# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 2 Literature Review
### Deep Learning in Construction  
Deep learning is a subordinate concept of machine learning in which a computer learns patterns from previous experiences. The complex structure of large datasets is obtained using a backpropagation algorithm that represents how the machine changes the hidden parameters used to compute each layer's representation from the previous layer's representation [24]. Artificial neural networks (i.e., the basic network concept in deep learning) use machine learning algorithms to imitate biological neural networks [25]. In general, a deep learning model consists of an input layer, a hidden layer, and an output layer. A deep neural network (DNN) literally means a deep learning model with multiple layers of \"deep\" hidden layers [26]. Deep learning has improved productivity and efficiency in a variety of industries owing to the automation of previously manual and time-consuming tasks [27]. Currently, research studies aiming to improve the traditional diagnostic framework by applying deep learning in the construction field are increasing. Traditional frameworks have been improved by applying deep learning to equipment tracking, crack detection, construction work management, sewage assessment, and 3D point cloud enhancement [14]. As a result of previous studies, the framework applied with deep learning could guarantee faster speed and accuracy compared to traditional methodologies. For instance, [PERSON] et al. [28] explored the use of 3D geometric deep neural networks, specifically multi-view CNN (MVCNN) and PointNet, to automate the classification of BIM element subtypes, such as doors and walls, in Industry Foundation Class (IFC) models. Their approach addressed the limitations of the manual specification of semantics, which is prone to errors and omissions. The MVCNN model achieved superior prediction performance, confirming that deep neural networks can effectively detect subtle differences in local geometries, enhancing the accuracy and efficiency of BIM element classification. [PERSON] et al. [2020] proposed an end-to-end vision measurement framework for structural health monitoring (SHM) using a novel deep neural network tracker called Siamese Single Decoder Network (SiamSDN). This framework does not require manual feature extraction or predefined motion areas; instead, it identifies the tracking object solely from the first frame. In their experiments involving a shaking table test and UAV-based field tests, the SiamSDN tracker improved displacement measuring accuracy by 66.16% and 57.54%, respectively, compared to traditional feature point matching approaches, demonstrating precise frequency characteristic acquisition. [PERSON] et al. [2021] introduced a powerful deep learning-based 3D reconstruction method for cable-stayed bridges. Their framework uses both multi-view images and photogrammetric point clouds as inputs to an encoder-decoder network that models high-level structural relations and low-level 3D geometric shapes. The method successfully reconstructed bridge models with structural components and their relationships, achieving an average F1 score of 99.01%, a Chamfer distance of 0.0259, and a mesh-to-cloud distance of 1.78 m. This performance surpasses that of manual reconstruction approaches in spatial accuracy, highlighting the method's robustness to noise and partial scans. [PERSON] et al. [2021] presented a novel fused BIM reconstruction approach for mechanical, electrical, and plumbing (MEP) scenes by leveraging semantic information from images and precise geometry from 3D LiDAR point clouds. They fine-tuned a state-of-the-art deep learning model for the semantic segmentation of MEP components and developed an instance-aware component extraction algorithm for LiDAR point clouds. Their method demonstrated higher accuracy and efficiency compared to previous BIM reconstruction methods, effectively handling complex MEP environments. [PERSON] et al.