1,721,049 research outputs found
Monitoring Technologies for Smart Cities and Civil Infrastructure Systems
No full textThe proportion of urban population in the world is expected to increase from 54% currently to 70% by 2050. A majority of Americans also reside in urban regions - according to the 2010 census 80% of Americans reside in urban areas. Given the large number of urban citizens in the world (and US) it is imperative that we identify solutions to improve the quality of life for urban residents and economic vitality of our cities. Studies to address and fulfill the needs of envisioned future smart city infrastructure should successfully integrate a range of engineering, humanities and sociological fields such as emerging communication technologies, Internet of Things (IoT), cyber security, cloud computing, intelligent transportation, infrastructure monitoring, analyzing tourism, theorizing structures of government and bureaucracy, project financing, public policy development and implementation. In this talk, we will first three overarching themes: (1) technologically advanced infrastructure with sensing and communication capability, (2) urban operations and services improved with better decisions using multilayered “big data”, and (3) utilization of technology for social, public policy, planning and governance to improve urban quality of life. Next, we will present a sampling of relevant U.S. research and education achievements in structural control and monitoring as compiled by U.S. Panel that are envisioned as concepts for smart cities. Finally, we will present our recent work at UCF CITRS in the area of structural health monitoring where novel technologies such as computer vision, deep learning have been developed for our existing and next generation of smart city infrastructure
Computer Vision And Sensor Fusion For Structural Health Monitoring Framework With Emphasis On Unit Influence Line Analysis
No full textCivil Infrastructure Systems performance change over their during their life due to different reasons such as damage, unexpected loadings, severe environmental conditions not anticipated during design, and even aging due to normal continued use. These loadings contribute to structural deterioration and damage. Condition assessment is one of the most challenging activities performed by civil engineers to objectively evaluate if structures are safe for the public use. In order to evaluate the condition of existing bridges effectively, engineers need to take into account several factors while trying to come with a correct judgment. In this paper, the variation of unit influence line (UIL), is employed as an index for predicting bridge behavior under known loading conditions. The UCF-4-Span Bridge is used to explore the integration of imaging devices and traditional sensing technology, with emphasis on the analysis of UIL as index for evaluating and tracking behavioral variations in bridges. Video images and computer vision techniques are used to detect, classify and track different vehicles (input) crawling over the bridge while sensors measure the associated responses (output). UIL are extracted and compared for diagnostic, evaluation, and condition assessment. © 2009 Society for Experimental Mechanics Inc
Evaluation Of A System Identification Method For Structural Health Monitoring: Theory And Examples
No full textIn this paper, the authors explore implementing an identification technique to real life structural health monitoring problems. The method presented in this paper is Observer/Kalman IDentification (OKID) which is used in conjunction with Eigensystem Realization Algorithm (ERA). After a review of theoretical background, the method is applied to two laboratory tests. In the first case, the authors analyze dynamic test results of three reinforced concrete beams with different fiber reinforced polymer configurations. While the beams are statically loaded until failure multiple input and multiple output data sets were collected and correlation between the capacity and the dynamic properties is explored. For the second case dynamic test data of a steel grid is analyzed. The grid is tested without any damage for both single span and two span configurations and dynamic properties identified using OKID/ERA are compared with their analytical counterparts
Some Issues In Condition Assessment Of Large Structures Using Dynamic Measurements
No full textThe purpose of this paper is to present an overview of experimental structural dynamics as a unique technology for global health monitoring of large structures. Assessment of damage and objective condition evaluation of existing civil infrastructure systems (CIS) are important needs for making decisions during regular operation as well as before and after disasters. Objective condition assessment is also a fundamental knowledge need for successful health monitoring of CIS. In this paper, the writers discuss promises as well as the challenges for dynamic measurements, and the use of modal flexibility matrices for condition assessment when dynamic methods are applied on large structures. They present snapshots from their past and current studies where dynamic tests were implemented on medium and long span bridges
Bridge Failures and Mitigation Using Monitoring Technologies
No full textThe current study aims to understand the reasons for bridge failures. It considers the recent damages and collapses of in-service real bridge structures and discusses mitigation methods based on monitoring technologies. Transportation systems serve a crucial function in the strategies for mitigating bridge damages and failures. After investigating recent damages and failures in real bridges, structural bridge failures are classified in this study according to their safety and operational function. For each function, global and local level failures are also defined with two groups: major/long-span and highway bridges. In the light of this classification, structural monitoring methods are identified according to global and local failures. Along with standard Structural Health Monitoring (SHM) systems with comprehensive sensor networks, at vision-based SHM system and developments in this area are shown to provide some opportunities for mitigation of bridge failures resulting from service loads rather than natural hazard-induced failures. © 2021 Elsevier B.V., All rights reserved
Completely Contactless Structural Health Monitoring Of Real-Life Structures Using Cameras And Computer Vision
