Search

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.

Fractional-order derivative (FD) models are widely used to describe the dynamic behavior of rubber-like materials. However, experiments have shown that FD models cannot reproduce the amplit...

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.

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.

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.

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 ...

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.

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.

espanolIntroduccion: En este articulo se describe la simulacion hibrida en tiempo real (RTHS) de un amortiguador no lineal de masa sintonizado (NTMD) y se comparan los resultados con los obtenidos de ensayos experimentales convencionales de una estructura a cortante, de un piso, con el NTMD. Objetivo: El objetivo de este articulo es valuar la efectividad de una RTHS para estimar el desempeno de un NTMD. Metodologia: La metodologia consistio de las siguientes tres etapas: identificacion de la estructura principal, diseno del NTMD y evaluacion experimental del sistema estructura-NTMD. Para la tercera etapa, se utilizaron RTHS y ensayos sobre mesa vibratoria. Resultados: Los resultados de los ensayos en mesa vibratoria demostraron que el NTMD redujo el 77% y 63% de las aceleraciones pico y RMS de la estructura principal, con respecto a la estructura sin control. Los valores de estas reducciones obtenidos con RTHS fueron 73% y 63%, respectivamente. Los indices de precision del sistema de transferencia correspondieron a una amplitud generalizada de 1.01 y un retraso de 2 ms. Conclusiones: el NTMD, con una razon de masas del 10%, alcanzo reducciones superiores al 60% de la respuesta estructural. La RTHS y el ensayo de mesa vibratoria demostraron que el sistema estructura-NTMD tuvo solo un pico en la respuesta en frecuencia. El ruido en la retroalimentacion de la RTHS aumento el grado de amortiguamiento de la estructura controlada. Finalmente, los resultados experimentales demostraron que la RTHS es una tecnica que predice efectivamente la aceleracion RMS del sistema estructura-NTMD y puede sobreestimar ligeramente su aceleracion pico. EnglishIntroduction: In this paper, the real-time hybrid simulation (RTHS) of a non-linear tuned mass damper (NTMD) is described, and the results are compared with those obtained from conventional experimental tests of a one-story shear frame structure with the NTMD. Objective: The aim of this article is to evaluate the effectiveness of a RTHS for the estimation of the performance of a NTMD. Method: the methodology consisted of the following three stages: main structure identification, NTMD design, and experimental evaluation of the structure-NTMD system. For the third stage, both real-time hybrid simulations and shaking table testing were conducted. Results: results from shaking table tests demonstrate that the NTMD reduced the peak and RMS accelerations of the main structure 77% and 63% respectively, with respect to the structure without control. The values of these reductions obtained with RTHS were 73% and 63%, respectively. The assessment indices of the transfer system correspond to a generalized amplitude of 1.01 and a delay of 2 ms. Conclusions: the NTMD, with a 10% mass ratio, achieved reductions greater than 60% in the acceleration response of the structure. The RTHS and shaking table test indicated that the structure-NTMD system had only one peak in the frequency response. Noise in RTHS feedback increased the damping level of the controlled structure. Finally, experimental results showed that the RTHS is a technique that predicts effectively the RMS acceleration of the structure-NTMD system, and it can slightly overestimate its peak acceleration.

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.

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.

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...

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.

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.

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.

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.

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 ...

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.