## 1 Introduction
When the size of the training dataset is small, the limited number of data points along with the complex interactions between the inputs/outputs, limits the ability for the ANN to fully capture (or learn) the complex relations between the inputs/outputs. In such cases, the proposed assisted approach helps to improve the ANN training process and enables the network to capture the inputs/outputs relations more adequately, and thus it improves the predictions' accuracy. As a matter of fact, the choice of the assistance parameters for any type of problem requires sufficient experience in that particular field, since the assistance parameters are chosen based on the specific knowledge of the existing relations between the inputs and outputs. The experimental dataset used in this investigation is obtained from several literature sources [30; 31; 32; 34; 41; 42]. The data covers four different panel configurations (unstiffened, stiffened, stiffened with middle broken stiffener, bolted lap-joint), different panel widths, sheet thickness, material conditions and grain orientations, along with many different lead and MSD cracks geometries. The dataset includes a total of 113 data points, each representing a unique experimental configuration. In order to demonstrate the benefit of using the proposed assisted-ANN approach, four different levels of assistance are investigated and compared. In addition to the assisted approach, the traditional approach, which relies on the use of independent geometric/material inputs only, is also used on the same data to serve as a benchmark. In our assisted approach, any known partial or full relation between any of the inputs and the output can be used to generate an assistance parameter. For the MSD problem at hand, the assistance parameters being used are mainly the crack-tip stress intensity factor (SIF) correction factors. These SIF correction factors are mainly geometry and configuration dependent, and hence they relate many of the geometric parameters to the residual strength. In addition to these SIF correction factors, theoretical residual strength predictions obtained using an analytical model (called the \"Linkup model\"), are also used as one of the assistance input parameters. Comparing the results obtained using the traditional and the assisted approaches shows that the assisted-ANN approach clearly improves the accuracy of the ANN residual strength predictions. Based on the testing dataset, the mean absolute percentage error (MAE\({}_{\text{P}}\)) went from 3.8% for the traditional approach, down to 2.97% for the assisted approach, which means that the relative error reduction is about 22%. The improvement attained using the assisted approach is clearly observed for all the three different ANN learning algorithms that are used in this study, namely; Bayesian Regularization (BR), Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG). Additionally, in order to further demonstrate the benefit of the proposed assisted-ANN approach for small datasets, the size of the training dataset is reduced from 75 data points (the initial size) all the way down to 22 data points, in three steps. As expected, reducing the size of the training dataset increases the error level for the ANN predictions. However, the results show that the relative error reduction, resulting from using the assisted approach, increases for smaller datasets. Therefore, the importance of using the assisted approach is more apparent for smaller datasets since the relative accuracy improvement becomes more significant. The assisted-ANN approach proposed here should prove to be very helpful in cases where the number of data points available for training the ANN is limited, which is generally the case in many experimental investigations in fracture mechanics. In addition, the use of such approach is not limited to fracture mechanics, but it is also generally applicable in other fields where the known analytical inputs/outputs relations can be used as inputs for assisting the ANN training/prediction process.