Failure Mode Detection of Reinforced Concrete Shear Walls Using Ensemble Deep Neural Networks

Test data generated by experimental studies on RCSWs, where specimens are tested using cyclic loading protocols, is used in this study, 393 experimental data of RCSWs tested that were collected from the literature by Mangalathu, Jang (Grammatikou et al., 2015; Mangalathu et al., 2020; Usta et al., 2017). It is worth noting that the RC walls in this repository are traditional RCSWs; they do not contain testing of repaired or precast RCSWs, nor do they include walls that have been subjected to dynamic loading. RCSWs have a distribution of 3 types of cross-section configurations: 238 rectangular walls, 95 barbell walls, and 60 flanged walls (Fig. 1). The distribution of the failure mode (output) of RCSWs is shown in Table 1. Design parameters are listed in Table 2. In Table 2, \(P\) is the axial load, \(A_{{\text{g}}}\) is the gross area of the section, \(f_{{\text{c}}}\) is the compressive strength of concrete, \(A_{{\text{b}}}\) is the boundary element area, \(\rho_{i,x/y}\) is the reinforcing ratio in the horizontal/vertical direction, \(t_{{\text{w}}}\) is the wall thickness, \(H\) is the height of the wall, \(l_{{\text{w}}}\) is the length of the wall, and \(f_{{\text{y}}}\) is the yield strength of the reinforcement. Due to insufficient research funds and scientific equipment capacities, many experimental investigations have been undertaken with small-scale specimens. The use of dimensionless values for the input variables is desirable to estimate the failure of the shear walls. Therefore, parameters are normalized to use dimensionless variables. The input variables are \(M/Vl_{{\text{w}}}\), \(A_{{\text{b}}} /A_{{\text{g}}}\), \(l_{{\text{w}}} /t_{{\text{w}}}\), \(P/f_{{\text{c}}} A_{{\text{g}}}\), \(\rho_{{{\text{vb}}}} f_{{{\text{y}},{\text{vb}}}} /f_{{\text{c}}}\), \(\rho_{{{\text{vw}}}} f_{{{\text{y}},{\text{vw}}}} /f_{{\text{c}}}\), \(\rho_{{{\text{hb}}}} f_{{{\text{y}},{\text{hb}}}} /f_{{\text{c}}}\), and \(\rho_{{{\text{hw}}}} f_{{{\text{y}},{\text{hw}}}} /f_{{\text{c}}} .\) Table 3 shows the statistical properties of the input variables, where Min., Max., and STD are the minimum, maximum, and standard deviation of variables, respectively. All 393 datasets are split randomly into training and testing sets. 80% of the total of 393 datasets is utilized randomly for model development and 20% of the datasets are chosen to determine the model accuracy. Input data are normalized so that all values are within the range of − 1 and 1. The limitation of the data is that the target class has an uneven distribution of observations.

Fig. 2 provides a stacked bar for types of walls/section vs. failure mode. In Fig. 2, F, FS, S, and SL stand for ‘flexural failure’, ‘flexure–shear failure’, ‘shear failure’, and ‘sliding shear failure’. Fig. 2a shows the distribution of types of walls vs. failure mode. It is clear that the number of failure modes is different for different types of walls. The graph shows that all slender walls have only flexural failure. Furthermore, there is some variation in the graph. For instance, there is a large growth in the number of walls with shear failure when comparing the moderate sector with the squat sector. Fig. 2b shows the distribution of section vs. failure mode. It is apparent from this graph that rectangular walls mostly have flexural failure. In addition, flanged walls mostly have shear failure. This is most likely because when walls have the boundary element, their flexural strength increases. As a result, shear failure may occur before flexural failure under lateral loading if the lateral force relating to flexural strength is greater than the lateral force corresponding to shear strength.

For almost all ensemble methods, a series of models must first be created as basic models (or sub-model) to form an ensemble model. This means that several models are taught using training data. The baseline models are constructed of five different deep neural networks. Models in Keras are defined as a sequence of layers. Each layer has some nodes (neurons). When generating deep neural networks, one of the most common questions is what should be the number of neurons per layer? Here, the learning rate, activation functions, optimizer, and the number of neurons per layer are determined using the Keras-Tuner library that helps to pick the optimal set of hyperparameters for deep neural networks. Fig. 3 is a simplified form of the workflow. Here, six activation functions (Sigmoid, Relu, Softplus, Tanh, Selu, and Elu) are considered.

