The Strain Transfer Mechanism of Fiber Bragg Grating Sensor for Extra Large Strain Monitoring

Sun, Li; Li, Chuang; Zhang, Chunwei; Liang, Tianqi; Zhao, Zihao

doi:10.3390/s19081851

Cited by 107 publications

(29 citation statements)

References 24 publications

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“…Visual inspections require experienced inspectors to carry inspection instruments to reach the structure surface and conduct the inspection, and such a process can be labor-intensive, time-consuming and sometimes risky. Sensor-based monitoring can identify defects from both the structure surface and interior, and it is more reliable when the sensors are functional [7,8]. As time goes by, however, the accuracy may be compromised due to changing environments or sensor aging problems.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

et al. 2021

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Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

et al. 2021

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“…Incorrect inclusion could lead to stress concentration between the structure and the sensor leading to erroneous measurements and potential damages to the host structure. Strain transfer analysis between the fiber cable, protective layer, bonding material, and the host structure [38,39] offers a way of identifying the parameters influencing the actual and measured strains and could be used for improving the strain measurements using fiber optic sensors. The current study adopted a more simplistic approach by using random noise to introduce these experimental effects in the numerical examples.…”

Section: Results For Damage Case-1mentioning

confidence: 99%

Full-Field Strain Reconstruction Using Uniaxial Strain Measurements: Application to Damage Detection

et al. 2021

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This work investigates the inverse problem of reconstructing the continuous displacement field of a structure using a spatially distributed set of discrete uniaxial strain data. The proposed technique is based on the inverse Finite Element Method (iFEM), which has been demonstrated to be suitable for full-field displacement, and subsequently strain, reconstruction in beam and plate structures using discrete or continuous surface strain measurements. The iFEM uses a variationally based approach to displacement reconstruction, where an error functional is discretized using a set of finite elements. The effects of position and orientation of uniaxial strain measurements on the iFEM results are investigated, and the use of certain strain smoothing strategies for improving reconstruction accuracy is discussed. Reconstruction performance using uniaxial strain data is examined numerically using the problem of a thin plate with an internal crack. The results obtained highlight that strain field reconstruction using the proposed strategy can provide useful information regarding the presence, position, and orientation of damage on the plate.

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“…Mathematical Problems in Engineering [59][60][61], whale optimization algorithm (WOA) [62][63][64], grey wolf optimizer (GWO) [65,66], bacterial foraging optimization (BFO) [67], and grasshopper optimization algorithm (GOA) [68]. e aim of optimization is to determine a suitable value for one or more parameters between all possible values for them in order to minimize or maximize a function and it can be applied to find feasible answer to many potential real-life applications such as deployment optimization [69], adaptive control concepts [35,[70][71][72], computer vision techniques [73], transportation networks [74], image and video processing [75][76][77][78][79][80], decision-making approaches [81][82][83], power allocation systems [84], sensor fusion approaches [85], monitoring systems [86][87][88][89], and deep learning models [19,[90][91][92][93]. e PSO algorithm as an optimization algorithm is a social interaction model between independent particles that use their social knowledge to find the minimum and maximum value of a function [15].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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The Strain Transfer Mechanism of Fiber Bragg Grating Sensor for Extra Large Strain Monitoring

Cited by 107 publications

References 24 publications

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Full-Field Strain Reconstruction Using Uniaxial Strain Measurements: Application to Damage Detection

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

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