Discrete multi-load truss sizing optimization: model analysis and computational experiments

Fakhimi, Ramin; Shahabsafa, Mohammad; Lei, Weiming; He, Sicheng; Martins, Joaquim R. R. A.; Terlaky, Tamás; Zuluaga, Luis F.

doi:10.1007/s11081-021-09672-6

Cited by 9 publications

(3 citation statements)

References 48 publications

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“…With advancements in data acquisition capabilities, there is a growing shift toward data-driven approaches for anomaly diagnosis. Tese methods, based on statistical learning, regression, and neural networks, ofer simpler forms and require less project work, making them increasingly popular in both academia and industry [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection

Kun,

Hongren,

Xin

et al. 2024

Shock and Vibration

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This paper introduces an innovative approach, Deep Multiscale Soft-Threshold Support Vector Data Description (DMS-SVDD), designed for the detection of anomalies and prediction of faults in heavy-duty gas turbine generator sets (GENSETs). The model combines a support vector data description (SVDD) with a deep autoencoder backbone network framework, integrating a multiscale convolutional neural network (M) and soft-threshold activation network (S) into the Deep-SVDD framework. In comparison with conventional methods, such as One-Class Support Vector Machine (OCSVM) and autoencoder (AE), DMS-SVDD demonstrates improvements in accuracy (by 22.94%), recall (by 32%), F1 score (by 12.02%), and smoothness (by 39.15%). The model excels particularly in feature extraction, denoising, and early fault detection, offering a proactive strategy for maintenance. Furthermore, the DMS-SVDD demonstrated enhanced training efficiency with a reduction in the convergence rounds by 66% and overall training times by 34.13%. The study concludes that DMS-SVDD presents a robust and efficient solution for gas turbine anomaly detection, with practical advantages for decision support in turbine maintenance. Future research could explore additional refinements and applications of the DMS-SVDD model across diverse industrial contexts.

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Section: Introductionmentioning

confidence: 99%

Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection

Kun,

Hongren,

Xin

et al. 2024

Shock and Vibration

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“…Many efforts have been made to ensure the safety of suspension bridges. Fakhimi et al provided an effective heuristic solution method based on an exact solution to the MILO problem through model analysis and computational experiments [2]. Suspension bridges are subjected to loads from various aspects for a long time during operation, and Ghyabi et al proposed a method for quantifying bridge deformation based on vision [3].…”

Section: Introductionmentioning

confidence: 99%

Parameter Selection for the Dehumidification System of the Main Cable of Suspension Bridge Based on Ventilation Experiments

et al. 2023

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Moisture in the main cable is the main cause of steel wire corrosion. Modern suspension bridges utilize main cable dehumidification systems to prevent corrosion. The main cable ventilation experiment can help the selection of the system parameters. This research is based on the ventilation experiment of the main cable of Xihoumen Bridge to obtain the design parameters of the dehumidification system. According to the experiment, the suitable dehumidification distance is 150–180 m; the pressure loss of the main cable with a length of 150 m is 200–300 Pa, so the inlet pressure should be higher than 300 Pa; increasing the outlet clamp can increase the dehumidification efficiency; Under single inlet and double outlet situation, every 100% increase in air volume increases the dehumidification capacity is about 35%. The water content of the main cable of Xihoumen Bridge is 5.74 kg/m3, and 1 m3 of dry air can remove 5.5 g of water under experimental conditions, and the minimum air volume is 30 m3/h. The main factors affecting the dehumidification time are air volume and leakage rate. Input these parameters into the dehumidification system for the dehumidification experiment, and the water content of the outlet clamp will drop by about 37.5% within ten days.

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“…Over time, worldwide civil infrastructure is aging as natural disasters because of increased population and operational load [1][2][3]. Structural health monitoring (SHM) technology is gradually emerging as an important means to monitor the "health" of engineering objects [4,5]. The use of intelligent sensing systems for real-time monitoring, dynamic management, and trend research of engineering structures [6,7].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Anomaly Detection of Bridge Monitoring Data with Data Migration between Different Bridges for Balancing Data

Zhang

et al. 2023

Applied Sciences

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Structural health inspection systems are widely used to manage and maintain infrastructure that involves massive sensor devices. However, these sensors receive the natural environment or external factors in the long-term exposure to the outdoor environment, resulting in the failure of the sensors, which causes multiple categories of abnormal data in the collected data. The data often is unbalanced due to the random nature of failures. This unbalanced anomaly data poses a major challenge to existing anomaly detection methods and will affect the effectiveness of the information provided by the structural health monitoring system. In the paper, a data migration method is proposed to migrate bridge data to the target bridge dataset for expansion so that the number of images of different categories in the target bridge dataset increases. This method can be divided into three steps: firstly, to classify the data; secondly, to determine the suitability of the data and to construct the dataset; and finally, to train the data. The comparative validation is used to compare the training performance of the dataset using data migration with the dataset only using the target bridge to analyze the abnormal data identification in each category. In the experiment, the recall of some categories of data reached a significant increase of more than 30%, achieving better identification of various categories of abnormal data. Adopting the method of data migration between different bridges can solve the impact of imbalanced data and improve the recognition performance of categories with fewer images.

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Discrete multi-load truss sizing optimization: model analysis and computational experiments

Cited by 9 publications

References 48 publications

Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection

Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection

Parameter Selection for the Dehumidification System of the Main Cable of Suspension Bridge Based on Ventilation Experiments

Multiclass Anomaly Detection of Bridge Monitoring Data with Data Migration between Different Bridges for Balancing Data

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