Enabling smart city resilience: post-disaster response and structural health monitoring

The concept of Smart Cities has been introduced to categorize a vast area of activities to enhance the quality of life of citizens. A central feature of these activities is the pervasive use of Information and Communication Technologies (ICT), helping cities to make better use of limited resources. Indeed, the ASCE Vision for Civil Engineering in 2025 (ASCE 2007) portends a future in which engineers will rely on and leverage real-time access to a living database, sensors, diagnostic tools, and other advanced technologies to ensure that informed decisions are made. However, these advances in technology take place against a backdrop of the deterioration of infrastructure, in addition to natural and human-made disasters. Moreover, recent events constantly remind us of the tremendous devastation that natural and human-made disasters can wreak on society. As such, emergency response procedures and resilience are among the crucial dimensions of any Smart City plan. The U.S. Department of Homeland Security (DHS) has recently launched plans to invest $50 million to develop cutting-edge emergency response technologies for Smart Cities. Furthermore, after significant disasters have taken place, it is imperative that emergency facilities and evacuation routes, including bridges and highways, be assessed for safety. The objective of this research is to provide a new framework that uses commercial off-the-shelf (COTS) devices such as smartphones, digital cameras, and unmanned aerial vehicles to enhance the functionality of Smart Cities, especially with respect to emergency response and civil infrastructure monitoring/assessment. To achieve this objective, this research focuses on post-disaster victim localization and assessment, first responder tracking and event localization, and vision-based structural monitoring/assessment, including the use of unmanned aerial vehicles (UAVs). This research constitutes a significant step toward the realization of Smart City Resilience

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles.

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach

Enabling smart city resilience: Post-disaster response and structural health monitoring

The concept of Smart Cities has been introduced to categorize a vast area of activities to enhance the quality of life of citizens. A central feature of these activities is the pervasive use of Information and Communication Technologies (ICT), helping cities to make better use of limited resources. Indeed, the ASCE Vision for Civil Engineering in 2025 (ASCE 2007) portends a future in which engineers will rely on and leverage real-time access to a living database, sensors, diagnostic tools, and other advanced technologies to ensure that informed decisions are made. However, these advances in technology take place against a backdrop of the deterioration of infrastructure, in addition to natural and human-made disasters. Moreover, recent events constantly remind us of the tremendous devastation that natural and human-made disasters can wreak on society. As such, emergency response procedures and resilience are among the crucial dimensions of any Smart City plan. The U.S. Department of Homeland Security (DHS) has recently launched plans to invest $50 million to develop cutting-edge emergency response technologies for Smart Cities. Furthermore, after significant disasters have taken place, it is imperative that emergency facilities and evacuation routes, including bridges and highways, be assessed for safety. The objective of this research is to provide a new framework that uses commercial off-the-shelf (COTS) devices such as smartphones, digital cameras, and unmanned aerial vehicles to enhance the functionality of Smart Cities, especially with respect to emergency response and civil infrastructure monitoring/assessment. To achieve this objective, this research focuses on post-disaster victim localization and assessment, first responder tracking and event localization, and vision-based structural monitoring/assessment, including the use of unmanned aerial vehicles (UAVs). This research constitutes a significant step toward the realization of Smart City Resilience.National Science Foundation Grant No. 1030454Association of American RailroadsOpe

Real-time response estimation of structural vibration with inverse force identification

This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration. The second-order ordinary differential formulation and its projection-based reduced-order modeling is used to avoid two times larger degrees of freedom within the state space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the experimental test, the algorithm is implemented on a single-board computer, including inverse force identification and unmeasured response prediction. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.Comment: 24 Pages, 15 Figures, 10 Table

Development of carbon-based adsorbent for separation of impurities such as siloxane and ammonia from land-fill gas

Land-fill gas or bio-gas is composed of large portion of methane and carbon dioxide, and small amount of impurities such as nitrogen, oxygen, hydrogen sulfide, siloxane and ammonia. These gases can be used as a gas-fuel after upgrading treatment. For the application of the land-fill gas and bio-gas as a fuel, we developed highly-performing carbon-based adsorbent which can separate siloxane and ammonia residue from these gases. It was quite necessary to consider the chemical properties of siloxane and ammonia for development of suitable adsorbent of each component. The siloxane can be polymerized in acidic or basic condition to form bulkier species which causes adsorbent deactivation and difficult regeneration. The ammonia gas is well known as basic molecules which have strong affinity to acidic species. In these reasons, we prepared neutral carbon materials by various methods for siloxane adsorption. In addition, we developed carbon-based basic ammonia-adsorbent by simple methods such as the chemical treatment of commercial activated carbon or the impregnation of organic molecules into the activated carbon. And then, adsorption-desorption isotherms and breakthrough curve of siloxane and ammonia were measured for thus synthesized adsorbents. Detail results for synthesis and the adsorption measurement of the studied adsorbents will be presented in the conference

