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

Abstract

Finite-element analysis (FEA) for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures. Conventional methods, such as FEA, provide high fidelity results but require the solution of large linear systems that can be computationally intensive. Instead, Deep Learning (DL) techniques can generate results significantly faster than conventional run-time analysis. This can prove extremely valuable in real-time structural assessment applications. Our proposed method uses deep neural networks in the form of convolutional neural networks (CNN) to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions. The CNN was designed and trained to use the geometry, boundary conditions, and load as input to predict the stress contours. The proposed technique’s performance was compared to finite-element simulations using a partial differential equation (PDE) solver. The trained DL model can predict the stress distributions with a mean absolute error of 0.9% and an absolute peak error of 0.46% for the von Mises stress distribution. This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.

Graphical abstract

Keywords

Deep Learning / finite element analysis / stress contours / structural components

1 Introduction

Stress analysis is an essential part of engineering and design. The development of various design systems continuously imposes higher demands on computational costs while preserving accuracy. Numerical analysis methods, such as structural finite element analysis (FEA), are typically used to conduct stress analysis of various structures. Researchers commonly use FEA methods to evaluate the design, safety, and maintenance of different structures in various fields, including aerospace, automotive, architecture, and civil, structural systems.

The current workflow for FEA applications includes: a) modeling geometry and its components, which can be time-consuming based on the system complexity; b) specifying material properties, boundary conditions, and loading; c) applying a meshing strategy for geometry. The time-consuming and complexity of current FEA workflows make it impractical in real-time or near real-time applications, such as in the aftermath of a disaster or during extreme disruptive events that require immediate corrections to avoid catastrophic failures.

Based on the steps of FEA described above, performing a complete stress analysis with conventional FEM has a high computational cost. To resolve this issue, we propose a Deep Learning (DL) method [1,2] to construct deep neural networks (DNN), which, once trained, allow bypass of FEA. This method may enable real-time stress analysis by leveraging machine learning (ML) algorithms. DNNs can model complicated, nonlinear relationships between input and output data. Thus, these models help us acquire adequate knowledge for predictions of unseen problems.

Data-driven approaches that model physical phenomena have been lauded for their significant and growing successes. Most recent works have included design and topology optimization [3–6], data-driven approaches in fluid dynamics [7–10], molecular dynamics simulation [11–14], and material properties prediction [15–18]. Also, Atalla et al. and Levin et al. [19,20] have used neural regression for FEA model updating. Recently, DL has shown promise in solving conventional mechanics problems. Some researchers used DL for structural damage detection, a promising alternative to conventional structural health monitoring methods [21–24]. Javadi et al. [25] used a typical neural network in FEA as a surrogate for the traditional constitutive material model. They simplified the geometry into a feature vector which approaches cases that are hard to generalize or more complicated. The numerical quadrature of the element stiffness matrix in the FEA on a per-element basis was optimized by Oishi and Yagawa [26] using DL. Their approach helps to accelerate the calculation of the element stiffness matrix.

A convolutional neural network (CNN) is a type of neural network which has shown great performance in several applications related to Computer Vision and Image Processing. The significant learning ability of CNN is mainly due to several feature extraction stages that can intrinsically learn representations from the feeding data. Madani et al. [27] developed a CNN architecture for stress prediction of arterial walls in atherosclerosis. Also, Liang et al. [28] proposed a CNN model for aortic wall stress prediction. Their method is expected to allow real-time stress analysis of human organs for a wide range of clinical applications.

In this work, we tackle the limitations of stress analysis using FEA. We propose an end-to-end DL method to predict the stress distribution in 2D linear elastic steel plates. The algorithm takes geometry, boundary conditions, and load as input and renders the von Mises stress distribution as an output. We model steel gusset plates with loading applied at different edges, different boundary conditions, and varying complex geometries. A dataset initialized with 104448 samples with varying geometry, boundary conditions, and loads is used to train and evaluate the network.

2 Background on deep learning and convolutional neural network

Artificial intelligence (AI) developed into ML over time from pattern recognition and learning theory [1]. Samuel [29] defined ML as a “field of study that allows computers to learn without being explicitly programmed”. ML algorithms can learn from data, and during the learning process, they build models which will be used to make decisions or data-driven predictions. DL is a subfield of ML which focuses on modeling hierarchical representations or abstractions to define higher-level concepts. The DL community is making significant advances in solving problems that AI has struggled to solve for many years [1]. Data of high dimensions have proven to be highly useful in discovering complex structures. Thus, DL is practical for many domains such as government and business, specifically computer vision and image recognition. These methods have shown significant performance in image classification [30], natural sentence classification [31], and image segmentation [32].

