Concrete is one of the most widely used materials in the construction industry. It is present in nearly every modern structure from roads, to bridges, to buildings. Concrete comes in different types. Each type has its own specific application. These different types come from the differing compositions and ratios of elements. Each type is designed to support different weights, compress more or less, and move in various ways. The introduction of artificial intelligence (AI) to the concrete industry is relatively new. Yet its impacts are already being felt. Civil engineers have been exploring methods of applying machine learning. The goals are to reduce human error, for damage detection, and to determine the correct proportions of concrete mixtures.
Applications in Structural Integrity
There have been various successful and innovative applications of AI in concrete. SmartRock is a sensor that is placed within a concrete framework on the rebar. When the concrete is poured, the sensor remains locked into the structure. It collects a series of data points around conditions and temperature. This data is then transmitted to a wireless application.
The primary use of this data is to understand the compressive strength of the concrete. Over time, as the temperatures and conditions change, the app can calculate the new values. The engineers can use these values to adapt accordingly.
Another application used is called Roxi. It uses the SmartRock data to predict how concrete may behave in future projects. This means a different project can have an already predicted timeline of compressive strength. The computer uses previous data points from a separate project with similar conditions. As more projects use this method of data collection, the predictions can get more and more accurate.
Applications in Damage Detection
One of the major issues within the construction industry is the potential damage that can happen to a structure. There are more obvious incidents of damage. These include earthquakes, strong storms, and vehicular crashes into buildings. There are also less obvious ones. These internal incidents of damage are some of the most dangerous. Oftentimes engineers cannot visibly see this damage and are unaware of its presence.
Structural health monitoring (SHM) is a method of analysis used in bridges. It uses computer software to analyze potential damage, model its severity, and predict how it will develop.
In a tester project, 5 cracks were mapped into a model of structural beams. The software was able to predict the location, depth, and width of the cracks as time progressed.
This application still needs time to become more accurate. But right now it serves as a rough estimate of cracks and damage in bridges.
Applications in Concrete Mixing
The strength and durability of concrete depend heavily on the mix that it is made of. Different combinations of elements produce concrete samples with different properties. Some concrete mixes use the addition of fibers. This is to increase the load-bearing ability of the concrete. But, the addition of the fibers impacts the workability of the concrete. The Concrete Research Council funded a project that applied statistical learning to the production of reinforced fiber concrete. The computer analyzed data from nearly 2,000 cases of this fiber concrete and the resulting properties.
Using this data, the computer can develop statistical models of what combinations of concrete will produce certain qualities. Having a database of averages makes fiber-reinforced concrete production more mathematical.
Engineers can understand which mixes will yield which results. They can then predict which types of concrete would be better for different projects. This database can also help predict the variability and sensitivity of the concrete.
Future of AI In Concrete
As technology continues to advance, the applications of AI in the concrete industry will continue to develop and refine. They have the potential to dramatically reduce human error in structural projects. They can be used to detect damage in the concrete used as it becomes older and weathered. This could lower the chance of collapse and potentially save lives.
The use of machine learning can effectively and efficiently analyze millions of data pieces.
It can then conduct real-life predictions based on trends and patterns. It can be used to assess potential performance and suggest which mixtures of concrete to use. AI will continue to advance the industry in ways we never imagined. Its applications can be revolutionary to an industry that we rely so heavily upon.
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