*2.2.1 Drones/UAV technologies application in building construction*

Few numbers of references are reported in the literature regarding use of drones/ UAVs technologies in construction by many authors [3, 25–27]. The reports show that the use of drones highlights the essential role of humans in robotics, as drones are being utilized for a range of tasks from painting to identifying safety concerns on work sites. See **Figure 4**. These robotic manipulators are appropriate for the different spraying activities involved in construction work. For example, high-rise building coating, fireproofing application, and shot blasting can all be done without the need for a human operator to be physically present. With the aid of drone robots, a variety

**Figure 4.** *Drone in construction work site.*

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

of applications, including cleaning and sandblasting, may be completed effectively. Drones that paint walls in tall buildings are among the robotic technologies. It comes equipped with automated spraying equipment and shot blasting capabilities for surface preparation.

Also, drone data has been integrated with data captured by ground-based robots, known as Autonomous Mobile Robots (AMRs), to provide a real-time view of a work site [28]. This data can then be integrated into virtual reality, allowing project managers to see the construction site without having to leave the office.

Drone data becomes necessary because traditional inspection is labor-intensive. Due to the harsh workplace and lack of trained personnel, site inspection can be completely automated using feedback from auto survey tools. These tools have a video and electronic display that transmits data to the microcomputer using fiber optic cable and employs a laser to configure the shield machine. This drone technology has been briefly applied in Architecture, Engineering, and Construction (AEC) domain and provided guidance for UAV operation and implementation in the construction industry [29]. Scaled Robotics are forms of drone which are developed as Autonomous Mobile Robots (AMRs) that are controlled *via* mobile devices where information is collected by robots using laser scanners, focusing on identifying obstacles, mistakes, or errors in an effort to reduce rework [30]. This is done with the aid of some digital photogrammetric systems that are combined with robots for demolition and site cleanup to manage large-scale earthwork projects. They are also remote-controlled vehicles featuring microwave communications, radar beacons for position, and radar sensors for avoiding obstructions and identify errors. They are, therefore, capable of controlling up to 100 units simultaneously and can identify impediments at a distance of up to 2 kilometers.

Again [31, 32], extensively conducted a systematic literature review on the current topic pertaining to implementation of Unmanned Aerial System (UAS) in the construction industry covering the most relevant job, cases, and areas of application. Their report revealed that the recent developments in UAS regulations have played a significant role in their popularity and wide deployment in various stages of the construction lifecycle. A comprehensive study conducted in the United States to identify the practical construction UAS application areas, their adopted technologies, as well as the benefits and barriers encountered during their implementation [33], and further revealed that drone robotics technologies today help many aspects of the building construction industries, including knowledge-based design and control [34].

However, a critical literature review has also been carried out on the relevant existing studies toward this immersive and digital technology [6]. The authors analyzed the literature using meta-synthesis technique to evaluate and integrate the findings in a single context. Their review shows that this class of construction drone robots for various purposes focuses mostly on tasks in hazardous areas where people cannot perform them. These domains include deep sea oil prospecting, nuclear power plants, and decommissioning issues. However, counter reports have revealed that these immersive and digital technologies have not been significantly utilized in the construction sector and by extension of other sectors of the economies in developing countries due to the high level of investment required [35–38]. It therefore makes sense to utilize these technologies that can function without administrative or motivational problems in hazardous conditions, bad weather, and at night to reduce the growing number of accidents in hazardous building tasks [39].

## *2.2.2 Bricklaying robots*

Bricklaying work is one of the most arduous jobs in construction [40, 41] since it includes a mason standing, kneeling, and lifting. In addition, the mason works almost exclusively outside and undergoes the weather conditions (rain, wind, heat, humidity). The mason sometimes works in height scaffolding or in trenched soils, which may put his life in jeopardy. However, in the last two decades, some research projects focused on the development of a bricklaying robot [42]. Bricklaying work follows predefined steps and thus is favorable for automation. However, the process cannot be fully automated and requires the supervision of a worker nearby to adjust/control the robot. Tan et al. [43] stressed the importance of the environment when designing a robot. They support the idea that robot level of autonomy should be in line with the environment (actively/passively/not assisted environment). For that, the authors proposed a framework to help categorize the robot/environment interaction.

Recent advancements in masonry work automation technology include Australian Hadrian X and Construction Robotics' SAM100 also known as semi-autonomous mason are robotic bricklaying machines [44, 45]. Hadrian X uses an intelligent control system alongside Computer Aided Drawing (CAD) to function and is capable of building a standardized home every two days on average. The robot is capable of laying the bricks with a high accuracy thanks to a laser guidance system. It is also able to work on almost any block size. The advantage of such a design is the flexibility in mobility: The robot can work under difficult circumstances linked to the environment. That is to say, by deploying Hadrian on a construction project, one can benefit from faster masonry work, least material wastage, and overall cost-efficacy. Hadrian X closely resembles a truck crane (**Figure 5**).