DL techniques can extract features; however, we should be careful in choosing the appropriate technique to use when dealing with a specific task. Within these approaches, CNNs have been demonstrated to be particularly efficient at acquiring a representation of the input data, including grid type data such as matrices or images. LeCun et al. [33] proposed the initial skeleton of CNN to classify handwritten digits. Over the last few years, massive hierarchical image databases, GPU programmable units, and highly parallel computing have significantly improved CNN. CNN architectures have developed greatly since the earliest work [34–36], and the performance has improved remarkably as the networks have become more complicated and deeper layers [37].

CNNs use four concepts to enhance their performance: local connections, weight sharing, pooling, and multiple layers. CNNs are composed of a series of steps. The first step involves a convolutional layer, with units in this layer organized in feature maps. Local patches in the feature maps of the previous layer are connected to each unit by a set of weights known as a filter bank. In the second step, the output of this locally weighted sum is then passed through a nonlinearity, such as a ReLU or other activation functions. The weights are then passed on to the pooling layer. Pooling layers are used to assemble semantically similar features into one. Finally, a series of convolutional, nonlinear, and pooling stages are stacked, followed by even more convolutional and fully-connected layers. A CNN uses backpropagating gradients similar to a typical deep network, allowing all the filter banks to be trained simultaneously [37].

3 Deep learning in civil and mechanical engineering

Artificial neural networks with multilayer perceptron (MLPs) have been used in civil and mechanical engineering for many years. Researchers use ANN for structural analysis [38–40], regression of material constitutive properties [41,42], and materials’ failure and damage [43,44]. Gulgec et al. [23] proposed a CNN architecture to classify simulated damaged and intact samples and localize the damage in steel gusset plates. Modarres et al. [45] studied composite materials to identify the presence and type of structural damage using CNNs. Also, for detecting concrete cracks without calculating the defect features, Zang et al. [46] proposed a vision-based method based on CNNs. An approach for predicting stress distribution on all layers of non-uniform 3D parts was presented by Khadilkar et al. [47]. More recently, Nie et al. [48] developed a CNN-based approach to predict the stress field in a 2D linear cantilever beam. However, the above works used DL techniques for structural analysis. Guo et al. [49] studied the bending analysis of Kirchhoff plates of various shapes, loads, and boundary conditions. Anitescu et al. [50] presented an artificial neural network-based collocation method for solving boundary value problems. Samaniego et al. [51] studied DNN as the basis of a technique for approximating data, and such Networks have shown promising results in areas such as visual recognition. Zhuang et al. [52] proposed a deep autoencoder-based energy method (DAEM) for the bending, vibration, and buckling analysis of Kirchhoff plates. Guo et al. [53] presented a modified neural architecture search method based on physics-informed DL for stochastic analysis of heterogeneous porous materials. Guo et al. [54] proposed a deep collocation method (DCM) based on transfer learning for solving potential problems in non-homogeneous media. To our knowledge, the current work is the first ‘DL-FE substitution’ approach to perform a fast and accurate prediction of high-resolution stress distributions in 2D steel plates.

4 Method

4.1 Data generation

Two-dimensional steel plate structures with five edges, E1 to E5 denoting edges 1 to 5, as shown in Fig.1, are considered to be made of homogeneous and isotropic linear elastic material. The 2D steel plates have similar geometry to that of gusset plates, as used for connecting beams and columns to braces in steel structures. The boundary conditions and loading angles simulate conditions that are similar to those affecting common gusset plate structures under external loading. Analysis of the behavior of these components is essential since various reports have observed failures of gusset plates subject to lateral loads [55–58]. The distributed static loads applied to the plates in this study range from 1 to 5 kN with intervals of 1 kN. Moreover, loads are applied with three angles, including π/6, π/4, and π/3, on either one or two edges of the plate. The load is decomposed to its horizontal and vertical direction components. Also, four types of boundary conditions are considered, as shown in Fig.2, based on real gusset plates’ boundary conditions. All the translational and rotational displacements are fixed at the boundary conditions. All input variables used to initialize the population are shown in Tab.1. The minimum and maximum range for the width and height of the plate are from 30 to 60 cm. Various geometries are generated by changing the position of each node in horizontal and vertical directions, as shown in Fig.1, which lead to 1024 unique pentagons. The material properties remain unchanged and isotropic for all samples.

