Joint shear strength prediction of beam-column connections using machine learning via experimental results

Salem, Hanaa; el-hassan, Khaled Abu; Almetwally, Ehab M.; Aziz, Mahmoud.

doi:10.1016/j.cscm.2022.e01463

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…Practical experiments were performed to verify the effectiveness of the method. Future work will involve incorporating the height information into the road extraction and using 3D reconstruction technology and machine learning technology [ 22 ] to obtain the road in 3D space and match the actual scene more closely. At the same time, real-time detection of drones is also a very important link.…”

Section: Discussionmentioning

confidence: 99%

A Novel Vectorized Curved Road Representation Based Aerial Guided Unmanned Vehicle Trajectory Planning

Zhang,

Hou,

Zhang

et al. 2023

Sensors

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Unmanned vehicles frequently encounter the challenge of navigating through complex mountainous terrains, which are characterized by numerous unknown continuous curves. Drones, with their wide field of view and ability to vertically displace, offer a potential solution to compensate for the limited field of view of ground vehicles. However, the conventional approach of path extraction solely provides pixel-level positional information. Consequently, when drones guide ground unmanned vehicles using visual cues, the road fitting accuracy is compromised, resulting in reduced speed. Addressing these limitations with existing methods has proven to be a formidable task. In this study, we propose an innovative approach for guiding the visual movement of unmanned ground vehicles using an air–ground collaborative vectorized curved road representation and trajectory planning method. Our method offers several advantages over traditional road fitting techniques. Firstly, it incorporates a road star points ordering method based on the K-Means clustering algorithm, which simplifies the complex process of road fitting. Additionally, we introduce a road vectorization model based on the piecewise GA-Bézier algorithm, enabling the identification of the optimal frame from the initial frame to the current frame in the video stream. This significantly improves the road fitting effect (EV) and reduces the model running time (T—model). Furthermore, we employ smooth trajectory planning along the “route-plane” to maximize speed at turning points, thereby minimizing travel time (T—travel). To validate the efficiency and accuracy of our proposed method, we conducted extensive simulation experiments and performed actual comparison experiments. The results demonstrate the superior performance of our approach in terms of both efficiency and accuracy.

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Section: Discussionmentioning

confidence: 99%

A Novel Vectorized Curved Road Representation Based Aerial Guided Unmanned Vehicle Trajectory Planning

Zhang,

Hou,

Zhang

et al. 2023

Sensors

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“…As can be seen from the previous studies, the regression algorithms in machine learning will apply to this study. Many algorithms can be used for regression such as deep learning, random forest, support vector regression, ridge regression, and so on [17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This indicates that unacceptable overfitting could occur in the deep learning algorithm. Since there has never been a study covering this aspect, the performance of the models cannot be compared with others directly; the performances are, therefore, compared to the most similar study, in [17]. In that study, the highest R 2 was 0.9752.…”

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confidence: 99%

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

et al. 2022

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The state-of-the-art design methods for railway prestressed concrete sleepers are currently based on the quasi-static stresses resulting from a simplification of dynamic wheel loads, and subsequently the quasi-static responses of concrete sleepers. This method has been widely used in practices to overcome the complexity of dynamic analysis and testing. A single load factor (or called dynamic impact factor) for a partial safety-factored design (or k factors for the test criteria) is commonly used to crudely account for dynamic train–track interactions over different levels of track irregularities. The dynamic impact factors for either design or testing are usually obtained from either (i) railway infrastructure managers (i.e., in EN 13230), or (ii) prescribed standardised factors (i.e., AS 1085.14, AREMA Chapter 30, JSA—JIS E 1201). The existing design concepts for prestressed concrete sleepers using either (i) an allowable stress design or (ii) the limit state design method require many iterations for calculations and optimisations. The design process to achieve optimal products suitable for track, operational, and environmental parameters is, thus, very time-consuming. On this ground, this study investigates the potential capability of machine learning (ML) to learn from large amounts of design data sets and then to facilitate the design and capacity prediction of railway prestressed concrete sleepers. Three ML algorithms are developed, namely deep learning, Bayesian Neural Network, and random forest. Through a combination of hand-calculated design data, industry design data, and experimental investigations in compliance with EN 13230, over 3000 sets of design data have been collected. These data sets are used to assimilate a comprehensive database for machine learning. Four indicators, namely mean squared error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), and R2 are used to benchmark the accuracy and precision of machine learning models. Our results reveal that the random forest algorithm offers the best performance. The values of MSE, RMSE, MAE, and R2 are 0.54, 0.74, 0.25, and 0.99, respectively. Note that the Bayesian neural network also performs very well. In contrast, the deep learning algorithm performs worse than the others. The insight demonstrates machine learning’s capability to aid in the design of railway prestressed concrete sleepers, to satisfy both serviceability and, ultimately, a limit state.

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“…In recent years, there has been a surge of interest among researchers in utilizing machine learning techniques to predict visual surface defects, e.g., [ 1 ], as well as, mechanical strength of concrete structural components, e.g., [ 2 ]. With regard to crack detection, researchers have conventionally adopted different computer-vision algorithms to visualize crack locations in images.…”

Section: Introductionmentioning

confidence: 99%

Improving Concrete Crack Segmentation Networks through CutMix Data Synthesis and Temporal Data Fusion

Jamshidi

El-Badry

Nourian

2023

Sensors

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A key element in an automated visual inspection system for concrete structures is identifying the geometric properties of surface defects such as cracks. Fully convolutional neural networks (FCNs) have been demonstrated to be powerful tools for crack segmentation in inspection images. However, the performance of FCNs depends on the size of the dataset that they are trained with. In the absence of large datasets of labeled images for concrete crack segmentation, these networks may lose their excellent prediction accuracy when tested on a new target dataset with different image conditions. In this study, firstly, a Transfer Learning approach is developed to enable the networks better distinguish cracks from background pixels. A synthetic dataset is generated and utilized to fine-tune a U-Net that is pre-trained with a public dataset. In the proposed data synthesis approach, which is based on CutMix data augmentation, the crack images from the public dataset are combined with the background images of a potential target dataset. Secondly, since cracks propagate over time, for sequential images of concrete surfaces, a novel temporal data fusion technique is proposed. In this technique, the network’s predictions from multiple time steps are aggregated to improve the recall of predictions. It is shown that application of the proposed improvements has increased the F1-score and mIoU by 28.4% and 22.2%, respectively, which is a significant enhancement in performance of the segmentation network.

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Joint shear strength prediction of beam-column connections using machine learning via experimental results

Cited by 10 publications

References 28 publications

A Novel Vectorized Curved Road Representation Based Aerial Guided Unmanned Vehicle Trajectory Planning

A Novel Vectorized Curved Road Representation Based Aerial Guided Unmanned Vehicle Trajectory Planning

Machine Learning Based Design of Railway Prestressed Concrete Sleepers

Improving Concrete Crack Segmentation Networks through CutMix Data Synthesis and Temporal Data Fusion

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