The optimizer is a relevant component of the training phase. The optimizer function assists the network in determining how to update the weights to minimize the loss. Eight optimization algorithms are utilized namely adaptive gradient (Adagrad) (Duchi et al., 2011), Adaptive delta (Adadelta) (Zeiler & Adadelta, 2012), Stochastic Gradient Descent (SGD) (Krogh & Hertz, 1992), Root Mean Square Prop (RMSProp) (Zeiler & Adadelta, 2012), Adaptive Moment Estimation (Adam) (Kingma & Ba, 2014), Adamax (a variant of Adam based on infinity norm) (Kingma & Ba, 2014), Nadam (Adam with Nesterov momentum) (Dozat, 2016), and Follow-the-regularized-leader (Ftrl) (McMahan et al., 2013). In this study, the basic input parameters contain \(M/Vl_{{\text{w}}}\), \(A_{{\text{b}}} /A_{{\text{g}}}\), \(l_{{\text{w}}} /t_{{\text{w}}}\),\(\rho_{{{\text{vb}}}} F_{{{\text{y}},{\text{vb}}}} /f_{{\text{c}}}\), \(\rho_{{{\text{hb}}}} F_{{{\text{y}},{\text{hb}}}} /f_{{\text{c}}}\), \(P/f_{{\text{c}}} A_{{\text{g}}}\), \(\rho_{{{\text{hw}}}} F_{{{\text{y}},{\text{hw}}}} /f_{{\text{c}}}\) , and \(\rho_{{{\text{vw}}}} F_{{{\text{y}},{\text{vw}}}} /f_{{\text{c}}} .\) The output of the model is predicted failure mode class, including ‘flexural failure’, ‘flexure–shear failure’, ‘shear failure’, and ‘sliding shear failure’.

Among all, five basic models are selected in this study based on their performance on test data. Table 4 summarizes the information of the final basic models (sub-model) that had the highest accuracy. Overfitting occurs when a model learns the knowledge and noise in the training set to the degree where it degrades the model’s performance on hold-out data. Sometimes in ensemble techniques, the sub-models may have overfitting problems. In this study, the learning curve of all models was monitored to ensure that overfitting did not occur. In this study, the learning curve (e.g., Fig. 4) of all sub-models is monitored to ensure that overfitting did not occur.

MAE is an ensemble method that involves training many models on the same data set (Brownlee, 2018). The outputs from each of the trained models are then added together, and the average is used as the final predicted value. The number of models needed for the ensemble can vary depending on the solution space complexity. One technique is to construct new models on a constant schedule (increasing the number of layers), add them to the group, and then assess their contribution to performance by predicting on a test set. Fig. 5 shows how the MAE method works using sub-models (Table 4).

The averaging performed in the MAE method means that the output values of the sub-models have an equal effect on the predicted final value (Brownlee, 2018). The WAE approach makes it possible for superior models to have a larger share of the predicted final value, while less talented models have a smaller share. In this method, a weight is assigned to the output of each subset model. The value of these weights is usually determined by an optimization algorithm.

Metaheuristic search algorithms are divided into three categories (Ahmadianfar et al., 2021), named by swarm-based algorithms, evolutionary-based algorithms, and trajectory-based algorithms. Evolutionary algorithms are developed mostly from Darwin’s theory of urgency and natural selection. Differential evolution (DE) is a kind of evolutionary algorithm (Bennis & Bhattacharjya, 2020) that initializes the members of the algorithm with several potential solutions at the start. The differential evolution iterative technique is then continued by applying the difference vector based on the DE operators, including mutation, crossover, and the selection mechanism. Following that, each solution is evaluated using a specified objective function in an iterative optimization process. The mutation process aims to vary the population member vector for the next iteration based on any available information from the previous step in the search (Eq. 1):where \(\vec{y}_{i}\) is the mutant vector (MV) of the ith member, \(x_{1,j}\), \(x_{2,j}\), and \(x_{3,j}\) are chosen at random from the population, and \(m_{{\text{r}}}\) is the scaling factor that fine-tunes the size of perturbation in the process. The crossover operator is utilized to broaden the population genetic variation among the MVs. As a result, the MV replaces its elements with those of the current population. The selection mechanism is used to determine which of the offspring individuals (and their parent) will survive in the next cycles, as well as to keep the pre-determined population size constant. The population is built using the individuals, which are selected between the trial and their predecessor vectors, which have better performance in terms of the objective function. Here, differential evolution is used to calculate the weight of each sub-model because of its advantages, such as discovering the global minimum of a search space independent of the initial values, fast convergence, and the usage of a few control factors (Karaboga & Cetinkaya, 2004). Fig. 6 shows the flowchart of the WAE method with the differential evolution algorithm.