4.1.1 Input data

The geometry is encoded into a 600 × 600 matrix as a single channel binary image. 0 (black) and 1 (white) denotes the outside and inside of the geometry, as shown in Fig.3 (a). The boundary conditions are also represented by another 600 × 600-pixel binary images, where the constrained edges are defined by 1 (white) (Fig.3). Moreover, each horizontal and vertical component of the load is encoded as one 600 × 600-pixel single-channel colored image, as shown in Fig.3(c) and Fig.3(d). The magnitudes of the decomposed horizontal and vertical components of the loads vary between 0.5 and 4.33 kN. These loads are normalized between (100,0,0) and (255,0,0) as RGB colors to create a color image where the colored part represents the location and magnitude of the load (Fig.3(c) and Fig.3(d)).

4.1.2 Output data

FEA is performed using the Partial Differential Equation (PDE) solver in the MATLAB toolbox to obtain the stress distributions of each sample. The MATLAB PDE toolbox mesh generator only generates unstructured triangulated meshes incompatible with CNN. Since each element should be represented by one pixel in an image, we develop a 600 × 600 grid surface equal to the dimensions of the most significant possible geometry. The stress values are then interpolated between the triangular elements and grids to determine a stress distribution compatible with our CNN network.

The stress values of all the elements outside the material geometry are assigned a zero, as shown in Fig.3(e). The dimensions of the largest sample are 600 mm × 600 mm, and the smallest are 300 mm × 300 mm. Therefore, the size of each element is 1 mm ×1 mm, which means that each image has 360000 pixels. This high-resolution dataset offers significant accuracy. The maximum and minimum von Mises stress values for elements among the entire dataset are 96366 and –0.73 MPa, respectively. We normalized all the output data between 0 and 1 to ensure faster convergence and encoded it to 600 × 600 matrices.

5 Convolutional neural network architecture

The CNN can be built using a sequence of convolutional layers. The convolutional layers learn to encode the input in simple signals and reconstruct the input [59]. Our CNN architecture consists of three stages of layers: The first stage is downsampling which consist of seven convolutional layers (E1, E2, E3, E4, E5, E6, E7), and the second stage has three layers (RS1, RS2, and RS3) of Squeeze-Excitation and Residual blocks (SE-ResNet). In addition, the Inception and MobileNetV2 blocks are swapped with the SE-ResNet block to check if these modules can further enhance the network’s performance. The third stage is upsampling, consisting of six deconvolutional layers (D1, D2, D3, D4, D5, D6), as illustrated in Fig.4.

5.1 Residual block

We use residual blocks [37] to address the vanishing gradient problem. In addition, residual blocks are computationally lightweight and result in only very small increases in model complexity. The shortcut connection in residual bocks simply performs identity mapping, and its output is added to the output of the stacked layers.

5.2 Squeeze-and-Excitation block

Squeeze-and-Excitation (SE) blocks improve the representative capacity of the network, enabling dynamic channel-wise feature recalibration. An SE block can be implemented with five phases. 1) The number of channels and the input convolutional block are given to the algorithm. 2) Using average pooling, the function reduces each channel to a single numeric value. 3) A fully connected layer is used to add nonlinearity followed by a ReLU function and the output channel complexity is also will be reduced. 4) Sigmoid activation followed by a second fully connected layer provides smooth gating for each channel. 5) Finally, the function, weight feature maps of each convolutional block based on the network results. Fig.5 depicts an SE-ResNet module which the SE block transformation. F_{\mathrm{t}\mathrm{r}} is regarded as the non-identity branch of a residual module. Before summation of the identity branch, both SE act. Using both SE and ResNet in the network outperforms using ResNet [60].

5.3 Inception block

Inception Modules are used to reduce the computational cost of CNNs. Since neural networks have to deal with a vast array of images, each with different content, they must be carefully designed. Using the vanilla version of the inception module, we can perform a convolution on the input meaning three different sizes of filters (1 × 1, 3 × 3, 5 × 5) instead of one. Also, max pooling is performed. The outputs are then concatenated and sent to the next layer. Therefore, convolutions occur at the same level in CNNs, where the network gets wider, not deeper. Compared with shallower and less wide CNNs, this method offers significant quality gains at a modest computational cost increase [36]. Fig.6 depicts the inception module.

