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We have always been striving to build technologies that could imitate human intelligence. That’s the reason we have AI at the forefront today, ready to help the industries in turning their aspirations into realities.
In construction, AI broadly covers two areas – deep learning and machine learning. Deep learning is a subfield of machine learning based mostly on neural networks. Though in a nascent stage of development, the technology will find its applications in structural health monitoring, assessment of building materials, construction site safety, building occupancy modeling, and energy demand prediction.
Currently, the applications of deep learning in this field are scarce compared to other digital technologies like machine learning (ML) and BIM.
Machine learning is a subset of artificial intelligence which involves unique statistical algorithms that automatically learn and improve from data without any human help and explicit programming. The implementation of artificial intelligence includes machine learning as intelligent behavior requires extensive information or knowledge.
With ML, machines can learn and predict outcomes on their own. Rather than a person programming them, they use algorithms with software that enable forecasting based on the analysis of data. For instance, a machine can tell you that it needs preventative maintenance without the need for manual inspections.
In construction, machine learning can help project engineers, supervisors, and everyone else involved in a project. ML can help monitor the work progress, assess the risks involved, notify the managers and supervisors of critical issues, improve the design and planning activities, and make informed predictions for a more streamlined workflow.
Applications of Machine Learning in Construction
1. Better Designs
Machine learning can improve designs to make them more suitable for the end-user. For example, if a firm wants to customize its office space based on its specific needs, ML can help predict the frequency of use for each room and present a design that is apt for the needs of the people.
2. Safer Jobsites
The adoption of ML on construction sites can take the level of safety to new heights. It can be used to identify, assess, and instantly report any anomaly detected.
Smartvid.io, an ML platform, uses visual and audio data emerging from a construction site for identifying safety hazards, making it possible to hold safety briefings to eliminate elevated danger and improve overall safety on construction sites.
3. Assess and Mitigate Risks
The application of ML has made risk assessment faster and way more accurate. Construction projects are usually complex and generate a great amount of data; ML programs can look through it to come up with precise and detailed risk assessments.
An Autodesk product called Construction IQ helps projects manage and mitigate risk on a daily basis and improve performance in real-time. The software has been exposed to millions of construction issues and observations, inspection data, building information models, change orders, and project outcomes, which helps understand the risks and gain actionable insights.
4. Boost productivity
When ML software is used in a construction project, it increases the level of productivity. The software solutions can monitor and supervise daily operations on sites like brickwork, concrete pouring, plumbing works, electrification, flooring, roofing, etc.
Triax Technologies Inc. has been helping project teams manage their workforce in real-time through their IoT-enabled Spot-R system. The system allows users to view the current location of workers right in their 3D models and 2D drawings. This data is then made available for machine learning algorithms to track productivity and suggest targeted improvements.
5. Predictive Maintenance of Equipment
Today, there are end-to-end systems that use machine learning and sensors to detect and alert companies of fluctuations in equipment vibration or temperature, with no machine learning or cloud experience required. Construction companies can use these systems to remotely maintain and update their fleet of sensors without ever having to physically touch them.
6. Computer-Vision-Powered Anomaly Detection
To enhance quality at construction sites, companies are now looking towards computer vision to provide greater speed and precision in identifying defects consistently.
ML provides high accuracy, low-cost anomaly detection solutions that can process thousands of images an hour to identify defects and aberrations, and then report the images that differ from the standard in order to take appropriate action.
7. Forecasting for Supply Chain Optimization
ML can help construction organizations foresee the future by analyzing time-series data and providing accurate forecasts, thereby reducing operating expenses and inefficiencies, ensuring higher resource and product availability, delivering products faster, and lowering costs.
Machine learning is a subset of artificial intelligence which involves unique statistical algorithms that automatically learn and improve from data without any human help and explicit programming.
Deep learning is a subfield of machine learning based mostly on neural network.
ML is used to monitor the work progress, assess the risks involved, notify the managers and supervisors of critical issues, improve the design and planning activities, and make informed predictions for a more streamlined workflow.