with significantly limited Earth support and extended uncrewed periods. Designing for the demands that extreme environments such as wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts (direct or indirect), vibrations, and solar particle events, will place on long-term deep space habitats represents one of the greatest challenges in this endeavor. This context necessitates that we establish the know-how and technologies to build habitat systems that are resilient. Resilience is not simply robustness or redundancy: it is a system property that accounts for both anticipated and unanticipated disruptions via the design choices and maintenance processes, and adapts to them in operation. We currently lack the frameworks and technologies needed to achieve a high level of resilience in a habitat system. The Resilient ExtraTerrestrial Habitats Institute (RETHi) has the mission of leveraging existing novel technologies to provide situational awareness and autonomy to enable the design of habitats that are able to adapt, absorb and rapidly recover from expected and unexpected disruptions. We are establishing both fully virtual and coupled physical-virtual simulation capabilities that will enable us to explore a wide range of potential deep space SmartHab configurations and operating modes.

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.

Currently, the lack of (1) a sufficiently integrated, adaptive, and reflective framework to ensure the safety, integrity, and coordinated evolution of a real-time hybrid simulation (RTHS) as it runs, and (2) the ability to articulate and gauge suitable measures of the performance and integrity of an experiment, both as it runs and post-hoc, have prevented researchers from tackling a wide range of complex research problems of vital national interest. To address these limitations of the current state-of-the-art, we propose a framework named Reflective Framework for Performance Management (REFORM) of real-time hybrid simulation. REFORM will support the execution of more complex RTHS experiments than can be conducted today, and will allow them to be configured rapidly, performed safely, and analyzed thoroughly. This study provides a description of the building blocks associated with the first phase of this development (REFORM-I). REFORM-I is verified and demonstrated through application to an expanded version of the benchmark control problem for real-time hybrid simulation.

Elastic scheduling allows for online adaptation of real-time tasks' utilizations (via manipulation of each task's computational workload or period) in order to maintain system schedulability in case the utilization demand of one or more tasks changes. This is done currently by assigning each task a utilization (and therefore period or workload) from within a continuous range of acceptable values. While this works well for anytime tasks whose quality of service improves with duration or for tasks that can run at any rate within a given range, many computationally-elastic tasks have a specific workload for each distinct mode of operation and therefore cannot perform arbitrary amounts of work. Similarly, some period-elastic tasks must run at specific (e.g. harmonic) rates. Therefore, a discrete set of candidate utilizations per task must be accommodated in such cases. This paper provides a new elastic task model in which each task has a discrete set of possible utilizations (instead of a continuous range). This allows users to specify only relevant candidate periods and workloads for each task. The discrete nature of this model also allows each task to modify its workload and/or its period when changing its mode of operation, instead of adapting in only one dimension of task utilization. Elastic tasks thus can exploit both period elasticity and computational elasticity. This greatly increases both the diversity of adaptations available to each task and the kinds of real-time tasks relevant to elastic scheduling. We use the real-world example of real-time hybrid simulation as a motivating application domain with discretely computationally-elastic, period-elastic, and combined-elastic parallel real-time tasks under the Federated Scheduling paradigm. We prove the scheduling of these tasks to be NP-hard, and provide a pseudo-polynomial time scheduling algorithm. We then use this scheduling algorithm to implement the first virtual real-time hybrid simulation experiment in which discrete elastic adaptation of platform resource utilizations enables adaptive switching between controllers with differing computational demands. We also study the effects of scheduling tasks with discretized vs. continuous candidate utilizations.

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 ...

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.

Complementary advances in computer vision and new sensing platforms have mobilized the research community to pursue automated methods for vision-based visual evaluation of our civil infrastructure. Spatial and temporal limitations typically associated with sensing in large-scale structures are being torn down through the use of low-cost aerial platforms with integrated high-resolution visual sensors. Despite the enormous efforts expended to implement such technology, practical real-world challenges still hinder the application of these methods. The large volumes of complex visual data, collected under uncontrolled circumstances (e.g. varied lighting, cluttered regions, occlusions, and variations in environmental conditions), impose a major challenge to such methods, especially when only a tiny fraction of them are used for conducting the actual assessment. Such difficulties induce undesirable high rates of false-positive and false-negative errors, reducing both trustworthiness and efficiency in the methods. To overcome these inherent challenges, a novel automated image localization and classification technique is developed to extract the regions of interest on each of the images, which contain the targeted region for inspection. Regions of interest are extracted here using structure-from-motion algorithm. Less useful regions of interest, such as those corrupted by occlusions, are then filtered effectively using a robust image classification technique, based on convolutional neural networks. Then, such highly relevant regions of interest are available for visual assessment. The capability of the technique is successfully demonstrated using a full-scale highway sign truss with welded connections.