A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices

Due to the nature of real-world problems in civil engineering, students have had limited hands-on experiences in structural dynamics classes. To address this challenge, this paper aims to bring real-world problems in structural dynamics into classrooms through a new interactive learning tool that promotes physical interaction among students and enhances their engagement in classrooms. The main contribution is to develop and test a new interactive computing system that simulates structural dynamics by integrating a dynamic model of a structure with multimodal sensory data obtained from mobile devices. This framework involves integrating multiple physical components, estimating students’ motions, applying these motions as inputs to a structural model for structural dynamics, and providing students with an interactive response to observe how a given structure behaves. The mobile devices will capture dynamic movements of the students in real-time and take them as inputs to the dynamic model of the structure, which will virtually simulate structural dynamics affected by moving players. Each component of synchronizing the dynamic analysis with motion sensing is tested through case studies. The experimental results promise the potential to enable complex theoretical knowledge in structural dynamics to be more approachable, leading to more in-depth learning and memorable educational experiences in classrooms

Structural damage detection using deep learning and FE model updating techniques

Abstract The structural condition can be estimated by various methods. Damage detection, as one of those methods, deals with identifying changes in specific features within structural behavior based on numerical models. Since the method is based on simulation for various damage conditions, there are limitations in applicability due to inevitable discrepancies between the analytical model and the actual structure. Finite element model updating is a technique for establishing a finite element model that can reflect the current state of a target structure based on the measured responses. It is performed based on optimization for various structural parameters, but the final output can converge differently depending on the initial model and the characteristics of the algorithm. Although the updated model may not faithfully replicate the target structure as it is, it can be considered equivalent in terms of the relationship between the structural properties and behavioral characteristics of the target. This allows for the analysis of changes in the mechanical relationships established for the target structure. The change can be related to structural damage, and artificial intelligence technology can provide an alternative solution in such complex problems where analytical approaches are challenging. Taking practical aspects from the aforementioned methods, a novel structural damage detection methodology is presented in this study for identifying the location and extent of the damage. Model updating is used to establish a reference model that reflects the structural characteristics of the target. Training data for various damage conditions based on the reference model allows the artificial intelligence networks to identify damage to the target structure

Structural Displacement Measurement Using an Unmanned Aerial System

© 2017 Computer-Aided Civil and Infrastructure Engineering Vibration-based Structural Health Monitoring (SHM) is one of the most popular solutions to assess the safety of civil infrastructure. SHM applications all begin with measuring the dynamic response of structures, but displacement measurement has been limited by the difficulty in requiring a fixed reference point, high cost, and/or low accuracy. Recently, researchers have conducted studies on vision-based structural health monitoring, which provides noncontact and efficient measurement. However, these approaches have been limited to stationary cameras, which have the challenge of finding a location to deploy the cameras with appropriate line-of-sight, especially to monitor critical civil infrastructures such as bridges. The Unmanned Aerial System (UAS) can potentially overcome the limitation of finding optimal locations to deploy the camera, but existing vision-based displacement measurement methods rely on the assumption that the camera is stationary. The displacements obtained by such methods will be a relative displacement of a structure to the camera motion, not an absolute displacement. Therefore, this article presents a framework to achieve absolute displacement of a structure from a video taken from an UAS using the following phased approach. First, a target-free method is implemented to extract the relative structural displacement from the video. Next, the 6 degree-of-freedom camera motion (three translations and three rotations) is estimated by tracking the background feature points. Finally, the absolute structural displacement is recovered by combining the relative structural displacement and the camera motion. The performance of the proposed system has been validated in the laboratory using a commercial UAS. Displacement of a pinned-connected railroad truss bridge in Rockford, IL subjected to revenue-service traffic loading was reproduced on a hydraulic simulator, while the UAS was flown from a distance of 4.6 m (simulating the track clearance required by the Federal Railroad Administration), resulting in estimated displacements with an RMS error of 2.14 mm

Vision-Based Structural FE Model Updating Using Genetic Algorithm

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating