Critical review of data-driven decision-making in bridge operation and maintenance

Wu, Chengke; Wu, Peng; Wang, Jun; Jiang, Rui; Chen, Mengcheng; Wang, Xiangyu

doi:10.1080/15732479.2020.1833946

Cited by 121 publications

(58 citation statements)

References 212 publications

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“…Nevertheless, it is challenging to build and solve such a complicated model when the complexity of the monitored structure increases and the environmental factors are considered. Currently, model-driven methods have been progressively replaced by datadriven methods [73,74]. The most critical drawback of the model-driven approach is that modeling usually requires expertise and is time-consuming.…”

Section: Vibration-based Damage Detectionmentioning

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: Vibration-based Damage Detectionmentioning

confidence: 99%

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

et al. 2021

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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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“…More technically, deep learning-based [63][64][65][66], machine learning [67][68][69], decision making-based theories, feature selection-based solutions [70][71][72], extremer machine learning solutions [73][74][75][76], as well as hybrid searching algorithms that enhanced conventional multilayer perceptron like harris hawks optimization [77,78], whale optimizer [79,80], bacterial foraging optimization [81], chaos enhanced grey wolf optimization [82], moth-flame optimizer [74,83], many-objective sizing optimization [84][85][86][87][88][89], Driven Robust Optimization [90], ant colony optimization [91], and global numerical optimization [92]. These techniques are successfully employed in different aspects such as building design [93][94][95][96][97][98][99][100], image processing/classification [101][102][103][104][105]…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Critical review of data-driven decision-making in bridge operation and maintenance

Cited by 121 publications

References 212 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

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

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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