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1. Performance assessment of prestressed concrete bridge girders using fiber optic sensors and artificial neural networks

   https://doi.org/10.1080/15732479.2020.1759658  

   Khandel, Omid; Soliman, Mohamed; Floyd, Royce W.; Murray, Cameron D. (May 2020, Structure and Infrastructure Engineering)

   null (Ed.)

   Structural health monitoring (SHM) activities are essential for achieving a realistic characterisation of bridge structural performance levels throughout the service life. These activities can help detect structural damage before the potential occurrence of component- or system-level structural failures. In addition to their application at discrete times, SHM systems can also be installed to provide long-term accurate and reliable data continuously throughout the entire service life of a bridge. Owing to their superior accuracy and long-term durability compared to traditional strain gages, fiber optic sensors are ideal in extracting accurate real-time strain and temperature data of bridge components. This paper presents a statistical damage detection and localisation approach to evaluate the performance of prestressed concrete bridge girders using fiber Bragg grating sensors. The presented approach employs Artificial Neural Networks to establish a relationship between the strain profiles recorded at different sensor locations across the investigated girder. The approach is capable of detecting and localising the presence of damage at the sensor location without requiring detailed loading information; accordingly, it can be suitable for long-term monitoring activities under normal traffic loads. Experimental laboratory data obtained from the structural testing of a large-scale prestressed concrete bridge girder is used to illustrate the approach. 

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2. Chapter 2 - Robust Output Only Health Monitoring of Steel Railway Bridges: Analysis of Applicability of Different Sensors

   https://doi.org/10.4018/978-1-7998-2772-6  

   Rageh, A; Lopez, S.; Linzell, D.; Eftekhar Azam, S. (January 2020, Handbook of Research on Engineering Innovations and Technology Management in Organizations)

   Gaur, L.; Solanki, A.; Jain, V.; Khazanchi, D. (Ed.)

   This chapter extends application of a framework proposed by the authors (73, 74) for automated damage detection using strain measurements to study feasibility of using sensors that can measure accelerations, tilts, and displacements. The study utilized three-dimensional (3D) finite element models of double track, riveted, steel truss span, and girder bridge span under routine train loads. The chapter also includes three instrumentation schemes for each bridge span (65) to investigate the applicability of the framework to other bridge systems and sensor networks. Connection damage was simulated by reducing rotational spring stiffness at member ends and various responses were extracted for each damage scenario. The methodology utilizes Supervised Machine Learning to automatically determine damage location (DL) and intensity (DI). Simulated experiments showed that DLs and DIs were detected accurately for both spans with various structural responses and using different instrumentation plans. 

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3. Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization

   https://doi.org/10.1061/%28ASCE%29CP.1943-5487.0000820  

   Gulgec, Nur Sila; Takáč, Martin; Pakzad, Shamim N. (May 2019, Journal of computing in civil engineering)

   Damage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the existence, location, and severity of damage. Therefore, this procedure requires complex data processing algorithms and dense sensor arrays, which brings computational intensity with it. To address this limitation, this paper introduces convolutional neural network (CNN), which is one of the major breakthroughs in image recognition, to the damage detection and localization problem. The CNN technique has the ability to discover abstract features and complex classifier boundaries that are able to distinguish various attributes of the problem. In this paper, a CNN topology was designed to classify simulated damaged and healthy cases and localize the damage when it exists. The performance of the proposed technique was evaluated through the finite-element simulations of undamaged and damaged structural connections. Samples were trained by using strain distributions as a consequence of various loads with several different crack scenarios. Completely new damage setups were introduced to the model during the testing process. Based on the findings of the proposed study, the damage diagnosis and localization were achieved with high accuracy, robustness, and computational efficiency. 

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4. Baseline-free damage detection in bridges using acceleration records with the application of Laplacian

   AbdelLate, A.I.; Uddin, Nasim (October 2020, ASCE International Conference on Transportation & Development” 2020, Seattle, Washington, USA)

   null (Ed.)

   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. 

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5. Wavelet Based Damage Assessment and Localization for Bridge Structures

   Tan, Chengjun; Elhattab, Ahmed; Uddin, Nasim. (March 2017, Chengjun Tan, Ahmed Elhattab, Nasim Uddin)

   Bridges as a key component of road networks require periodic monitoring to detect structural degradation for early warning. In term of maintaining the bridge safety, it is essential to estimate the damage location and extent. This paper hypothetically investigates employing the wavelet transform to analysis the signal of a vehicle/bridge system to localize and estimate the damage severity. The paper investigated the feasibility of using direct measurements from the bridge system, in compare with using indirect measurements from a crossing inspection vehicle. The study utilizes an implicit Vehicle-Bridge Interaction (VBI) algorithm to simulate the passage of the instrumented vehicle over the bridge to generate the signal; then the signals are processed using Wavelet Transform. The study found that using the indirect vehicle measurements is more sensitive to bridge damage since the vehicle acts as a moving sensor over the bridge. Further, the paper shows promising results for damage detection using the bridge displacement responses, if the static component of the displacement is removed from the recorded displacement history. 

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