Machine learning is a term to describe the ability of computers to learn without being directly programmed. It is transforming the way many industries do business by making them more efficient, providing actionable insights, and automating data-centric workflows. The construction industry is a prime example of a market that can benefit from intelligent automation. Machine learning applications can use the massive amounts of big data generated during the construction process and turn it into insights that can improve design, build efficiency, and operations.
Machine learning is a field in artificial intelligence that involves developing algorithms that can receive input data, perform a task for which they have been trained using various algorithms, and produce output data. This process of inputting data to train the system is called "supervised learning," while providing no prior knowledge or information is called "unsupervised learning."
The concept of machine learning is not new. However, it has grown in popularity and capabilities with the development of advanced algorithms, increased computing power, and the proliferation of data. With technological advancements over recent decades, companies have effectively outsourced tasks to computers, including processing power-intensive tasks such as purchasing. Today, the idea of outsourcing business processes to machines is no longer a novelty but a necessity.
The construction industry has the potential to benefit enormously from machine learning. The skills needed to design and build structures are not easily attained, making it challenging to find talent. In addition, the construction industry has been slow to realize the value that big data can provide - or at least, there have been few tools available to allow the industry to capitalize on big data. While other industries have used their data as a competitive advantage, the construction industry still lags behind. However, with help from machine learning, this increasingly digital and data-centric industry may find its competitive edge.
One of the most significant advantages that machine learning can provide to the construction industry is increasing the accuracy and speed of predicting cost overruns. Most budget forecasting and risk analysis are still done manually and based on past information, which can be affected by human error and data that is not comprehensive enough. When construction projects are consistently over budget, it is usually because the information available does not provide a full picture of what was being built.
Machine learning can be used to develop algorithms that process new data, which enables computers to learn and make predictions based on changing conditions. For example, an algorithm might receive information on several past projects to use to forecast a new project's cost. The algorithm might find that certain factors - such as labor costs, building codes, and the number of stories - predict cost overruns. By comparing this information to data from a new project, such as labor and material costs and the number of stories, the algorithm could predict how much it is likely to cost. If this forecast exceeds an acceptable amount, the project can be adjusted before construction begins.
In addition to improving cost forecasting, machine learning could be used to improve the investment return on construction projects. Machine learning tools can be trained with historical data and real-time data to help companies make decisions based on future demands, risks, and other relevant information. This type of analysis has been used widely to make supply chain-related decisions, such as finding a product's most cost-effective production location. However, it can also be used in real estate development.
For example, machine learning tools could forecast the volume of new office space needed in a city and recommend potential locations based on risk level and expected demand. This type of analysis can be done in real-time, allowing companies to find the most profitable sites for new office buildings. In addition, machine learning tools could be used to estimate how much demand there will be for new office space in one area compared to another.
One of the most common uses for machine learning in construction is in building information modeling. Building information modeling is how architects and other stakeholders create a 3D digital representation of a building, from the structure to the furniture and fixtures. This helps companies design buildings more efficiently and communicate design details with contractors and construction crews.
These processes can be automated through machine learning, reducing the time required to complete modeling. For example, machine learning tools can be used to generate the 3D models that represent a building design. Since these models can be compared with a data set for accuracy, they can be created more rapidly. In addition, these machine learning algorithms could help with tasks such as estimating the number of materials needed for a building, determining which designs will be most feasible, and directing construction crews.
Machine learning algorithms also offer a new approach to project management. In the past, project managers have relied on tools such as cost-benefit analysis and critical path analysis when planning construction projects. However, these tools are limited in that they cannot account for changes in the project environment.
For example, machine learning algorithms could be used to track how changes in demand, materials costs, and other factors can affect project duration. This type of analysis helps managers identify potential delays before they become serious problems. In addition, machine learning tools can identify which factors affect project timelines the most to help managers prioritize issues that need to be resolved quickly to avoid project stalls.
Machine learning also offers advantages in how it can help companies manage project scope. A machine learning tool could be trained to determine what changes in a project's scope will cause it to exceed the planned budget. This capability could help companies avoid costly scope creep.
If you are looking for new ways to increase your construction company's efficiency and profits, contact the experts at Bitstrapped. We can help you identify use cases for machine learning and bridge the expertise gap to implement these tools in your company. Let's transform your construction operation with the power of machine learning. A 30-minute discovery call is free.
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