Although the average of the model can be improved by weighting the influences of each sub-model, it may be further improved by teaching a completely new model (a neural network) to discover how to better combine each of the sub-models, using the so-called Integrated Stacking Model (ISE) (Brownlee, 2018; Naimi & Balzer, 2018). The new model is usually called meta-learner, where the sub-models are integrated with a neural network. In other words, the ISE can be interpreted as a single huge model which then learns how to merge the results from every single sub-model in the most efficient way possible. Here, the architecture of the meta-learner consists of only one hidden layer with 5 neurons. The process of determining the hidden layer’s neurons is just a case of trial and error (after examining the range of 2 to 20 neurons, the best performance of the ISE model with at least 5 neurons is obtained). Fig. 7 represents a diagram to understand the ISE model process.

Mangalathua et. al. (2020) used eight machine learning models to establish a model to distinguish the failure mode of the RCSWs. The following is a list of machine learning models that Mangalathu et. al. (2020) utilized to determine the failure modes of concrete shear walls.

Tables 6, 7 and 8 show the three performance measures of the models used by Mangalathu et. al. (2020) and the ensemble models examined in this study. It should be noted that the Mangalathu et. al. (2020) database was used in this study. Weighted-average precision or recall means that precision or recall are calculated for each class and weight by the number of instances of each class. Overall, we see the WAE model outperforms the other approaches. The best model of Mangalathu et. al. (2020) has an accuracy of 0.86 while the WAE model has an accuracy of 0.99. In terms of other performance measures, the WAE model also fares well.

Because various splits of the data can generate significantly diverse results, repeated tenfold cross-validation is performed to measure the best deep neural network’s performance. This indicates that the data is apportioned into training and test sets with a 90–10 split every time. Fig. 10 shows this by presenting model performance using tenfold cross-validation for 10 repetitions. The green triangle reflects the arithmetic mean, whereas the orange line denotes the distribution’s median. The average appears to be around 0.85, 0.84, and 0.8 for the Random Forest (Fig. 10b), CatBoost (Fig. 10c), and Decision Tree (Fig. 10d), respectively. For the WAE model, the accuracy score fluctuates slightly around 0.98. As a result, these scores can be considered the most reliable estimate of models’ performance. Also, the analysis of the test accuracy of the Random Forest, CatBoost, and Decision Tree clearly demonstrates a variance in the performance of the models trained on the dataset using tenfold cross-validation. It is now understood that although common machine learning techniques provide more flexibility and can scale in response to the amount of accessible training data, they learn using a stochastic learning algorithm (Brownlee, 2018; Maclin & Opitz, 2011), which means they are susceptible to the specifics of the training data and may discover a various set of hyperparameters each time they are trained, which in turn considers different levels of nonlinearity and level of interaction between parameters, resulting in different predictions (Brownlee, 2018; Maclin & Opitz, 2011). These algorithms include a lot of instability, which can be problematic when trying to come up with a final model to utilize for generating predictions (Brownlee, 2018; Maclin & Opitz, 2011). Training deep neural networks instead of a single model and combining the results from these models is a powerful way to lower the variation. This is also known as ensemble deep neural network models, which is used in this study, because it can not only minimize prediction variance, but also produce results that are better than any single model.