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.

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.

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.

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.

Nonlinear energy sink (NES) devices have recently been introduced in civil engineering for structural control. Because of the essential geometric nonlinearities governing these devices, identification must be performed in the time domain. Such methods can be challenging due to processing requirements, sensitivity to noise, and the presence of nonlinearity. Bayesian analysis methods have been shown to overcome these challenges, providing robust identification of nonlinear models. In this study we compare the unscented Kalman filter and the particle filter for the identification of a prototype NES device. Simulated responses developed using a device model and a sample set of parameters are used here to demonstrate and evaluate the identification process. Analysis of the identification results is conducted by varying the identification technique used and the selection of the prior distributions on the parameters. These preliminary numerical results will inform a later implementation on experimental response data.

Reconnaissance teams are charged with collecting perishable data after a natural disaster. In the field, these engineers typically record their observations through images. Each team takes ...

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.

Real-time hybrid simulation (RTHS) is a novel cyber-physical testing technique for investigating especially large or complicated structural systems. Controllers are required to compensate for the dynamics of transfer systems that emulate the interactions between the physical and numerical substructures. Thus, both the stability and accuracy of RTHS testing highly depend on the effectiveness of the control strategy. This paper proposes a robust sliding mode controller (SMC) as a transfer system control strategy in RTHS. A design procedure of the SMC control strategy is presented. The benchmark problem on RTHS control is utilized for demonstration and validation of SMC for this class of problems. Virtual RTHS results show that the SMC strategy significantly improves the performance and robustness of RTHS testing.

Summary Real-time hybrid simulation (RTHS) is an effective and versatile tool for the examination of complex structural systems with rate dependent behaviors. To meet the objectives of such a test, appropriate consideration must be given to the partitioning of the system into physical and computational portions (i.e., the configuration of the RTHS). Predictive stability and performance indicators (PSI and PPI) were initially established for use with only single degree-of-freedom systems. These indicators allow researchers to plan a RTHS, to quantitatively examine the impact of partitioning choices on stability and performance, and to assess the sensitivity of an RTHS configuration to de-synchronization at the interface. In this study, PSI is extended to any linear multi-degree-of-freedom (MDOF) system. The PSI is obtained analytically and it is independent of the transfer system and controller dynamics, providing a relatively easy and extremely useful method to examine many partitioning choices. A novel matrix method is adopted to convert a delay differential equation to a generalized eigenvalue problem using a set of vectorization mappings, and then to analytically solve the delay differential equations in a computationally efficient way. Through two illustrative examples, the PSI is demonstrated and validated. Validation of the MDOF PSI also includes comparisons to a MDOF dynamic model that includes realistic models of the hydraulic actuators and the control-structure interaction effects. Results demonstrate that the proposed PSI can be used as an effective design tool for conducting successful RTHS. Copyright © 2016 John Wiley & Sons, Ltd.

Fragility functions enable the assessment of a structural system for a given hazard scenario. Specifically, the fragility function provides the probability of an undesirable structural state conditioned on the occurrence of a specific hazard level. Multiple sources of uncertainty are present when estimating fragility functions, e.g., record-to-record variation, uncertain material and geometric properties, model assumptions, and limited data to characterize the hazard. The objective of this study is to develop a methodology that will accelerate the process of fragility function estimation under limitations in computational resources and data. The approach used in the methodology is as follows. The stochastic map between hazard level and structural response is first constructed using Bayesian inference for a finite number of simulations. The Bayesian approach enables the quantification of the epistemic uncertainty due to a limited number of simulations. This epistemic uncertainty is exploited to sequentially select subsequent simulations that accelerate learning based on up to two different earthquake intensity measures, peak ground velocity and spectral velocity. The methodology is applied to a benchmark model of a twenty-story nonlinear building. Simulations are performed using a set of synthetic ground motions obtained from scenario earthquakes in California. Through this case study the methodology developed here is demonstrated. Additionally, the case study highlights the ability of the methodology to achieve lower levels of epistemic uncertainty than traditional techniques using the same number of simulations. This approach is expected to enable more efficient fragility function determination.