5.4 MobileNetV2 block

MobileNetV2 is based on an inverted residual block with shortcut connections between thin bottleneck layers [61]. A lightweight depth-wise convolution technique is used in the intermediate layer to filter features as a source of nonlinearity. The nonlinearities must be removed in the narrow layers to maintain representational power. In general, in this model, the bottlenecks encode the intermediate inputs and outputs of the model, while the inner layer encodes how the model can transform from lower-level concepts such as pixels to higher-level features such as categories of images. Lastly, shortcuts can improve training speed and accuracy, just like traditional residual connections. Fig.7 depicts the MobileNetV2 module.

5.5 Network layers and hyperparameters

All the details of the network layers and hyperparameters can be found in Tab.2 and Tab.3. As can be seen, the models consist of 7 Conv layers, 3 different bottleneck blocks, and 6 ConvT layers. Of various combinations of Conv and ConvT layers with maximum channels of 512, 1024, 2048, and 4096, the model with 1024 channels shows the best performance. Therefore, we keep the network with 1024 channels as the primary model and swap the bottleneck each time with the SE-ResNet, Inception, and MobileNetV2. We keep the bottleneck dimension the same for all models to match the ConvT first layer. The batch size is set to 16, leading to the best accuracy compared to other batch sizes. Different learning rates from 1e−3 to 1e−6, and 1e−5 lead to the best convergence.

6 Loss function and performance metrics

We used MSE (mean squared error) for the training loss defined in Eq. (1). MSE gives a more significant penalty to large errors than MRE (mean relative error). Also, the errors are normally distributed. Using MAE (mean absolute error), MRE, PMAE (percentage mean absolute error), PAE (peak absolute error), and PPAE (percentage peak absolute error) helps evaluate the overall quality of predicted stress distribution. These metrics are defined in Eqs. (2)–(5), respectively.

(1)MSE=\frac{1}{n}\sum _{i=1}^n({s\left(i\right)-\hat{s}\left(i\right))}^2,

(2)MAE=\frac{1}{n}\sum _{i=1}^n|s\left(i\right)-\hat{s}\left(i\right)|,

where s(i) is the stress value at a node i computed by FEA as the ground truth and, \hat{s}\left(i\right) is the corresponding predicted stress by the DL model. Also, n is the total number of elements in each sample which is 360000 in our work. Symbol | | denotes the absolute value. Our model’s prediction and ground truth are displayed as 600 × 600 resolution images.

For measurement of the accuracy of predictions by comparing them to the ground truth, we use MRE:

(3)MRE=\frac{1}{n}\sum _{i=1}^n\frac{\left|s\right(i)-\hat{s}(i\left)\right|}{\in +\mathrm{m}\mathrm{a}\mathrm{x}(s\left(i\right),\hat{s}\left(i\right))}\times 100,

where the \in term is a small value to avoid a division by zero.

The percentage mean absolute error is defined as:

(4)PMAE=\frac{MAE}{\mathrm{m}\mathrm{a}\mathrm{x}\left\{s\left(i\right)\right\}-\mathrm{m}\mathrm{i}\mathrm{n}\left\{s\left(i\right)\right\}}\times 100,

where max{s\left(i\right)} is the maximum value in a set of ground truth stress values, and min{s\left(i\right)} is the minimum value.

PAE and PPAE measure the accuracy of the most significant stress value in the predicted stress distribution. PAE and PPAE are defined as:

(5)PAE=\mathrm{m}\mathrm{a}\mathrm{x}\left\{s\left(i\right)\right\}-\mathrm{m}\mathrm{a}\mathrm{x}\left\{\hat{s}\left(i\right)\right\},

(6)PPAE=\frac{PAE}{\mathrm{m}\mathrm{a}\mathrm{x}\left\{s\left(i\right)\right\}}\times 100.

7 Results and discussion

All codes are written in PyTorch Lightning and run on two NVIDIA TITAN RTX 24G GPUs. We use AdamW (Adam algorithm with weight decay) optimizer to speed up the convergence of models. We train and evaluate different models based on Tab.3 to find the model with the best performance. The training data size of models 1 to 3 is 83558, and the testing data size is 20890, randomly divided with a train/test ratio of 80%–20%. Fig.8 shows MSE and MAE losses as a function of epochs in model 1. Fig.8(a) and Fig.8(b) are linear and logarithmic scales. Fig.8(a) shows that the MSE and MAE curves rapidly decline after a few epochs. However, Fig.8(b) gives a more precise representation of the model’s behavior. Fig.8(b) shows that MSE is smaller than MAE, but both have similar general trends.

