**1. Introduction**

Robotics has influenced nearly every modern construction industry by improving efficiency, safety, and cost [1]. Robotics offers process automation and reliability thanks to sensor technologies [2]. However, while most industries have embraced this new technology from the moment it is released, many construction industries have

historically been slow to onboard automated solutions [3]. The reason could be linked to product features and complexity (project size, lifetime and uniqueness, versatile construction environments). According to Ref. [4], several other phenomena could add to the characteristics of these construction industries that often contribute to the complexity of projects. This phenomenon includes client needs that are sometimes imprecise and changing, causing significant change in costs; little overall learning because of few repetitions; high risks due to novelty; technical, climatic, and even societal uncertainties; coordination and complex decision-making processes between the teams involved; and changing conditions of realization. The weak capital budgets in R&D and the reluctance of strategies related to construction automation are other important factors [5].

These phenomena have remained unresolved for a variety of reasons. First, building nowadays are complex entities, and construction entails many different trades coming together to work in perfect sync with each other [6–9]. A replica of human-like dexterity, intelligence, and situational awareness, developed over hundreds of millions of years, is needed to break even [10]. Secondly, building construction project sites is often chaotic, disorganized spaces with materials, tools, debris, and wires spread about. Many areas of the sites have unpaved soft soil, into which a builder may sink if he steps off the beaten path. There are some environmental factors such as dust, rain, ice, and storms. There are humans walking around, etc. All these have not improved for so long because construction industries are the least digitized sector [3, 5].

Nevertheless, several ongoing initiatives suggest a gradual change in practices in this industry for rapid industrialization, which is enabled thanks to robotics and automation. According to Ref. [11], rapid industrialization is always based on quantity and quality that includes prefabrication, mechanization, automation, robotics, and finally reproduction. This gradual change toward rapid industrialization is driven by a concern to change the narrative and to be in line with the innovations observed at the international level that will respond to important building construction challenges: workforce, competitiveness, sustainable development, etc. In view of this, digital transformation with robotics technologies is one of the preferred avenues to improve the sector's overall performance over the long term [12]. This digital transformation is expressed in different ways. One of the strong currents is based on the so-called concept of the Fourth Industrial Revolution (4IR), based on unsupervised robotics utilization in building constructions.

#### **1.1 Rationale and research gap**

While the robotics use in building construction will only continue to grow from traditional design through final inspection and maintenance, the full benefits of construction robotics have yet to be realized. For example, as robotics begins to move from the lab to the real world, robots face many new challenges. A building construction assistant robot, for instance, must perform many complex tasks such as bricklaying, foaming, sorting, operating appliances, picking up, and cleaning materials in the site. It must also handle the huge variety of objects, materials, and the likes associated with these tasks such as picking up different objects, some of which it may never have seen before. For all of these problems, there exists only an abstract relationship between the robot's visible inputs and the task at hand.

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

Traditionally, a roboticist, or team thereof, would hand-design these robots for each task they want the robots to perform. Even for tasks which human users can perform intuitively, such as bricklaying, grasping, cutting objects, detecting, and avoiding various forms of obstacles in building construction sites, and these robots can be very difficult to design because developers were not able to easily translate these abstract intuitions between the robot's visible inputs and the task at hand into code. This makes it extremely challenging to scale these approaches up to the huge amount of works and obstacles that building construction robots must deal with in the realworld construction.

Secondly, most robotics that engaged in construction activities primarily rely on a system performance metric that is dependent on a metric connected to the given human-defined tasks, or task-dependent metric. This also implies a conventional paradigm of same manual development, where human designers were in charge of planning and coding for particular tasks-based activities that the robot would perform and how it would perform them [13–15]. This has some drastic limitations on the construction industry. For example, it is impossible for a bricklaying robot to perform delicate tasks such as installing electrical cables, or even to be able to detect and avoid various types of obstacles on the building construction sites. This limitation caused during design and coding thus prevented these robots from independently "thinking," "knowing," or "understanding" the multiple building operations thus generating questions about what developers had overlooked that made them to fail to get the anticipated outcome of operational autonomy.

Several researchers have criticized this conventional architecture for its lack of computational data [14–19] though went on to propose several failed models, which would have addressed this research gap. For example, in their research, several researchers [14–16] provided the first implementations of this autonomous curiosity, but they were unable to integrate their concept within the issues with construction of robo-mason by demonstrating how the robot's work patterns could emerge without the assistance of a human.

However, in order to establish this brain model that will give the robot significant cognitive developing abilities unique to humans, this study plans on modeling the brain at a level above the neural level, or what would normally be thought of as the unsupervised or unmanned level using neural deep learning algorithms that will allow the robots to learn independently from some training data. Our understanding of brain abstraction is sufficient to program a system that exhibits similar properties and connections to the human brain without having to model its detailed local wiring. Quite clearly, we will model this machine learning algorithmic concept based on neural networks and highly-parameterized models, which will use multiple layers of representation to transform data from a task-specific representation to an autonomous task. While our algorithm is not designed to solve central problems in artificial intelligence (AI), such as speech or object recognition, its development was motivated by the need to improve the performance of robots in the building construction industry. Traditional algorithms often struggle to generalize well on AI tasks specific to this domain, which prompted us to explore the potential of deep learning.

This chapter presents a distinct application of deep neural learning algorithms that can enable a building construction robotics to learn from some training data and to perform highly cognitive artificial intelligence operations. Here, rather than forcing the engineer to hand-code an entire end-to-end construction robotic system, our machine learning algorithm will allow portions of the system to be *learned* from some

training data. This approach will allow us to model concepts, which might be difficult or impossible to properly hand-model. It will also allow for adaptable models—as long as the form of the model is general, meaning it can be adapted to more or different cases simply by providing training data for these new cases.

However, by using unsupervised feature learning algorithms, deep learning approaches are able to pre-initialize these networks with useful building construction features, thus avoiding the overfitting problems commonly seen when neural networks are trained without this initialization. This machine learning algorithm can therefore work very well on a wide variety of construction projects.

Nevertheless, we begin with a general description of deep learning algorithms for unsupervised feature learning as well as their strengths and particular advantages as learning algorithms for robotics applications on building constructions. Finally, we present a simple deep/neural network algorithm to diverse robotics tasks on building construction—bricklaying, grasping, cutting materials, and aerial robot obstacle avoidance—highlighting the strengths of these algorithms in real-world robotics applications in building sites. It is our hope that these algorithms will demonstrate a more appropriate computation model where, for instance, robots' artificial intelligence and ability to detect obstacles and carry out multiple construction tasks unsupervised are no longer isolated from the subjective experience of the body.
