Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Jang, Keunyoung; Kim, Namgyu; An, Yun‐Kyu

doi:10.1177/1475921718821719

Cited by 163 publications

(84 citation statements)

References 31 publications

(34 reference statements)

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“…Currently, deep learning exhibits powerful performance in image recognition, speech recognition, natural language understanding, biomedicine, and other fields . In materials science, various architectures (e.g., convolutional neural network [CNN], recurrent neural network [RNN], deep belief network [DBN], and deep coding network] have demonstrated excellent performance in material detection, material analysis, material design, and quantum chemistry . CNN and RNN will be introduced in the following section.…”

Section: Modelingmentioning

confidence: 99%

“…Machine learning is more accurate and convenient than human judgment in material analysis for the detection of metal corrosion and asphalt pavement cracking and the determination of concrete strength . Agrawal et al explored various applications in which machine learning methods (such as feature selection and predictive modeling) are used to predict the fatigue strength of steel by studying the relationship among various properties of the alloy and its composition and manufacturing process parameters.…”

Section: Applicationsmentioning

confidence: 99%

“…Currently, deep learning exhibits powerful performance in image recognition, speech recognition, natural language understanding, biomedicine, and other fields. [64][65][66][67][68][69][70][71][72][73] 42,43,[74][75][76][77][78] CNN and RNN will be introduced in the following section. The following methods will not be discussed in detail: DBN, which can be used not only to classify and recognize data but also to generate data; deep stacking network, which consists of multiple blocking models and is easy to train via a supervised approach; and deep Q network, which is a new algorithm that combines deep learning with reinforcement learning to realize end-to-end learning from perception to action.…”

Section: Deep Learningmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning in materials science

Wei

Chu

Sun

et al. 2019

InfoMat

641

298

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Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.

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Section: Modelingmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

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Machine learning in materials science

Wei

Chu

Sun

et al. 2019

InfoMat

641

298

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show abstract

“…The main developers of UAVs for bridge and other structural inspections are the departments of transportation (DOT) and the universities in the USA [86,[151][152][153][154]. Along with the developments in wireless data transmission techniques, several studies have been conducted that utilized UASs technologies to broaden vision-based inspection in SHM [14,155,156], as well as vibration-based techniques [157].…”

Section: Applications Of Uavs and Portable Smartphones For Dl-based Shmmentioning

confidence: 99%

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Azimi

Eslamlou

Pekcan

2020

Sensors

412

158

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Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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“…With the help of the approaches of deep learning, the structural damage can be more accurately identified from one‐dimensional data 37–41 . Combined with computer vision technology, the networks of deep learning represented by convolutional neural network (CNN) are developed to conduct the smart detection of the cracking, corrosion, and looseness for various structural components 42–47 . Moreover, scholars provide the comprehensive approach of deep learning to capture the nonlinear behavior of structure and evaluate the complex process of the evolution of structural performance 48–50 .…”

Section: Introductionmentioning

confidence: 99%

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Zhao

Wei

et al. 2020

Struct Control Health Monit

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Nonlinear mapping of the fuzzy relation exists between structural inputs and outputs, as well as between structural global and local response. It is difficult for the numerical simulation to introduce the nonlinear effects of the variability of loading effects and the uneven deterioration of structure. The big monitoring data make it feasible to mine these nonlinear effects, and the network of deep learning is a good tool to establish the nonlinear mapping model between the multi-source monitoring data. Based on the temperature, strain, and dynamic displacement data from the structural health monitoring (SHM) system of an in-service bridge, the deep learning regression network of long short-term memory (LSTM) is designed and trained, which is used for modeling the nonlinear mapping between the structural input-output and the structural global-local response. The digital model of the nonlinear mapping of the temperature to temperature-induced strain, dynamic displacement to vehicleinduced strain, and vehicle-induced strain to dynamic displacement is established by the deep learning of LSTM regression network. The established nonlinear mapping model can be used to recover the abnormal and missing data with multi-source data, and it digitally linked the structural input-output and the structural global-local response. Then, the utilization of SHM data analytics will be enhanced, and the digital modeling for the in-service state of the bridge structure will be fast implemented.

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Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Cited by 163 publications

References 31 publications

Machine learning in materials science

Machine learning in materials science

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

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