SAM100 (Semi-Automated Mason) on the other hand is designed to work collaboratively with masons to increase productivity by 3 to 5 times while reducing lifting by 80% and so on [45]. The robot has successfully passed the prototyping phase and is

**Figure 5.** *Bricklaying robot "HADRIAN X." source: Pivac and Pivac [44].*

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

**Figure 6.** *SAM100 robot onsite screenshot. Source: Podkaminer and Peters [45].*

now commercially available. **Figure 6** shows the utilization of SAM100 onsite. This robot is by far the most complete masonry robot realized until now. It can lay bricks with precision and includes the binder in the process of laying as well. SAM100 is capable of laying 800–1.200 bricks a day. The robot performs in a straight line with a limited height capacity. SAM100 costs around 500.000\$ (442.030 €).

## *2.2.3 Specific design building construction robots*

Several other research projects have focused on a specific design problematic of the automation process. For example, SAM100 design is based on an articulated arm as found in previous research projects [12, 46]. The "HADRIAN X" is based on a variant of the articulated arm supported by a truck-crane robot. FUNAC robots and COGIRO robots with long arms were also not left out in the discussions. COGIRO robot is used as a precise tele-operated crane to position prefabricated roof elements, while the FUNAC robots does monotonous, risky, repetitive construction works, handling payloads up to 2300 KG due to the strength of its axis, see **Figure 7**.

These robots promise to reduce operating costs and waste, as well as provide safer work environments and improve productivity. However, while deploying robots like the FUNAC in a building construction project, the number of axes it has must be a crucial factor to be considered [39]. Many people might not be aware of the significance of the robots' axes and how they regulate a robot's range of motion and strength of work. Each axis of a robot stands for a degree of freedom, or to put it another way, an independent motion that enables a construction robot to become more functional. In other words, the more degrees of freedom and higher usefulness a robot has, the more axes it has. For instance, six-axis FUNAC robots are perfect for repetitive, tiresome, and even dangerous construction tasks that were previously solely performed by people because designers mirror human arm movements and offer the same degrees of freedom as human arms [47].

**Figure 7.** *FUNAC robots with long arms placing bricks and other materials.*

FUNAC robots can therefore do whatever their human counterparts can do in building construction, without becoming fatigued or risking their safety. They have better access to even challenging vocations due to their wider range of motion and numerous degrees of freedom, thus they carry out several repetitive operations. It is perfect for the majority of applications found on construction sites, grasping, and handling payloads up to 2300 KG and reaching beyond 3.5 meters. In **Figure 8** below, FUNAC robot models are displayed.

These six-axis robots offer more advantages than other types since they have a wider range of motion and can move in more directions than just the x, y, and z planes owing to their various degrees of freedom. For example, a three-axis robot can only move in three planes (x, y, and z) because it lacks the other three axes. Robots with four or five axes may move in all three planes and can even perform extra actions like rotating or elevating the mixers.

However, due to the minimal amount of movement required to remove something from a conveyor and place it on a pallet, four-axis robots are frequently utilized in

**Figure 8.** *The FANUC M-10ia and the FANUC R-2000ib six-axis robots.*

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

palletizing applications. Although practically any palletizing operation may be completed using the four-axis FANUC M-410ib/160 without the use of the 2 extra axes, it has been previously shown [39] that a robot's range of motion increases with each axis it possesses.

In sum, we have conducted this extensive literature work to raise the consciousness of our readers on what the literature said regarding the applications and strengths of robotics in building construction. Our conclusion is that when applying robotics in building construction, engineers have a lesser work envelope because these machines are more versatile and have a wider range of work alternatives.

## **2.3 Unsupervised robots in building construction: Requirements and important qualities**

#### *2.3.1 Robot requirements for building construction tasks*


#### *2.3.2 Important qualities needed in construction robots*

Sensing and control, mobility and manipulation, human aspects and task factors, expert systems, and task flexibility are some of the qualities. The following sections describe these qualities in more detail.

#### *2.3.2.1 Sensing and control*

The largest challenge in developing construction-related robots is sensing and control, particularly in terms of navigation and position. The mobile autonomous robot requires location and heading data constantly for control. With the aid of video and image recognition systems, obstacles can be avoided and objects can be located. On numerous prototypes, obstacle avoidance is accomplished using touch sensors and ultrasonic technology [49].

