Crack detection in concrete gravity dams using a genetic algorithm

Rezaiee‐Pajand, Mohammad; Tavakoli, Fariba Hassanzadeh

doi:10.1680/stbu.12.00078

Cited by 14 publications

(6 citation statements)

References 26 publications

(21 reference statements)

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“…Rezaiee-Pajand and Tavakoli [11] introduced an efficient method for crack detection in concrete gravity dams using a hybrid genetic algorithm (GA) and FE methods. The GA identifies the location and magnitude of cracks in dams by minimizing the difference between the analytical responses and the measured ones.…”

Section: Literature Reviewmentioning

confidence: 99%

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

Hariri-Ardebili

Barak

2020

Engineering Structures

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Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in riskinformed condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive, it is valuable to develop a series of forecasting models based on the unique ground motion characteristics. This paper discusses the application of six different machine learning techniques on forecasting the structural behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from ground motion signals and used in machine learning. A large set of about 2,000 real ground motions are used, each includes about 35 meta-features. The major outcome of this study is to show the applicability of meta-modeling-based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset. This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infrastructural systems.

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Section: Literature Reviewmentioning

confidence: 99%

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

Hariri-Ardebili

Barak

2020

Engineering Structures

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“…Cracks seriously damage the reliability and longevity of the structure; they can exist not only at the structure's surface but can also extend into the interior [7,8]. Zhang and Zhuang [9,10] proposed a self-propagating strong discontinuity embedded approach with the statically optimal symmetric (SDA-SOS) formulation to study the propagation law of cracks. Their computational examples showed that cracks seriously degrade structure durability.…”

Section: Introductionmentioning

confidence: 99%

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

Cao

2022

Front. Struct. Civ. Eng.

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It is of great significance to quickly detect underwater cracks as they can seriously threaten the safety of underwater structures. Research to date has mainly focused on the detection of above-water-level cracks and hasn’t considered the large scale cracks. In this paper, a large-scale underwater crack examination method is proposed based on image stitching and segmentation. In addition, a purpose of this paper is to design a new convolution method to segment underwater images. An improved As-Projective-As-Possible (APAP) algorithm was designed to extract and stitch keyframes from videos. The graph convolutional neural network (GCN) was used to segment the stitched image. The GCN’s m-IOU is 24.02% higher than Fully convolutional networks (FCN), proving that GCN has great potential of application in image segmentation and underwater image processing. The result shows that the improved APAP algorithm and GCN can adapt to complex underwater environments and perform well in different study areas.

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“…Out of these, cracks represent the primary danger because they can exist not only at the dam’s surface but also extend into the interior [ 2 ]. In other words, cracks in dams are the equivalent of mutations as dams accumulate internal damage [ 3 ]. Thus, cracks are always used to indicate the degree of risk in the field of dam damage, which has attracted the attention of numerous scholars [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A novel underwater dam crack detection and classification approach based on sonar images

Shi

Fan

et al. 2017

PLoS ONE

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Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

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Crack detection in concrete gravity dams using a genetic algorithm

Cited by 14 publications

References 26 publications

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

A series of forecasting models for seismic evaluation of dams based on ground motion meta-features

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural network

A novel underwater dam crack detection and classification approach based on sonar images

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