The application of Machine Learning (ML) is rising day by day in all fields of studies. As a geotechnical engineering enthusiast, I want to know what are the most widely used ML algorithms (based on various python libraries and R packages) that can be used to improve the state-of-the-art practices in geotechnical engineering. Moreover, what are (/ can be) some of the most key applications of ML in geotechnical engineering research and application? It would be great if someone can suggest some key skills regarding ML/Deep learning/Programming for a young researcher in geotechnical engineering field. Thank you.
Thank you so much Dauji Saha
for your valuable opinion. Surely, this will be helpful for me. However, can you suggest me some resources (if there are any) to practice model development centered around Civil Engineering related works? Actually, I want to get some hands-on experience of working with Civil related data in ML before diving into the real project with my own data since data collection is a hectic process sometimes. In this way, I can get a complete idea of the workflow and data processing which can help me to get an idea what can be expected and which kind of data I would need for a successful model development.Thank you again for your detailed reply.
Dear Abhijeet Acharjee Jeet
These links might be useful, have a look:
1. http://mobile.kwc.edu/how_to_do_normal_distribution_on_minitab.pdf
2. https://www.science.gov/topicpages/p/python+scripting+language.html
3. Article Geospatial Analysis by Python and R: Geomorphology of the Ph...
Kind Regards
Qamar Ul Islam
Thank you so much dear Qamar Ul Islam for suggesting the resources. I will surely go through them.
The machine-learning algorithm could deal with Regression and Classification work. in research of traditional civil engineering, lots of prediction models are established to handle the various problems. Therefore, the machine-learning algorithm or data mining methods provide a new strategy to establish the mentioned above models. for example, the landslide displacement prediction, is also one of my research interests. the inducing factors of the landslide could be regarded as features in ML theory, and the landslide displacement also is regarded as labels. the consequent thing is to use different ML algorithms to train the model and other data to evaluate the accuracy of the prediction model. the svm, bpnn, rf and elm ML algorithms are widely used in Civil Engineering. with the development of Deep Learning, LSTM, CNN and other improve Deep learning algorithms are utilized to handle the prediction issue of big data. the sklearn module of Pythone is the best tool to establish ML models from my view.
by the way, I think that ML is just a tool to solve the problem and the analysis of failure or deformation mechanism is the most important.
Thank you so much dear Zhiyong Fu for your insightful comment. I will surely go through your works on landslide displacement prediction modelling as it seems really interesting. Moreover, thanks for mentioning the names of the commonly used ML algorithms in civil engineering field. It will help me to explore more. I also agree that sklearn is a very good module to develop the models. Can you suggest me some sources where I can find some real civil engineering datasets for ML applications or some resources of ML which solely focuses on civil engineering related data to better understand the processes?
it's a pity that I don't find any datasets for the application of civil engineering. i just can recommend some literature about the application of ML in Civil engineering. Article Application of back-propagation neural network on bank destr...
Article A deep learning algorithm using a fully connected sparse aut...
https://www.sciencedirect.com/science/article/abs/pii/S0341816219305685
Conference Paper Machine learning techniques for structural health monitoring
Article Supervised Machine Learning Techniques to the Prediction of ...
https://www.sciencedirect.com/science/article/abs/pii/S0886779820306532
The papers seem very interesting. Thank you dear Zhiyong Fu for providing me with these great papers. I believe they will be very helpful for me to get started.
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