Artificial Intelligence in Construction

Real Estate

Artificial Intelligence (AI) is unequivocally recognized from the point of mechanical advancement and something that all people are accustomed to utilizing at work and in their day-to-day routine. It has initiated a significant change that will greatly impact work, society, and the worldwide economy.

AI in construction industry is utilized to design the means of electrical and plumbing frameworks in present-day structures. Development organizations are now using AI to foster security frameworks, arrange and control building destinations, gathers and assesses the continuous associations of laborers, apparatus and materials on the building site and cautions groups in case of wellbeing issues, development blunders or low efficiency.

AI is unlikely to replace the human workforce. However, it will change plans of action in the development business, lessen expensive errors and make construction operations more productive.

Most common AI Use Cases

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Process Optimization

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    Design software that analyses a countless number of project development alternatives and adds to the persistent improvement of planning.

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    Models that decreases inspection costs

Risk Management

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    Image recognition and classification systems that can evaluate photos and videos collected on construction sites and identify incorrect or dangerous behavior to support the design of safety courses.

Trends in the application of artificial intelligence in civil engineering

Robotic assembly automation

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    As a result, an autonomous robot with some of the workers' skills, such as the ability to manipulate various construction materials, walk over uneven terrain, and assemble structures with impeccable precision, can be created. The machine strictly adheres to the programming developed by the engineers, with the main advantage of having a near-zero chance of making an error.

Smart building construction

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    The concept of intelligence will be what distinguishes new constructions, and with artificial intelligence in construction, this can be applied to various items in a building, for example. More precise motion sensors that ensure energy savings, temperature sensors that detect the presence of people in a given environment, and smart elevators that optimize resident movement. If these initiatives already exist in some newer buildings, the tendency is that they are combined — in an intelligent way — to generate savings in time and money.

Application of Big Data on the Construction Site

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    Some studies and experiments provide hints about what will become common in civil construction in the future, and one of them is the use of Big Data on construction sites. To give you an idea, the Massachusetts Institute of Technology (MIT) is already using computers to collect and analyze information for the construction of wind farms. Computers analyze wind behavior and, based on the data generated, can optimize the location where wind blades will be installed. In this case, in addition to artificial intelligence increasing energy generation, computer-based analyses take less time than those performed by field engineers.

Customer service that is personalized and based on relationships

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    Some construction companies, particularly those with direct customer contact, require the maintenance of entire teams to provide ongoing support, such as answering questions, generating duplicate invoices, or responding to complaints. This necessitates a slew of expenses and processes that can be streamlined with artificial intelligence in construction industry.

Our Projects

Artificial Intelligence Drone computer vision

How Construction debris detection from a drone can reduce inspection cost AI with Drones

Group 1277


Computer Vision


City municipality

Potential industries

Environment and Urban Development, Real Estate, Logistics, and Transportation


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Our Project Approach

The AI ​​project lifecycle has been adopted from an existing standard used in software development. Also, the approach takes into account the scientific challenges inherent in machine learning projects involving software development processes. The approach aims to ensure the quality of development. Each phase has its own goals and quality assurance criteria that must be met before the next stage can be initiated.

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