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.

Identifying damage initiation and development in engineering structures non-parametrically in the form of a nonlinear restoring force (NRF) after strong dynamic loading is attractive. Due to the individuality of various engineering structures, it is quite challenging to assume, in advance, a general parametric model describing the nonlinear behavior. Although a traditional extended Kalman filter (EKF) is efficient in state vector estimation and structural parameter identification with partially available output measurements, a known structural mass is usually required. In this study, a simultaneous NRF and mass identification approach is developed for multi-degree-of-freedom (MDOF) structures using the EKF with weighted global iteration (EKF-WGI) based on limited available absolute acceleration response. The NRF is modeled in a nonparametric way with a power series polynomial model (PSPM) as a function of unknown structural displacement and velocity responses. Then, the performance of the new approach is numerically evaluated using multi-story structures equipped with magneto-rheological (MR) dampers having known applied excitations and partially available noise-contaminated acceleration measurements, but unknown mass. No parametric model for the NRF of the MR dampers is employed. The effect of different noise levels and different initial estimation errors of structural mass on both NRF and mass identification results and the convergence of the approach are investigated. Finally, a dynamic test on a four-story frame structure equipped with an MR damper is carried out and the algorithm is experimentally validated. Comparisons show that the identified NRF provided by the MR damper matches the measurement and that the identified mass is also accurate.

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.

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.

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.

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 ...

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.

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 ...

This paper presents the problem definition and guidelines for a benchmark control problem in real-time hybrid simulation for a seismically excited building, to appear in a Special Issue of Mechanical Systems and Signal Processing. 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. The focus here is on the design of an effective transfer system displacement tracking controller which is a commonly used approach for ensuring that interface conditions between numerical and experimental substructures are satisfied. In this study, a laboratory model of a three-story steel frame is considered as the reference structure. Realistic numerical models are developed and provided to represent the numerical and experimental substructures and the transfer system, which is comprised of hydraulic actuation, sensing instrumentation, and control implementation hardware. Experimental components are identified and provided as Simulink models, which are executed in real-time using Simulink Desktop Real-Time capability to enable realistic virtual real-time hybrid simulation. The task of each participant of the Special Issue is to design, evaluate, and report on their proposed controller approaches using the numerical models and computational codes provided. Such approaches will be assessed for robustness and performance using the provided tools. This benchmark problem is expected to further the understanding of the relative merits, as well as provide a clear basis for evaluating the performance of various control approaches and algorithms for RTHS. To illustrate some of the design challenges, a sample control strategy employing a proportional-integral (PI) controller is included, in addition to the built-in control loop of the transfer system.

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.

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.

Prior to each payment to contractors and suppliers, measurements are made to document the actual amount of pay items placed at the site. This manual process has substantial risk for personnel, and could be made more efficient and less prone to human errors. In this project a customized software tool package has been developed to address these concerns. The Pay Item Measurement (PIM) tool package is provided as two complementary tools, the Orthophoto Generation Tool and the Graphical Measurement Tool. PIM has been developed in close cooperation with the advisory committee and field engineers from INDOT, and is specifically designed to incorporate the typical actions that INDOT personnel follow. PIM is intended to generate orthophotos for measurements on a planar surface. User guidelines explain the process of how to collect suitable high-quality images, which is a critical step for successful orthophoto construction. INDOT will also use PIM to identify features and make annotations, and to readily compute distances, perimeters, and areas for documenting and recording. This customized tool package will be most useful, and accurate, when the user guidelines are followed. Several examples are included to demonstrate the characteristics of high-quality image sets that will be successful, and to also provide examples of sets that would fail. Step-by-step instructions are provided to demonstrate the correct use of the tool. An instructional video and sample digital image sets complement this report and tool package.

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.

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.