Improved online sequential extreme learning machine for identifying crack behavior in concrete dam

Dai, Bo; Gu, Chongshi; Zhao, Erfeng; Zhu, K. J.; Cao, Wenhan; Qin, Xiangnan

doi:10.1177/1369433218788635

Cited by 38 publications

(26 citation statements)

References 25 publications

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“…Vision-based sensors have received considerable attention in the recent past towards the NDE of diverse civil infrastructures, ranging from sewers [ 151 ], tunnels [ 152 , 153 ], structural ceilings [ 154 ], roads [ 155 , 156 , 157 ], dams [ 158 ], pavements [ 159 , 160 , 161 , 162 ], and bridge decks [ 40 , 48 , 49 , 163 , 164 ]. The advent of state-of-the-art learning-based techniques for data analysis has facilitated the widespread usage of vision-based sensors within different robotic systems for the NDE of bridges [ 12 , 37 , 40 , 47 , 49 , 50 , 51 , 70 , 79 , 88 , 89 , 90 , 91 , 99 ].…”

Section: Tools and Techniques For Data Collectionmentioning

confidence: 99%

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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Section: Tools and Techniques For Data Collectionmentioning

confidence: 99%

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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“…e statistical model can accurately measure the correlation degree and regression fitting degree of each factor, which is more simple and convenient to analyze the influence of multiple factors. A lot of literature studies show that the crack opening displacement in the concrete dam can be well analyzed by the statistical model [20,[24][25][26]. erefore, the statistical model is adopted to analyze the crack opening displacement of the concrete dam in this paper.…”

Section: If the Crack Opening Displacement Ismentioning

confidence: 99%

Unit Root Analysis Method of Actual Crack Behavior of the Concrete Dam Based on Residuals of the Monitoring Model

Zhao

2020

Advances in Civil Engineering

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Analysis on the actual crack behavior is of important significance to evaluate safety of the concrete dam. In this paper, mainly based on the research status on cracks of the concrete dam, the unit root analysis method of actual crack behavior of concrete dam is proposed based on the monitoring data and autoregression model, aiming to enrich the analysis methods of crack behavior of concrete dam and deepen the understanding of crack behavior of concrete dam. Firstly, the feasibility of analyzing actual crack behavior based on residual sequence of the crack opening displacement monitoring model is discussed. Next, the basic principle of the unit root analysis method of crack behavior is elaborated and the unit root analysis method of actual crack behavior is established by studying prior conditions of the autoregression model and parameter estimation methods of the autoregression model, as well as determination criteria of the model order. Finally, the proposed unit root analysis method of actual crack behavior is applied to analyze monitoring data of a concrete gravity-arch dam. The result shows that the unit root analysis method of actual crack behavior is relatively simple and convenient in practical application, which is an available method to evaluate crack behavior based on data in dam health monitoring.

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“…To avoid the ill-posed matrix inversion problem associated with OS-ELM, the size of hidden neurons is usually larger than the input datum size. Genetic algorithm (GA), [58] whici is a renowned global optimization tool, was used recently to optimize the randomly initialized weight and biases of simple OS-ELM [59]. The improved OS-ELM was able to give better generalization efficiency and opened the door to a new phase in the research of finding a new sequential learning algorithm which combines OS-ELM with various evolutionary algorithms.…”

Section: R Improved Online Sequential Extreme Learning Machine Algormentioning

confidence: 99%

An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

Arshad¹,

Abido²

2020

Preprint

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This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

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Improved online sequential extreme learning machine for identifying crack behavior in concrete dam

Cited by 38 publications

References 25 publications

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Unit Root Analysis Method of Actual Crack Behavior of the Concrete Dam Based on Residuals of the Monitoring Model

An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends

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