In this work, SHAP (Lundberg & Lee, 2017) is utilized to analyze the WAE model's predictions. SHAP is a game theory-based technique that can be used to indicate how the parameters affect the response. The output model in SHAP is created by adding input variables in a linear form (Eq. 5):

In Eq. 5, \(f(x)\) is the original model, \(x\) is the original input, k is the explanation model for \(f(x)\). A connection is made between \(x\) and \(x^{\prime}\) employing a function called \(h_{x} (x^{\prime})\). The decision score for each class is averaged across the samples in the training set to approximate \(\varphi_{0}\), which is stored as the expected value attribute of the explainer. The unknowns of Eq. 5 are calculated using Eq. 6:

In Eq. 6, \(M\) is the number of simplified input, \(\left| {z^{\prime}} \right|\) the count of entries that are non-zero in \(z^{\prime}\), \(S\) is the set of non-zero indices in \(z^{\prime}\), \((z^{\prime}\backslash i)\) denote setting \(z_{i}^{0} = 0\), and \(E[f(z)\left| {z_{s} } \right.]\) is SHAP explanation.

The SHAP summary chart, shown in Fig. 11, ranks features according to their importance in identifying failure modes. As we can see, the model’s most critical component is the aspect ratio (\(M/Vl_{{\text{w}}}\)). This is most likely due to the relative involvement of shear and flexural deformations. Flexural deformations cause the majority of lateral deformations in slender walls. The contribution of shear deformations is notably higher for moderate-aspect-ratio walls and especially short walls due to the presence of load transmission systems (e.g., strut action). The effect of each feature on each output (type of failure mode) is also shown in different colors. As an example, the ratio of length to thickness of the wall (\(l_{{\text{w}}} /t_{{\text{w}}}\)) has a greater effect on the wall with flexural-shear failure mode. In other words, for the ratio of length to thickness of the wall, the mean (|SHAP|) value is about (0.16–0.09) = 0.07 in shear failure mode class, and (0.28–0.17) = 0.11 in flexural-shear failure mode class, which means that the feature \(l_{{\text{w}}} /t_{{\text{w}}}\) can influence predicting the flexural-shear failure mode more than the shear failure mode. The other features that mostly affect the detection of different types of failure modes are the boundary element area to area of cross-section ratio (\(A_{{\text{b}}} /A_{{\text{g}}}\)), and the vertical and horizontal boundary element reinforcing contribution (\(\rho_{{{\text{vb}}}}\)\(f_{{{\text{y}},{\text{vb}}}} /f_{{\text{c}}}\); \(\rho_{{{\text{hb}}}}\)\(f_{{{\text{y}},{\text{hb}}}} /f_{{\text{c}}}\)).

To visualize the impact of the characteristics on the decision scores associated with each class, a different type of summary plot is employed (Fig. 12). In Fig. 12, the attributes that have the greatest impact on the decision score for each class are located at the top and blue-colored or red-colored points represent low values or high values of the parameter, respectively. Except for the flexure–shear failure mode and shear failure mode class (Fig. 12b, c), the aspect ratio has the most impact on the model output. As the value of the aspect ratio increases, their impact also increases and the model is more likely to predict flexure failure class (Fig. 12a) which corresponds to a larger probability of walls yielding in flexure before reaching the nominal shear strength of the wall; consequently, flexural behavior dominates the inelastic response.

