Investigation of steel frame damage based on computer vision and deep learning

Kim, Bubryur; Yuvaraj, N.; Park, Hee Won; Preethaa, K. R. Sri; Pandian, R.; Lee, Dong‐Eun

doi:10.1016/j.autcon.2021.103941

Cited by 38 publications

(10 citation statements)

References 73 publications

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“…The load and resistance reduction factor approach was implemented to assess the bond strength of various design models based on the error models and the reliability index of FRP and RC beams. The approach was found uneconomical, with partial safety factors that were not fit for all design models [5]. The bond strength varies based on the FRP width, breadth, elastic modulus, and interfacial concrete composition.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

Kim

Lee

et al. 2022

Mathematics

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Developments in fiber-reinforced polymer (FRP) composite materials have created a huge impact on civil engineering techniques. Bonding properties of FRP led to its wide usage with concrete structures for interfacial bonding. FRP materials show great promise for rehabilitation of existing infrastructure by strengthening concrete structures. Existing machine learning-based models for predicting the FRP–concrete bond strength have not attained maximum performance in evaluating the bond strength. This paper presents an ensemble machine learning approach capable of predicting the FRP–concrete interfacial bond strength. In this work, a dataset holding details of 855 single-lap shear tests on FRP–concrete interfacial bonds extracted from the literature is used to build a bond strength prediction model. Test results hold data of different material properties and geometrical parameters influencing the FRP–concrete interfacial bond. This study employs CatBoost algorithm, an improved ensemble machine learning approach used to accurately predict bond strength of FRP–concrete interface. The algorithm performance is compared with those of other ensemble methods (i.e., histogram gradient boosting algorithm, extreme gradient boosting algorithm, and random forest). The CatBoost algorithm outperforms other ensemble methods with various performance metrics (i.e., lower root mean square error (2.310), lower covariance (21.8%), lower integral absolute error (8.8%), and higher R-square (96.1%)). A comparative study is performed between the proposed model and best performing bond strength prediction models in the literature. The results show that FRP–concrete interfacial bonding can be effectively predicted using proposed ensemble method.

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Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

Kim

Lee

et al. 2022

Mathematics

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“…Ensemble models produce a higher accuracy than an individual model and can be applied to linear and nonlinear datasets. The ensemble method improves the robustness of the prediction model [ 27 ]. This proposed work builds an efficient aggregate model by stacking multilayer perceptron (MLP), support vector regression (SVR), and linear regression (LR) to predict the liquefaction-induced settlement at Pohang using available SPT data obtained from the Korea geotechnical information database system.…”

Section: Introductionmentioning

confidence: 99%

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Preethaa

Natarajan

Rathinakumar³

et al. 2022

Sensors

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Earthquakes cause liquefaction, which disturbs the design phase during the building construction process. The potential of earthquake-induced liquefaction was estimated initially based on analytical and numerical methods. The conventional methods face problems in providing empirical formulations in the presence of uncertainties. Accordingly, machine learning (ML) algorithms were implemented to predict the liquefaction potential. Although the ML models perform well with the specific liquefaction dataset, they fail to produce accurate results when used on other datasets. This study proposes a stacked generalization model (SGM), constructed by aggregating algorithms with the best performances, such as the multilayer perceptron regressor (MLPR), support vector regression (SVR), and linear regressor, to build an efficient prediction model to estimate the potential of earthquake-induced liquefaction on settlements. The dataset from the Korean Geotechnical Information database system and the standard penetration test conducted on the 2016 Pohang earthquake in South Korea were used. The model performance was evaluated by using the R2 score, mean-square error (MSE), standard deviation, covariance, and root-MSE. Model validation was performed to compare the performance of the proposed SGM with SVR and MLPR models. The proposed SGM yielded the best performance compared with those of the other base models.

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“…(7) Kim et al applied machine vision to detect steel frame damage and effectively facilitated the real-time inspection and location of steel frame damage. (8) Bouguettayaa et al focused on the detection of wildfires at their early stages in forest and wildland areas, using deep-learning-based computer vision algorithms to prevent wildfires. (9) Chian et al applied machine vision for detecting missing barricades.…”

Section: Introductionmentioning

confidence: 99%

Design of Automatic Test System for Dynamic Performance of Magnetic Head Based on Machine Vision

Long¹,

Bian²,

Wang³

2022

Sensors and Materials

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The dynamic performance test of a hard disk head is the most effective method of evaluating hard disk performance, but it must be conducted in a dust-and electrostatic discharge (ESD)-free environment. With the rapid development of perpendicular magnetic recording (PMR) technology, the flying height of a magnetic head is reduced from 18 nm to less than 2 nm, which makes the testing conditions increasingly demanding, and the traditional way of manual testing by an operator cannot match the requirement of production testing. To meet the technical requirement of the dynamic performance test of a hard disk head, an automatic test system is developed using a CCD image sensor and an Epson robot. In this system, the vision system of the robot can accomplish, in real time and accurately, the image acquisition and image processing of the object in the magnetic head testing process and calculate the actual position of the object after hand-eye calibration. Visual guidance is provided to the robot to grasp and place the target accurately, and related sensors provide various state data during the system operation. Thus, the difficult problem in the development process from manual operation to robot automation in the dynamic performance test of a magnetic head is successfully solved. The results show that the consumption of test materials is considerably reduced, and the test precision and efficiency are markedly improved.

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Investigation of steel frame damage based on computer vision and deep learning

Cited by 38 publications

References 73 publications

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

Ensemble Machine Learning-Based Approach for Predicting of FRP–Concrete Interfacial Bonding

A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction

Design of Automatic Test System for Dynamic Performance of Magnetic Head Based on Machine Vision

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