Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

Bolandi, Hamed; Li, Xuyang; Salem, Talal; Boddeti, Vishnu Naresh; Lajnef, Nizar

doi:10.1007/s11709-022-0882-5

Cited by 27 publications

(7 citation statements)

References 59 publications

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“…Zong et al [14] used the central composite experimental design method (CCD) and second-order response surface model to update the finite element model based on the health monitoring of a large-span continuous rigid frame bridge, proving that finite element model updating based on a second-order response surface has a high accuracy [15][16][17]. In recent years, finite element modeling and correction technology based on artificial intelligence has attracted more and more attention; for instance, wavelet convolutional neural networks and deep-learning neural networks have been used for wind-induced vibration modeling and stress distribution prediction [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

Dong,

Shi,

Zhong

et al. 2024

Buildings

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In this paper, A high-order response surface method is proposed for finite element model updating of continuous beam bridges. Firstly, based on visual inspection and environmental vibration testing, the peak picking (PP) method and random subspace identification (SSI) method are used to identify the dynamic characteristic parameters of the structure. Then, the finite element model of the continuous beam bridge is updated based on the third-order response surface method. It can be concluded that the results of the updated finite element model are in good agreement with the test results, and the maximum error between the calculated and measured frequency is less than 3%, with MAC values greater than 85%. Moreover, the updated finite element model can reflect the current situation of real bridges and serve as the basis for bridge health monitoring, damage detection, and safety assessment.

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Section: Introductionmentioning

confidence: 99%

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

Dong,

Shi,

Zhong

et al. 2024

Buildings

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“…The test and train correlation results presented in table 1 reveal a positive relationship between the train dataset size and the correlation, confirming the expected trend. The observed increase in correlation with larger dataset sizes highlights the significant impact of dataset size on the model's predictive performance [71]. The model was trained on a system equipped with dual Quadro RTX 5000 GPUs and 128 GB of RAM.…”

Section: Effect Of the Dataset Sizementioning

confidence: 99%

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Rezasefat,

Hogan

2023

Mach. Learn.: Sci. Technol.

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This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.

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“…The process is broken down into three different stages: first, starting to learn reflective feature space using the multiple CNNs through the Cont-RPs of the three frequency varieties; second, incorporating their symmetrical outputs into one incorporated recognition variable; and third, creating class probabilities based on the representation vectors by learning the relationships between them using a fully-connected layer including a nonlinear function. Although our CNNs feature the same structure as the initial portion of the VGG16 model [51], many of the parameters were trained from scratch using the training dataset. Figure 2 shows the overview of the presented CNN framework.…”

Section: Classification and Feature Extractionmentioning

confidence: 99%

Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines

Shahbazi

Byun

2023

Biology

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Pregnancy and early childhood are two vulnerable times when immunological plasticity is at its peak and exposure to stress may substantially raise health risks. However, to separate the effects of adversity during vulnerable times of the lifetime from those across the entire lifespan, we require deeper phenotyping. Stress is one of the challenges which everyone can face with this issue. It is a type of feeling which contains mental pressure and comes from daily life matters. There are many research and investments regarding this problem to overcome or control this complication. Pregnancy is a susceptible period for the child and the mother taking stress can affect the child’s health after birth. The following matter can happen based on natural disasters, war, death or separation of parents, etc. Early Life Stress (ELS) has a connection with psychological development and metabolic and cardiovascular diseases. In the following research, the main focus is on Early Life Stress control during pregnancy of a healthy group of women that are at risk of future disease during their pregnancy. This study looked at the relationship between retrospective recollections of childhood or pregnancy hardship and inflammatory imbalance in a group of 53 low-income, ethnically diverse women who were seeking family-based trauma treatment after experiencing interpersonal violence. Machine learning Convolutional Neural Networks (CNNs) are applied for stress detection using short-term physiological signals in terms of non-linear and for a short term. The focus concepts are heart rate, and hand and foot galvanic skin response.

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Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

Cited by 27 publications

References 59 publications

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

FE Model Updating of Continuous Beam Bridge Based on Response Surface Method

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines

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