Optimization Analysis Method of New Orthotropic Steel Deck Based on Backpropagation Neural Network‐Simulated Annealing Algorithm

Xu, Xiaoyan; Shi, Kewei; Li, Xuehong; Li, Zhijun; Wang, Rengui; Chen, Yu‐Wen

doi:10.1155/2021/8888168

Cited by 6 publications

(7 citation statements)

References 39 publications

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“…Therefore, the research combines the K -medoid algorithm with the high-order simulated annealing neural network algorithm to mine medical big data. Due to the deficiency of random selection of initial center point in the K -medoid algorithm, this paper studies the effective initialization of the K -medoid algorithm through improved granular computing [ 23 ]. Suppose that T =( X , B ) is the clustering space, the sample object set is U , and the attribute set is B , then the sample object similarity S ( x i , x j ) satisfies the following conditions:

…”

Section: Optimization Of Neural Network Algorithm Based On High-order Simulated Annealingmentioning

confidence: 99%

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

Zhang

Zhong

2021

Computational Intelligence and Neuroscience

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With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

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…”

Section: Optimization Of Neural Network Algorithm Based On High-order Simulated Annealingmentioning

confidence: 99%

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

Zhang

Zhong

2021

Computational Intelligence and Neuroscience

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“…The NN model helps reduce the number of simulations from 41 to 7 with a maximum error of 0.35. Other research related to fatigue load estimation using FNN can be seen in previous works 254–261 …”

Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

135

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Neural network (NN) models have significantly impacted fatigue‐related engineering communities and are expected to increase rapidly due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.

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“…However, the effect of OSD panel redecking on the existing steel truss bridge is unknown. Most of the previous studies focused on fatigue performance (Zhang et al, 2017;Luo et al, 2019;Liu et al, 2019a;Huang et al, 2020b), OSD component optimization (Xu et al, 2021;Laan, 2021;Huang et al, 2020a), and the development of steel ultrahigh performance concrete (steel-UHPC) composite deck (Yuan et al, 2019;Liu et al, 2019b;Wang et al, 2021;Cheng et al, 2021;Chen et al, 2019;Shao et al, 2018). This study aims to evaluate the steel truss bridge performance based on OSD panel redecking.…”

Section: Introductionmentioning

confidence: 99%

Increasing Inventory Rating Factor of Steel Truss Bridge Through Orthotropic Steel Deck Panel Application

Pradana

Triwiyono²,

Awaludin³

et al. 2022

jcef

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Currently, 18,648 bridges with a total length of 510,366 km have been constructed in Indonesia, but only 86% are in good condition, while the rest are damaged. Steel truss bridge damage generally occurs on the RC decks, and its repair is often implemented through deck replacement or redecking using Orthotropic Steel Deck (OSD) panel. In Indonesia, this method has only been applied limitedly at the Citarum I Bridge in 2009 and the Cisadane Bridge in 2013, while the effect on the existing steel truss bridge is unknown. Therefore, this study aims to evaluate the steel truss bridge performance after OSD panel redecking through numerical modeling. The design process of the OSD panel was carried out by micro-modeling on ABAQUS CAE using shell elements with a mesh size of 50x50 mm and pinned boundary conditions. In this stage, the materials were assumed to be elastic with small deformations. The evaluation of steel truss bridge performance was performed on the A-class steel truss bridge Bina Marga design standard with a 60 m span by comparing the existing bridge inventory rating factor (using RC decks) to OSD panel redecking, which is an indicator of bridge self-weight reduction. Based on the structural macro-model developed using SAP2000, the bridge self-weight reduced the axial tension and compression forces on the steel truss bridge mainframe by 20.6%-24.6% and 20.5%-24.5%, respectively. Consequently, this increased the inventory rating factor by 9.3%-9.5%. In other words, using the OSD panels lighter than the existing RC decks increases the steel truss bridge capacity to resist the live load or vehicle rating throughout its service life.

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Optimization Analysis Method of New Orthotropic Steel Deck Based on Backpropagation Neural Network‐Simulated Annealing Algorithm

Cited by 6 publications

References 39 publications

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

[Retracted] Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High‐Order Simulated Annealing Neural Network Algorithm

Fatigue modeling using neural networks: A comprehensive review

Increasing Inventory Rating Factor of Steel Truss Bridge Through Orthotropic Steel Deck Panel Application

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