Artificial Intelligence in Construction Industry
The distribution of artificial intelligence in construction is not very wide, but it still exists. There are many ways to implement AI for building, so let’s take a closer look at the most popular ones. Construction projects generate a set of data from daily reports, telematics, plans and specifications, punch lists, worker productivity levels, work costs, takeoffs, and quotations, change orders, site photos, finished products, RFI, and more.
The problem is that all this data is unstructured and the data is collected and stored both manually and digitally in disparate systems. In other words, once the project is complete, the data is often siled and unused. Collecting, structuring, and standardizing all data is one of the key issues that need to be addressed to maximize the potential of artificial intelligence in construction.
Future of Artificial Intelligence in Construction
Artificial intelligence in construction is no longer the future in our homes and pockets. Today, artificial intelligence is a loyal assistant to humans. But many science fictions, scientists, and the general public are wondering.
What if AI tomorrow becomes smarter than humans?
But nothing really changes. The pitfalls of the definition of “smart” are: Computers cannot solve problems by themselves and still need human help.
As a result, whatever the computer task, everything results in mechanical and intelligent decision making an optimal selection. But the construction industry is ready for meaningful digital changes.
There are a vast number of applications and software built on artificial intelligence and machine learning. You might not think the solution uses artificial intelligence because AI is about details.
As mentioned above, there are many ways to apply AI. Even though machine learning technologies seem very expensive, they are worth using and the results are not disappointing.
Benefits of Artificial Intelligence in Construction
Prevent Budget Overrun
Despite hiring the best project teams, most megaprojects are over budget. Artificial neural networks are used in projects to predict cost overruns based on factors such as project size, contract type, and project manager capability level. Historical data such as planned start and finish dates are used in predictive models to envision realistic timelines for future projects. Artificial intelligence in construction helps staff gain remote access to real-world training materials and quickly improve their skills and knowledge. This reduces the time it takes to onboard new resources into your project. As a result, project delivery is accelerated.
Artificial Intelligence in Construction Building Design
The modeling of building information is a process based on 3D models, which gives experts from the fields of construction, engineering, and architecture insights into the effective planning, planning, construction and management of buildings and infrastructures. To plan and design building construction, 3D models need to consider architectural, engineering, mechanical, electrical, and plumbing (MEP) planning, as well as a set of activities for each team. The challenge is to ensure that different models of sub-teams do not collide with each other.
The industry seeks to use machine learning in the form of generative design to identify and mitigate conflicts between different models generated by different teams during the planning and design stages and prevent redo. There is software that uses machine learning algorithms to explore all variations of the solution and generate alternative designs. Leverage machine learning to specifically create 3D models of mechanical, electrical, and plumbing systems while at the same time learning from each iteration to find the best fit while learning that the entire MEP system route does not conflict with the building’s architecture. Derive a solution.
Artificial Intelligence in Construction Project Planning
Launched in 2018, the AI startup has promised that robots and artificial intelligence will hold the key to solving low-budget construction projects. The company uses a robot to autonomously capture a 3D scan of a construction site and feed that data into a deep neural network to classify distances for various subprojects. If things don’t seem to be going smoothly, management can intervene to address small issues before they become major ones. Future algorithms will use an AI technique called “reinforcement learning.” This approach allows the algorithm to learn on a trial-and-error basis. You can evaluate endless combinations and alternatives based on similar projects. It helps you plan your project by optimizing the best paths and automatically fixing them over time.
Artificial Intelligence in Construction Safety
Since the construction industry is one of the most traumatic, various accidents carry considerable risk. Therefore, construction companies need to analyze key prerequisites and avoid any future accidents.
Accidents can be divided into groups: human and equipment related. Companies typically develop long policies and sets of rules to prevent all human-related problems. But still, people very often violate safety regulations.
Some open-source libraries analyze images and videos from safety cameras in real time. These tools can detect abnormal behavior and alert construction companies.
Equipment-related issues are also very common, as large companies often rely on personnel while maintaining sophisticated equipment. We recently created a solution (actually a custom survey application) to help city governments track equipment degradation. Simply put, a group of employees inspects equipment, takes pictures, uploads them, and fills out reports.
As already mentioned, there is a Python library that makes it easy to analyze images. Engineers train machine learning models to detect photo anomalies and avoid additional costs.
Since yesterday’s failure is today’s success, every company needs to store information about the most serious accidents it encountered during the development of previous projects. Data scientists investigated how this accident happened and found events that should be prevented.
The data can include not only documents but also images, audio, and video material. In other words, artificial intelligence can track and predict accidents and avoid harmful situations.
Big Data Processing
AI can process and analyze vast amounts of information. Self-learning algorithms can take hours from manually processing complex unstructured data to hours.
Method: Data scientists manually map and cleanse some data to create a so-called sample dataset. These datasets are used as initial data for machine learning. With these datasets, the model explores existing data for valuable insights.
Indeed, this type of work can be done by humans just as it was done in the 90s. There was a dedicated data analysis department that delved into the data to find the required correlation. But now, one script can do it without spending a lot of money and resources.
All that machine learning needs to do its job is well-prepared and reliable data. No matter how good human intelligence is, it’s a little difficult to digest big data without automatic processing.