# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 3 Methodology
### Distance-DNN  
This process analyzes the features of distance and parameter index data collected in the previous process to determine integrity evaluation and structural error types. In this study, a DNN model was built to perform this function. The name of the proposed deep learning model is Distance-DNN. Integrity evaluation can be performed quickly even with an input value of 1024 \(\times\) 2 consisting of 1024 distance data points and 1024 parameter data points in the float format between 0 and 1.  
Distance-DNN performs structural integrity evaluation and analysis of types of structural errors. Theoretically, a DNN structure cannot simultaneously perform more than one function [26]. Therefore, each network that performs individual tasks must be configured, and the framework unites the individual networks to accomplish their tasks. In this study, an integrated framework divided into a structural integrity evaluation (SIE) network and a structural error type analysis (SETA) network is proposed so that the Distance-DNN can smoothly perform two functions. Here, the SIE network evaluates the structural integrity of the input data, and the SETA network analyzes the types of structural errors.  
This full network architecture is visualized in Figure 5, where the SIE and SETA networks are structurally similar. The network sequentially analyzes features as input data (\(1024\times 2\)), which go through a Multi-Layer Perceptron (MLP). The numbers in brackets are the change in layer size. And the global max pooling layer to aggregate information crosses all layers. While both networks share a similar base architecture for feature extraction, the SIE network concludes with a single neuron output layer for regression (integrity rate), using a linear activation function. In contrast, the SETA network ends with a multi-class softmax output layer for the classification of structural error types. Finally, the output score for the specified label is derived through the MLP. The SIE network evaluates only the structural integrity and derives an output score for the \"Fitting Rate\". The SETA network needs to derive structural error types, and it is necessary to classify them. In this study, four types of structural errors were classified as \"Tilt\", \"Deflection\", \"Rotation\", and \"Error\"; this was set as the output label of the SETA network. The output classes of the structural error type analysis (SETA) network are defined as follows:  
* Tilt: A deviation of the structural component from its intended vertical or horizontal orientation.
* Deflection: A bending or sagging of the component due to load or structural issues.
* Rotation: A rotational displacement around the component's axis.
* Error: Any other form of structural discrepancy not covered by the above categories, such as misalignment or incorrect placement.  
The \"Error\" label includes all error types that are not included in \"Tilt\", \"Deflection\", and \"Rotation\". For example, if the structural object is installed in a different place than the planned location or the intermediate joint member is different, it is included in the \"Error\" label.  
The order of structural integrity evaluation using Distance-DNN is described as follows: First, through the Scan-vs-BIM process, data consisting of 1024 distance data points and 1024 parameter data points in a float format between 0 and 1 are loaded. The data go through the SIE network, and the output score for \"Fitting Rate\" is calculated. Afterwards, the output scores for the \"Tilt\", \"Deflection\", \"Rotation\", and \"Error\" labels are calculated through the SETA network. Performing cross-validation synthesizes the derived structural integrity evaluation score and probability of each type of error and then exports the results. The result is expressed as a percentage for each label, and all structural objects are evaluated.