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1. An Intelligently Designed AI for Structural Health Monitoring of a Reinforced Concrete Bridge

   Locke, William R; Mokalled, Stefani C; Abuodeh, Omar R; Redmond, Laura M; McMahan, Christopher S (January 2021, The Concrete Industry in the Era of AI)

   e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques. 

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2. 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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3. Seismic fragility analysis using nonlinear autoregressive neural networks with exogenous input

   https://doi.org/10.1080/15732479.2021.1894184  

   Sheikh, Imran A.; Khandel, Omid; Soliman, Mohamed; Haase, Jennifer S.; Jaiswal, Priyank (January 2021, Structure and Infrastructure Engineering)

   null (Ed.)

   Rapidly growing societal needs in urban areas are increasing the demand for tall buildings with complex structural systems. Many of these buildings are located in areas characterized by high seismicity. Quantifying the seismic resilience of these buildings requires comprehensive fragility assessment that integrates iterative nonlinear dynamic analysis (NDA). Under these circumstances, traditional finite element (FE) analysis may become impractical due to its high computational cost. Soft-computing methods can be applied in the domain of NDA to reduce the computational cost of seismic fragility analysis. This study presents a framework that employs nonlinear autoregressive neural networks with exogenous input (NARX) in fragility analysis of multi-story buildings. The framework uses structural health monitoring data to calibrate a nonlinear FE model. The model is employed to generate the training dataset for NARX neural networks with ground acceleration and displacement time histories as the input and output of the network, respectively. The trained NARX networks are then used to perform incremental dynamic analysis (IDA) for a suite of ground motions. Fragility analysis is next conducted based on the results of the IDA obtained from the trained NARX network. The framework is illustrated on a twelve-story reinforced concrete building located at Oklahoma State University, Stillwater campus. 

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4. Behavior of Reinforced HPFRCC Columns Subjected to Axial and Lateral Loads

   Almeida, Joseph A; Bandelt, Matthew J (September 2023, ACI, Rilem, fib)

   Massicotte, Bruno; Mobasher, Barzin; Plizzari, Giovanni (Ed.)

   While widely adopted prescriptive-based design practices work to limit the probability of complete collapse, relatively little attention and emphasis is placed on the damage levels and functionality of structures after seismic events. High-performance fiber reinforced cementitious composites reinforced with steel (R/HPFRCCs) have been of growing interest for such seismic applications to improve structural level damage and performance. In order to progress the implementation of these materials at the structural level, a systematic approach toward understanding the mechanics of R/HPFRCC columns is warranted. Therefore, in this study, an existing numerical framework for R/HPFRCC beams was extended to the analysis of columns across a range of materials, reinforcement ratios, and axial load levels to evaluate the change in component level response. It was observed that axial load can considerably increase the nominal bending moment capacity of R/HPFRCC columns as well as affect the drift capacity. A shift from failure on the tension side of the element (e.g., reinforcement fracture) to the compression side (e.g., crushing of the HPFRCC) of the numerically tested column occurred between an axial load ratio of 10 and 20%. Lastly, changes in bond stress due to the material level tensile strength were shown to considerably impact the ultimate component drift capacity. 

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5. Modeling Household Earthquake Hazard Adjustment Intentions: An Extension of the Protection Motivation Theory

   https://doi.org/10.1061/(ASCE)NH.1527-6996.0000607  

   Li, Yueqi; Greer, Alex; Wu, Hao-Che (May 2023, Natural Hazards Review)

   Nasim Uddin Louise K. Comfort (Ed.)

   While existing literature has explored how hazard experience, salience, and demographics characteristics shape threat appraisal and hazard adjustment intentions, this study expands on past studies by exploring how additional factors such as qualitative characteristics of the hazard, political ideology, and oil entanglements shape threat appraisals, coping appraisals, and adjustment intentions in response to a techna hazard. This study builds on protection motivation theory (PMT) to explore factors that shape Oklahomans’ intentions to adjust to induced seismicity using data collected from households (n=866) across 27 counties in Oklahoma that have experienced varying levels of seismic activity resulting from oil and gas exploration. Correlational analyses and structural equation modeling show that several variables not included in the original PMT, such as feelings of dread or negative emotions associated with earthquakes, are important predictors of intentions to adopt hazard adjustments. This study concludes with examining the effect of additional factors on adjustment intentions and risk perceptions that can help guide future earthquake risk management in identifying and taking appropriate actions that will stimulate precautionary behavior of private actors. 

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