On the other hand, the boundary element area to area of cross-section ratio is the next most important feature (Fig. 12a), and lower values of this feature correspond to a higher chance to predict flexure failure class. This observation is likely related to the wall flexural strength increasing when the boundary element area is augmented. As a result, if the lateral force related to flexural capacity is greater than the lateral force corresponding to shear capacity, shear failure may happen before flexural failure under lateral loading. In the case of the shear failure class (Fig. 12c), the boundary element area to area of cross-section ratio has the most impact on the model output. The impact of the aspect ratio is almost inverse to its effect in the flexure failure class (Fig. 12a). Low values of the feature increase the likelihood of shear failure. In the case of the flexure–shear failure mode class (Fig. 12b), although mostly influenced by the ratio of length to thickness of the wall, there is no clear correlation probably associated with the difficulty of assigning and identifying such failure mode. In the case of shear sliding failure mode, the aspect ratio feature also has the most impact. The model is more likely to anticipate shear sliding failure mode as the aspect ratio lowers (Fig. 12d) because the shear sliding strength tends to remain constant with wall height, but lateral load might increase with height reduction by preventing flexural failure. Regarding the least important factors, the SHAP values of axial load ratio and web reinforcing ratio in vertical/horizontal direction are almost close to zero for all failure types signifying that the axial load ratio and the web reinforcing ratio are the least important factors compared to other parameters. The effect of these factors on the failure mode identification cannot be interpreted in Fig. 12 since the dots are mixed and do not show the change in the SHAP value with the variation of the input features appropriately. In addition, the maximum SHAP value of the aspect ratio for flexural failure cases is higher than the other cases, which means that a small increase in the aspect ratio value increases the probability of flexural mode more than in other cases. For flexure–shear failure mode (Fig. 12b), the cross-sectional aspect ratio, defined as the ratio of length to thickness of the wall, is the most dominant parameter. A similar trend was reported by Lowes et. al. (ACI Committee, 2019) for the cross-sectional aspect ratio, where their study showed that walls with moderate to high shear stress demand and higher cross-sectional aspect ratio are susceptible to flexure–shear failures.

Fig. 13a shows the value of the aspect ratio on the x-axis and the SHAP value of it with respect to flexural failure mode on the y-axis by changing the reinforcing ratio in the vertical direction (\(\rho_{{{\text{vb}}}} f_{{{\text{y}},{\text{vb}}}} /f_{{\text{c}}}\)). The blue points represent lower values of \(\rho_{{{\text{vb}}}} f_{{{\text{y}},{\text{vb}}}} /f_{{\text{c}}}\). Blue dots are almost on the right-hand side of Fig. 13a, where values of aspect ratio are high. Hence, increasing the aspect ratio while the boundary element reinforcing ratio in the vertical direction is low is resulting in a higher chance of flexural failure mode. For Fig. 13b, despite some noise, SHAP values (with respect to shear failure) for low aspect ratio are above zero, which suggests that increasing the boundary element reinforcing ratio in the vertical direction (\(\rho_{{{\text{vb}}}} f_{{{\text{y}},{\text{vb}}}} /f_{{\text{c}}}\)), while the wall aspect ratio is low, increases the probability of shear failure mode which can be caused by the reinforcement. The use of boundary element reinforcement can help to increase the wall flexural strength, delay the beginning of bar buckling and enhance the inner concrete’s normal strain capacity.

Three types of failure for structural walls, namely flexural, shear and shear sliding failures can be categorized using ACI 318–19 design code (ACI Committee, 2019) and the concept of strength calculation. It is evident that if the shear strength of the RCSWs (Eq. 7) is lower than the shear force associated with flexural capacity, the failure occurs in shear mode. The ACI suggested a shear friction limit (Eq. 8), which is commonly used for shear sliding in walls. This equation is used as a guideline for when sliding shear takes over. In this paper, ACI 318–19 is utilized for calculating the shear and flexural capacity of RCSWs. The ACI suggested that a shear stress limit of \(0.66\sqrt {f^{\prime}_{{\text{c}}} }\) MPa be used as a guideline to prevent diagonal compression failure:where \(V_{{\text{n}}}\) is nominal shear strength, \(A_{{{\text{cv}}}}\) is the gross area of the section, \(f^{\prime}_{{\text{c}}}\) is concrete strength, \(\rho_{{\text{t}}}\) is the transverse reinforcement ratio, \(f_{{\text{y}}}\) is the yield strength of the transverse reinforcement, \(\mu\) is coefficient of friction, and \(A_{{{\text{vf}}}}\) is the area of reinforcement crossing the assumed shear plane to resist shear, \(A_{{\text{c}}}\) is the area of concrete section resisting shear transfer. Fig. 14 shows the confusion matrix based on the code concept for the test set. It can be seen that by using ACI 318–19 the accuracy in failure mode prediction is almost 53.2% which is much lower than the WAE model’s accuracy (with 98.7%). In addition, code concepts cannot be used to identify flexure–shear failure mode. For this reason, walls with flexure–shear failure mode are either in the flexural failure mode group or in the shear sliding failure mode group.