#### *2.3.2.2 Mobility and manipulation*

The ability of equipment to move about construction sites is influenced by a number of variables, including the types of working environments and surface materials that must be traversed. For example, robots installed on rails have enough mobility to perform a variety of finishing activities and wall inspection jobs [50]. Different robots will do manipulative jobs according to their load bearing capabilities, arm length, and grip style.

#### *2.3.2.3 Human aspects and task factors*

The main motivation for the development of construction robots for use in severe settings, high and deep locations, boiling seas, and radiation zones is safety [51]. In these applications, the robots could operate alone or in tandem with a human operator working remotely from a secure location. Man or machine must have overall control for safety reasons. However, it is important to consider the current state of telecontrol to prevent delay issues when dealing with challenging tasks and to provide accurate manipulation feedback to the operator. During teleoperation, human factors are very crucial, even though automation reduces manual labor, it often requires more mental and cognitive effort. At this stage, the human-machine interface is particularly important for machine control and display.

#### *2.3.3 Expert system and task flexibility*

The construction industry will not benefit much from robotics on its own. Real progress can only be made when the construction process is completely organized. Expert systems, CAD/CAM, and database technologies are crucial in robots for task flexibility in civil engineering applications [52]. This field is now conducting in-depth research on unique programming environments, engineering graphics, logic, computation, and control requirements. The loadings, material properties, components, connectors, assembly, and geometric reasoning system all play a role in how construction components are represented in three dimensions by an expert system. A computerized work-control system will be necessary for robotic work to be employed efficiently. This system must include the construction site organization and sophisticated processing of commodities from the site to the robot.

#### **2.4 Theoretical framework and hypotheses formulation**

The Cognitive Behavioral Therapy (CBT), which was developed by psychologist Albert Ellis in the 1960s, and the theory of neural network, which was developed by Levin and Narendra in 1993, have been employed to guide this study. The principles of cognitive behavioral psychotherapies have described how a robo-mason can recognize, interact, think, and give meaning to situations, detect obstacles as well as form beliefs about themselves, their environments, and the world. This theory serves as a mechanism for robots to interact with its environments in an appropriate manner based on its cognitive behavior [53]. This theory better explains our study because the conventional robot design philosophy has given considerably more consideration to the embedded structure than to cognitive autonomous behavioral concerns, despite the fact that both play a substantial impact in the behaviors that follows. Autonomous cognitive design considerations of construction robo-mason are now essential in this study for a robot with embedded structure to learn and develop in order to eventually adapt to increasingly complicated building construction environments [54]. The major advantage is for the robot to change from dependent, excessive, and unhelpful behavior patterns to autonomous, advantageous, and balanced solutions.

The second theory is the theory of nonlinear dynamic system using neural network developed by Levin and Narendra [55]. The theory explains the use of neural networks to improve the stability and controllability of robotic systems. Though their study is restricted to nonlinear systems with complete state information access and feedforward MLNs with dynamic BP, their method takes into account a discrete-time *Deep Neural Networks for Unsupervised Robotics in Building Constructions: A Priority Area… DOI: http://dx.doi.org/10.5772/intechopen.111466*

**Figure 9.**

*Proposed architecture of the neural networks in the work of Levin and Narendra [55].*

system at index k, as indicated in Eq. (1). The proposed design of the neural networks is shown in **Figure 9**.

$$\mathbf{x}(\mathbf{k}+\mathbf{1}) = \mathbf{f}(\mathbf{x}(\mathbf{k}), \mathbf{u}(\mathbf{k})) \tag{1}$$

where *x*(*k*) ∈ *χ* ⊂ R*<sup>n</sup>* , *u*(*k*) ∈ *U* ⊂ R*<sup>r</sup>* and *f*(0,0) = 0 so that *x* = 0 is an equilibrium. Eq. (2) shows the conditions required for the neural networks to achieve feedback linearization and stabilization of the system.

$$\begin{cases} \boldsymbol{u} = \text{NN}\_{\boldsymbol{\Psi}}(\boldsymbol{v}, \boldsymbol{z}) \\ \boldsymbol{z} = \text{NN}\_{\boldsymbol{\Psi}}(\boldsymbol{\chi}) \end{cases} (\boldsymbol{z}, \boldsymbol{v}) = \boldsymbol{e} \mathbf{R}^{\boldsymbol{\nu}} \boldsymbol{x} \mathbf{R}^{\boldsymbol{r}} \tag{2}$$

However, Sontag tested and used this model to examine the potential and ultimate constraints of alternative neural networks designs [56]. He asserts that nonlinear systems like robots in general may be stabilized using neural networks with two hidden layers. Their conclusion seems to go against neural networks approximation theories, which contend that neural networks with a single hidden layer are the best approximators. In contrast to approximation problems, Sontag's solutions are based on the representation of the control problem as an inverse kinematics problem.