We save the best checkpoint during validation, and all error metrics are based on the best checkpoint. Models 4 to 6 are validated with K-fold cross-validation to ensure that the model is generalizable. To reduce the computational cost, we divide the dataset into three folds. K-fold cross-validation shows the best performance in all models based on most metrics, as can be seen in Tab.4, which means the model is generalizable.

We replace the SE-ResNet block in the bottleneck with the Inception and MobileNetV2 block in models 2 and 3, respectively. Model 1, has the best performance in terms of PPAE, with an error of 0.46% and model 2 is the best model, based on PMAE with a 0.57% error. Fig.9 depicts the performance of different models in terms of MAE. As can be seen in Fig.9, models 3 and 1, which have MobileNetworkV2 and SE-ResNets in a bottleneck, have almost the same performance, and model 2 with inception block is the best in terms of MAEs. We deem these results satisfactory for stress distribution predictions, specifically the PPAE, the most critical load value for stress distribution in engineering domain applications.

Fig.10 illustrates the cumulative distribution of PMAE and PPAE in the test dataset of model 1. Fig.10(a) shows the probability of mean in PMAE is 80%, which means that about 80% of predicted samples have a PMAE of less than 0.9, and 50% of samples have a PMAE of less than 0.46, which is the median. Fig.10(b) shows that about 99% of predicted samples have a PPAE of less than 0.46, and 50% of the predicted samples have a PPAE of 0.06.

The predictions produced from some randomly selected samples from the test dataset of model 1 are visualized in Fig.11. Each row represents a sample. Columns (a) to (d) represent geometry, boundary conditions, and load in horizontal and vertical directions, respectively. Columns (e) and (f) represent the ground truth and predicted stress distributions. As can be seen, there is a high fidelity fit between ground truth and predicted stress distributions in both maximum stress and stress distribution in the different samples. Also, some inaccurate predictions are shown in Fig.12. These predictions still provide useful information.

7.1 Effect of dataset size on the performance of the network

We break the data into different sizes to evaluate the effect of data size on the network’s performance of model 1. Therefore, besides training with the entire dataset, 104448 samples, we train the network with 10000, 20000, 30000, 40000, 50000, and 70000 samples. Fig.13 demonstrates that training with just 10% of the dataset can achieve a mean error of 1.85%, which is acceptable in most engineering applications. Also, it can be seen that if we want to accomplish a mean error of less than 1%, we should train the network with at least 90% of the dataset.

We also evaluate the effect of data size on the Gaussian distributions of PMAE and PPAE, illustrated in Fig.14(a) and Fig.14(b). As shown in Fig.14(a), increasing the data size decreases the standard deviation of PMAE. However, a 70000 data size and the total data size have almost the same standard deviation. Fig.14(b) shows that the standard deviation of PPAE decreases when the data size increases from 50000 to 70000. As a result, we should train the network with at least 70000 examples, 67% of our dataset, to ensure PPAE’s acceptable standard deviation accuracy.

8 Conclusions

In this work, we used end-to-end DL techniques. We developed a CNN to alleviate the need for finite element methods for prediction of high-resolution stress distributions in loaded steel plates. The CNN was designed and trained to use the geometry, boundary conditions, and load as input, and it provided high-resolution stress contours as the output. We used the PDE toolbox of MATLAB to generate the output data for training, containing 104448 FEM samples. We trained and evaluated different models to find the model with the best performance. The best model can predict the stress distributions with a mean absolute error of 0.9% and a maximum stress error of 0.46% in the von Mises stress distribution. The effects of dataset size on the model performance were also studied. Training the network with just 10% of the dataset achieved a mean error of 1.85%, which can be considered acceptable in specific engineering applications. Moreover, we evaluated the effect of dataset size on the Gaussian distribution of mean and maximum stress errors. Increasing the data size decreased the standard deviation of mean error. The standard deviation of maximum stress error also decreased with increase of the number of samples. Furthermore, the Gaussian distributions of mean and maximum stress errors demonstrated that a greater quantity of data induced less standard deviation in PMAE and PPAE.

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