No full textA newly developed, completely contactless structural health monitoring system framework based on the use of regular cameras and computer vision techniques is introduced for obtaining displacements and vibrations of structures, which are critical responses for performance-based design and evaluation of structures. To provide contactless and practical monitoring, the current vision-based displacement measurement methods are improved by eliminating the physical target attachment. This is achieved by means of utilizing imaging key-points as virtual targets. As a result, pixel-based displacements of a monitored structural location are determined by using an improved detection and match key-points algorithm, in which false matches are identified and discarded almost completely. To transform pixel-based displacements to engineering units, a practical camera calibration method is developed because calibration standard on a physical target no longer exists. Moreover, a framework for evaluating the accuracy of vision-based displacement measurements is established for the first time, which, in return, provides users with the most crucial information of a measurement. The proposed framework along with a conventional sensor network and a data acquisition system are applied and verified on a real-life stadium during football games for structural assessment. The results obtained by the new method are successfully validated with the data acquired from sensors such as linear variable differential transformers and accelerometers. Because the proposed method does not require any type of sensor and target attachment, common field works such as sensor installation, wiring, maintaining conventional data acquisition systems are not required. This advantage enables an inexpensive and practical way for structural assessment, especially for real-life structures. Copyright © 2016 John Wiley & Sons, Ltd
A New Methodology For Identification, Localization And Quantification Of Damage By Using Time Series Modeling
No full textIn this study, a novel methodology based on time series analysis is developed to detect, locate and quantify structural changes by using free response (acceleration) data of a structure. The preliminary results of an ongoing study are presented to examine the capabilities of the methodology for damage detection in the context of Structural Health Monitoring (SHM). The basic idea behind the methodology is that a model between the outputs of a structure can be related to the structural properties of the system without using the input (excitation to the structure) or any other information. An Auto-Regressive and Moving Average with eXogenous input (ARMAX) model is created for each DOF (Degree Of Freedom) by using other DOFs\u27 outputs as the inputs to the ARMAX model. It is shown that the approach is successfully applied to two numerical models for identification, localization and quantification of the damage. The numerical models used in this study are a spring mass system and a finite element model of a laboratory structure. The potential and advantages of the methodology under investigation are discussed. Its limitations and shortcomings are also addressed along with proposed solutions and future work plans
A Comparative Evaluation Of Two Statistical Analysis Methods For Damage Detection Using Fibre Optic Sensor Data
No full textOne of the commonly used optic sensing technologies is a point sensor with Fibre Bragg Grating (FBG), which is employed with an in-house developed FBG interrogator. It is critical to couple such sensing capabilities with effective data analysis methods that can identify structural changes and detect possible damage. In this study, Robust Regression Analysis (RRA) and Cross Correlation Analysis (CCA) are employed to analyse strain data collected with FBG sensors that are installed on a 4-span bridge type structure. In order to test the efficiency of these non-parametric data analysis approaches, several tests are conducted with different damage scenarios in the laboratory environment. The efficiency of FBG sensors in conjunction with RRA and CCA algorithms for detection and localising damage are explored. Based on the findings, the RRA and CCA methods with FBGs can be expected to deliver promising results as to observing and detecting both local and global damage
Demonstration Of A Computer Vision And Sensor Fusion For Structural Health Monitoring On Ucf 4-Span Bridge
No full textStructural health monitoring (SHM) offers an automated method for tracking the health of a structure by combining assessment algorithms with sensing technologies. Novel structural health monitoring strategies for better management of civil infrastructure systems (CIS) are increasingly becoming more important as CIS are aging and subject to natural and man made hazards. The integration of imaging and optical devices with traditional sensing technology is very promising new paradigm for SHM and long-term condition assessment. This paper, presents a demonstration of an innovative and practical integrated monitoring and analysis system, combining real-time video images with sensor readings. Video stream is correlated with measured structural responses allowing a direct cause/effects relationship can be determined. This feature allows real-time assessment, continuously tracking and recording the structural performance either in situ or remotely. Any abnormal behavior triggers the recording of image and numerical information for condition-based maintenance, improving safety and operational management. The demonstration of the system is performed on the UCF 4-span bridge, a very unique and special laboratory structure designed and built by the writers to test their methods, algorithms, and to investigate the system integration before field deployment. The structure characteristics, analytical model, and sample data are also presented
Computer Vision Oriented Framework For Structural Health Monitoring Of Bridges
No full textNovel structural health monitoring strategies for better managing of civil infrastructure systems (CIS) are increasingly becoming more important as CIS are aging and subject to natural and man made hazards. Bridges constitute a critical link of the transportation network hence; any damage or collapse could result in loss of humans\u27 life and also has a negative effect in regional and national economy. The objective of this research is to propose and implement a novel framework for structural health monitoring of bridges by combining computer vision and a distributed sensors network that allows not only to record events but to infer about the damaged condition of the structure. Video stream will be used in conjunction with computer vision techniques to determine the class and the location of the vehicles moving over a bridge. The video input will also be used for surveillance purposes. A database will be constructed using information from vehicles training sets, experimental results from the sensors network and, analytical models. Then, the proposed system, by interpreting the images and by correlating those with the information contained in the database, will evaluate the operational condition of the bridge and/or will emit alerts regarding suspicious activities
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