Gathering measurements from a structure can be extremely valuable for tasks such as verifying a numerical model, or structural health monitoring (SHM) to identify changes in the natural frequencies and mode shapes which can be attributed to changes in the system. In most monitoring applications, the number of potential degrees-of-freedom (DOF) for monitoring greatly outnumbers the available sensors. Optimal sensor placement (OSP) is a field of research into different methods for locating the available sensors to gather the optimal measurements. Three common methods of OSP are the effective independence (EI), effective independence driving point residue (EI-DPR), and modal kinetic energy (MKE) methods. However, comparisons of the different OSP methods for SHM applications are limited. In this paper, a comparison of the performance of the three described OSP methods for parameter estimation is performed. Parameter estimation is implemented using modified parameter localization with direct model updating, and added mass quantification utilizing a genetic algorithm (GA). The quantification of the mass addition, using simulated measurements from the sensor networks developed by each OSP method, is compared to provide an evaluation of each OSP methods capability for parameter estimation applications.
Vehicle counting is used by the government to improve roadways and the flow of traffic, and by private businesses for purposes such as determining the value of locating a new store in an area. A vehicle count can be performed manually or automatically. Manual counting requires an individual to be on-site and tally the traffic electronically or by hand. However, this can lead to miscounts due to factors such as human error A common form of automatic counting involves pneumatic tubes, but pneumatic tubes disrupt traffic during installation and removal, and can be damaged by passing vehicles. Vehicle counting can also be performed via the use of a camera at the count site recording video of the traffic, with counting being performed manually post-recording or using automatic algorithms. This paper presents a low-cost procedure to perform automatic vehicle counting using remote video cameras with an automatic counting algorithm. The procedure would utilize a Raspberry Pi micro-computer to detect when a car is in a lane, and generate an accurate count of vehicle movements. The method utilized in this paper would use background subtraction to process the images and a machine learning algorithm to provide the count. This method avoids fatigue issues that are encountered in manual video counting and prevents the disruption of roadways that occurs when installing pneumatic tubes
Progressive collapse is of rising importance within the structural engineering community due to several recent cases. The alternate path method is a design technique to determine the ability of a structure to sustain the loss of a critical element, or elements, and still resist progressive collapse. However, the alternate path method only considers the removal of the critical elements. In the event of a blast, significant damage may occur to nearby members not included in the alternate path design scenarios. To achieve an accurate assessment of the current condition of the structure after a blast or other extreme event, it may be necessary to reduce the strength or remove additional elements beyond the critical members designated in the alternate path design method. In this paper, a rapid model updating technique utilizing vibration measurements is used to update the structural model to represent the real-time condition of the structure after a blast occurs. Based upon the updated model, damaged elements will either have their strength reduced, or will be removed from the simulation. The alternate path analysis will then be performed, but only utilizing the updated structural model instead of numerous scenarios. After the analysis, the simulated response from the analysis will be compared to failure conditions to determine the buildings post-event condition. This method has the ability to incorporate damage to noncritical members into the analysis. This paper will utilize numerical simulations based upon a unified facilities criteria (UFC) example structure subjected to an equivalent blast to validate the methodology.
After a blast event, it is important to quickly quantify the structural damage for emergency operations. In order improve the speed, accuracy, and efficiency of condition assessments after a blast, the authors have previously performed work to develop a methodology for rapid assessment of the structural condition of a building after a blast. The method involved determining a post-event equivalent stiffness matrix using vibration measurements and a finite element (FE) model. A structural model was built for the damaged structure based on the equivalent stiffness, and inter-story drifts from the blast are determined using numerical simulations, with forces determined from the blast parameters. The inter-story drifts are then compared to blast design conditions to assess the structures damage. This method still involved engineering judgment in terms of determining significant frequencies, which can lead to error, especially with noisy measurements. In an effort to improve accuracy and automate the process, this paper will look into a similar method of rapid condition assessment using subspace state-space identification. The accuracy of the method will be tested using a benchmark structural model, as well as experimental testing. The blast damage assessments will be validated using pressure-impulse (P-I) diagrams, which present the condition limits across blast parameters. Comparisons between P-I diagrams generated using the true system parameters and equivalent parameters will show the accuracy of the rapid condition based blast assessments.
The detection and localization of damage in a timely manner is critical in order to avoid the failure of structures.
When a structure is subjected to an unscheduled impulsive force, the resulting damage can lead to failure in a very short
period of time. As such, a monitoring strategy that can adapt to variability in the environment and that anticipates
changes in physical processes has the potential of detecting, locating and mitigating damage. These requirements can be
met by a cyber-physical system (CPS) equipped with Wireless Smart Sensor Network (WSSN) systems that is capable of
measuring and analyzing dynamic responses in real time using on-board in network processing. The Eigenparameter
Decomposition of Structural Flexibility Change (ED) Method is validated with real data and considered to be used in the
computational core of this CPS. The condition screening is implemented on a damaged structure and compared to an
original baseline calculation, hence providing a supervised learning environment. An experimental laboratory study on a
5-story shear building with three damage conditions subjected to an impulsive force has been chosen to validate the
effectiveness of the method proposed to locate and quantify the extent of damage. A numerical simulation of the same
building subject to band-limited white noise has also been developed with this purpose. The effectiveness of the ED
Method to locate damage is compared to that of the Damage Index Method. With some modifications, the ED Method is
capable of locating and quantifying damage satisfactorily in a shear building subject to a lower frequency content