Artificial intelligence is the product of the constant need for data innovation. It is frequently used interchangeably with the concepts of robotization and automation and confused with machine learning or algorithms.
Dictionaries define artificial intelligence (AI) as the hypothesis and improvement of computer systems equipped for performing tasks requiring human knowledge, such as visual perception, speech recognition, and interpretation between languages.
The word “intelligence” is characterized as the capacity to comprehend, learn, and use information and abilities in new scenarios. The purpose of AI is to analyze and perceive the information gained and then perform explicit tasks.
Artificial Intelligence Technologies
The application of artificial intelligence technology in the construction industry is very much feasible now because of the advancement of three technologies: deep learning, machine learning (ML), and natural language processing.
With the help of machine learning, the execution of pre-assigned rules has improved. Hence, machine learning enhanced the algorithms that have been used so far in the artificial intelligence framework.
Machine learning allows computers to learn from data through exposure to different types of experiences, study patterns, and make decisions. It comes in numerous structures and can be addressed as design recognition, information mining, information revelation, proactive analytics, adaptive systems, statistical modeling, self-coordinating frameworks, etc.
Deep learning is a subset of machine learning. It depends on algorithms that don't need manual administration. Deep learning utilizes sets of accessible information (Big Data) and the computing power of computers (processor power, server farms, computing in the cloud).
Natural language processing is derived from machine learning and deep learning. The aim of natural language processing is to recognize speech. After conducting research in the field of speech recognition, it has empowered us to work with enormous data sets, lexical, and syntactic.
These three technological advancements: machine learning, deep learning, and natural language processing have improved the artificial intelligence devices in the field of image recognition, text, voice, automatic robots, and decision making. Therefore, the application of artificial intelligence has expanded in medicine, law, education, and construction. Five applications of artificial intelligence in construction are discussed in the following section.
1. AI for Design of Buildings
Building information modeling (BIM) is a 3D model-based software that creates a digital model of a building. All the stakeholders of a project, such as architect, client, builders, suppliers can work on this digital model to productively design, plan, and manage buildings.
For constructing a building for a particular project, BIM 3D model considers all the required information from the civil, mechanical, architecture, electrical, and plumbing plans. The effect of each plan is taken into account and subsequently each activity is planned.
The challenge is to ensure that the various models from the sub-groups don't clash with one another. The construction industry is attempting to utilize artificial intelligence as a generative plan to recognize and reduce clashes between the various models produced by the various groups in the preliminary and working drawing stages to prevent rework.
Researchers are developing new programming software that uses artificial intelligence to investigate varieties in each solution and produces design alternatives. These software use artificial intelligence to explicitly make 3D models of mechanical, electrical, and plumbing (MEP) systems while at the same time ensuring that the whole courses for MEP systems don't clash with the structure design. An example of such software is Tekla Structures.
2. AI for Project Planning
In 2018, an AI startup claimed that its artificial intelligence and robots hold the key to resolving overspending and delays in construction projects.
The company uses robots to automatically capture the 3D outputs of the construction building and feed that information into an artificial neural network that determines how far the various sub-projects are completed. If things appear to be off track, the supervisory group can step in to fix small issues before they become significant problems.
Reinforcement learning is a new AI technique developed using algorithms of the future. This technique permits algorithms to learn from field experiments. It can survey endless combinations and options based upon the use of data from similar projects undertaken in the past. It helps in project planning since it provides the best possible solution and rectifies itself over time.
3. AI for Construction Safety
In the construction industry, the death of construction workers is five times higher than other industries. As per Occupational Safety and Health Administration (OSHA), the primary sources of accidents in construction s are due to the fall of objects and electric shocks.
A general contractor from Boston with yearly revenue of $3 billion is developing an algorithm that examines photographs from its places of work, checks them for security risks, for example, laborers not wearing defensive gear and associates the pictures with its accident records. The company says it can conceivably figure out the potential hazard for projects to hold safety briefings when a threat is recognized.
4. AI for Prevention of Cost Overruns
Even by employing the best project management team, most large-scale projects spend more than the allocated budget. To track spending, artificial neural networks are used to predict cost overruns. The neural network predicts the cost overruns based on the factors like the type of contract, size of the project, and competence of the project leader.
Verifiable information, for example, earliest start and finish times, latest start and finish times of similar projects, are used by predictive models to calculate the reasonable timelines for future projects.
Artificial intelligence assists workers in giving remote training on material handling, which helps the workers to improve their knowledge and skills rapidly. This reduces the time required to install new assets onto projects and facilitates early project delivery.
5. AI for Improving Productivity
Some companies sell construction equipment that are self-driven and can perform monotonous activities more productively than their human counterparts. Examples of such construction equipments are concrete pouring machines, automatic welding machines, robotic bricklaying, and automatic building demolition machines.
Preliminary site preparation or excavation work can be performed by automatic or semi-automatic bulldozers, which can set up the construction site with the assistance of a human programmer to provide the exact specifications required by the construction drawings. Therefore, the requirement of the human factor in the construction work can be removed and hence the overall time needed to finish the project can be reduced.
Task supervisors can likewise follow the progress of job site work. Supervisors can use biometric systems, facial recognition systems, and similar innovative techniques to check the productivity of workers.
FAQs
Dictionaries define artificial intelligence (AI) as the hypothesis and improvement of computer systems equipped for performing tasks requiring human knowledge, such as visual perception, speech recognition, and interpretation between languages.
Machine learning allows computers to learn from data through exposure to different types of experiences, study patterns, and make decisions. Machine learning comes in numerous structures and can be addressed as design recognition, information mining, information revelation, proactive analytics, adaptive systems, statistical modeling, self-coordinating frameworks, etc.
Deep learning is a subset of machine learning. It depends on algorithms that don't need manual administration. Deep learning utilizes sets of accessible information (Big Data) and the computing power of computers (processor power, server farms, computing in the cloud).
Building information modeling (BIM) is a 3D model-based software that creates a digital model of a building. All the stakeholders of a project, such as architect, client, builders, suppliers can work on this digital model to productively design, plan, and manage buildings.
To track the spending of a project, artificial neural networks are used to predict cost overruns. The neural network predicts the cost overruns based on the factors like the type of contract, size of the project, and competence of the project leader.
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