Why is Machine Learning Important in Civil Engineering?

Data analytics and prediction has a vital role to play in the field of civil engineering. It is used for the analysis of data from survey forecasting how long concrete would last and much more. The expressions, rules and terms that are mentioned in the guidelines in IS codes are very complex for implementation in all those activities where a lot of data with many variables source from site investigations and laboratory tests are involved. In order to keep pace with the world and other engineering domains the construction industry is using Machine Learning and other interdisciplinary technologies for data management purposes. Read and find out why Machine Learning is important in the field of Civil Engineering.

Reducing risks of errors

In Machine Learning, computer algorithms are used – which automatically improve upon getting exposed to more information over a period. The errors in data management and prediction can be reduced significantly with the help of this interdisciplinary technology. Even a few years ago, this task involved a lot of accuracy but was also more error-prone due to human agencies involved in the process. With machine learning this has become more convenient.

Use of varied methods

Various methods are used in machine learning for grouping identifying and prediction of data. Decision Tree and Artificial Neural Network are some of the instances of these methods. Students of civil engineering can use concepts of machine learning at the beginning in small sized projects. These can include:

  • Using Sieve Analysis for determining type of oil
  • Knowing about the coefficient of thermal expansion
  • Getting an idea about the compressive strength of concrete, following some days
  • Classification of soil with the use of Plasticity Index and Liquid Limit
  • Using data of a building for energy variables estimation etc.

A few tools may also be used for ML projects like Google Co Labs, MATLAB, Anaconda and Weka. There can also be an implementation of Keras, Tensor Flow, Python and other libraries.

Higher scope of development

In the present age, a civil engineer with an idea about these interdisciplinary domains can be more successful in his profession and can also help the construction industry to develop more. In the previous year, the construction industry witnessed a lot of unemployment and different types of losses due to non-use of interdisciplinary technologies. It is, thus, a good time to begin preparing students of civil engineering right from the roots. The use of interdisciplinary technologies like machine learning should be used in the field of civil engineering.

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