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Existing damage detection techniques are reliant on monitoring the anomalies in the structure behavior. This requires knowledge of the undamaged baseline structure. This paper introduces a “Baseline-free” damage detection approach that utilizes the acceleration records of the structure to precisely estimate the loci of the damages without the need of using prior data from the structure. The paper investigates the application of Laplacian – second derivative – to the structure measured accelerations in order to localize the damages signature in the measurements. The paper will emphasize on bridges as a case study. The bridge will be dam-aged with different damage levels and locations to investigate the approach fidelity in quantifying the damage severity and position. First, acceleration measurements from the bridge are evaluated for different cases. After-ward, Laplacian is applied to the amplitudes of these measurements to magnify anomalies within them. 

Recently, the concept of "drive-by" bridge monitoring system using indirect measurements from a passing vehicle to extract key parameters of a bridge has been rapidly developed. As one of the most key parameters of a bridge, the natural frequency has been successfully extracted theoretically and in practice using indirect measurements. The frequency of bridge is generally calculated applying Fast Fourier Transform (FFT) directly. However, it has been demonstrated that with the increase in vehicle velocity, the estimated frequency resolution of FFT will be very low causing a great extracted error. Moreover, because of the low frequency resolution, it is hard to detect the frequency drop caused by any damages or degradation of the bridge structural integrity. This paper will introduce a new technique of bridge frequency extraction based on Hilbert Transform (HT) that is not restricted to frequency resolution and can, therefore, improve identification accuracy. In this paper, deriving from the vehicle response, the closed-form solution associated with bridge frequency removing the effect of vehicle velocity is discussed in the analytical study. Then a numerical Vehicle-Bridge Interaction (VBI) model with a quarter car model is adopted to demonstrate the proposed approach. Finally, factors that affect the proposed approach are studied, including vehicle velocity, signal noise, and road roughness profile. 

Closed-loop state estimators that track the movements and behaviors of large-scale populations have significant potential to benefit emergency teams during the critical early stages of disaster response. Such population trackers could enable insight about the population even where few direct measurements are available. In concept, a population tracker might be realized using a Bayesian estimation framework to fuse agent-based models of human movement and behavior with sparse sensing, such as a small set of cameras providing population counts at specific locations. We describe a simple proof-of-concept for such an estimator by applying a particle-filter to synthetic sensor data generated from a small simulated environment. An interesting result is that behavioral models embedded in the particle filter make it possible to distinguish among simulated agents, even when the only available sensor data are aggregate population counts at specific locations. 

We present a method to apply simulations to the tracking of a live event such as an evacuation. We assume only a limited amount of information is available as the event is ongoing, through population-counting sensors such as surveillance cameras. In this context, agent-based models provide a useful ability to simulate individual behaviors and relationships among members of a population; however, agent-based models also introduce a significant data-association challenge when used with population-counting sensors that do not specifically identify agents. The main contribution of this paper is to develop an efficient method for managing the combinatorial complexity of data association. The key to our approach is to map from the state-space to an alternative correspondence-vector domain, where the measurement update can be implemented efficiently. We present a simulation study involving an evacuation over a road network and show that our method allows close tracking of the population over time. 

Cracks of civil infrastructures, including bridges, dams, roads, and skyscrapers, potentially reduce local stiffness and cause material discontinuities, so as to lose their designed functions and threaten public safety. This inevitable process signifier urgent maintenance issues. Early detection can take preventive measures to prevent damage and possible failure. With the increasing size of image data, machine/deep learning based method have become an important branch in detecting cracks from images. This study is to build an automatic crack detector using the state-of-the-art technique referred to as Mask Regional Convolution Neural Network (R-CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object localization but also for object instance segmentation of natural images. It is found that the built crack detector is able to perform highly effective and efficient automatic segmentation of a wide range of images of cracks. In addition, this proposed automatic detector could work on videos as well; indicating that this detector based on Mask R-CNN provides a robust and feasible ability on detecting cracks exist and their shapes in real time on-site. 

Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency. 

Bridge Weigh-in-Motion (B-WIM) is the concept of using measured strains on a bridge to calculate the axle weights of trucks as they pass overhead at full highway speed. There exist a consensus that conventional instrumentation faces substantial practical problems that halts the feasibility of this theory, namely cost, installation time and complexity. This article will go through a new concept by moving toward the first Portable Bridge Weigh-In-Motion (P-B-WIM) system. The system introduce flying sensor concept which consist of a swarm of drones that have accelerometers and able to latch bridge girders to record acceleration data. Some perching mechanisms have been introduce in this paper to allow drones to latch bridges girders. At the same time, a new algorithm is developed to allow the B-WIM system to use the acceleration data to estimate the truck weigh instead of the strain measurements. The algorithm uses the kalman-filter-based estimation algorithm to estimate the state vectors (displacement and velocities) using limited measured acceleration response (from drones). The estimated state vector is used to feed a moving force identification (MFI) algorithm that shows good results in estimating a quarter car model weight.