In Civil Engineering, from analysing survey data to predicting the life of concrete and many more, the application of data analytics and prediction plays an important role in various fieldsof this discipline. The guidelines or prescribed rules and expressions available in IS Codes are too complex to implement in the case of tasks involving large numbers of data with multiple variables obtained from laboratory tests and site investigations. Moreover, the nature of these data keeps on changing over time depending upon the variables involved. To keep up with the growing world and other fields of engineering, the construction industry shall also come forward to utilize interdisciplinary technologies such as Machine Learning (ML) to manage their data. Machine Learning uses computer algorithms that improve automatically as they get exposed to more data over time.
“Construction gets a lot of flak over not documenting things, but I think that’s false. We’re incredible at documenting. We’re just terrible at putting it where it can be found and shared.” -Jeff Sample, Contechcrewmentioned in his blog on BIM 360. Platforms like Autodesk BIM 360 deals with sending, receiving, managing and updating documents during a construction project. It also uses machine learning models to offer prediction capabilities using Construction IQ. But knowledge of such software and platforms is neither available to every section of the industry nor is its knowledge provided to students pursuing the Civil Engineering program.
Machine Learning uses techniques for clustering (grouping), classification (identifying) and regression (prediction). Artificial Neural Network and Decision Tree are a few examples of such techniques. As an initial step, a Civil Engineering student can implement Machine Learning concepts in small projects such as findings oil type using sieve analysis, finding compressive strength of concrete after a specific number of days, finding the coefficient of thermal expansion, soil classification using liquid limit and plasticity index and estimation of energy variables using a building’s data. Some tools which can be used for ML projects are Weka, Anaconda, MATLAB and Google Colabs. Libraries such as Python, Tensor flow, and keras may also be used.
In today’s world, a Civil Engineer having knowledge of such interdisciplinary domains will be a step closer to not only his/her success in career but can also lead to the development of the construction industry. The past year has shown us that the lack of usage of interdisciplinary technologies in the construction industry has led to various losses and unemployment of many in this field. Its high time to start preparing individuals from the roots with the incorporation of such interdisciplinary technologies in the Civil Engineering discipline
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