**2. Literature review**

#### **2.1 Deep learning/neural network for unsupervised robotics technologies**

Deep learning is a machine learning approach based on modeling adaptation of biological neural systems. It can also be defined as a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs [20]. Information processing is carried out through connectionist approach to computation and this amount of information needs a complex abstraction as data representations through a hierarchical learning process [21]. The term hierarchical learning here is referred to neural networks [22]. An example of such a neural network is shown in **Figure 1**.

Here, our algorithm could be implemented to train the network in an unsupervised manner. This is because the multi-layer (with many hidden layers) neural network is being used as shown in **Figure 2** in which each layer takes input from the previous layer, processes it, and outputs it to the next layer, in a daisy-chain fashion.

With this, our deep learning can be used to generate high level of abstraction for the building construction robots. So for complex abstractions of data representations through a hierarchical learning process, our deep learning model can produce results faster than standard machine learning [23]. And also our proposed deep learning model will be integrated with building construction features that are important by itself to be learned, instead of requiring to be manually selecting the pertinent features. However, this unsupervised learning model does not require the presence of a teacher. The desired output is not presented to the network. The system learns on its own by adapting the structural features in the input patterns. The general role of our unsupervised learning model is shown in **Figure 3**.

*Deep Neural Networks for Unsupervised Robotics in Building Constructions: A Priority Area… DOI: http://dx.doi.org/10.5772/intechopen.111466*

**Figure 1.** *Multi-layer artificial neural network.*

**Figure 2.** *Deep neural network for deep learning.*

#### **2.2 Robotics application and its capacity on building construction**

Robotics applications in construction are a large field of study due to the multidisciplinary trades that constitute the act of constructing. For example, bridges are a type of construction subjected to various robotic automations. Oh et al. [9] developed a robot for bridge inspection and Lorenc et al. [24] for maintenance. Bridges can be difficult to access, and there is a high demand for robots to perform diagnostics and repairs. However, we have gathered information from various resources especially from relevant literatures during a span of 10 years, i.e., 2013– 2023 pertaining to more forms of robotics' applications and its capacity in building construction.

**Figure 3.** *Classification and clustering.*
