Impact of Machine Learning on Infrastructure Development
Many applications in civil engineering have made extensive use of machine learning, which has developed into a useful tool for resolving challenging engineering issues. Machine learning tools have become more accessible because of the quick increase in data availability, growing processing power, and streamlined programming techniques.
Machine Learning’s Use in Civil Engineering
In civil engineering, engineers have always tried to increase the effectiveness of traditional materials, solutions, and testing procedures. Recent developments in materials science and various composite materials have led to the introduction of challenging mathematical issues in the field of civil engineering. Consequently, it is not possible to carry out the conventional procedures for underpinning theories and testing methodologies. Throughout their service lives, these contemporary materials and solutions may be subjected to severe natural or artificial loading conditions, which could result in significant property loss and fatalities.
Numerous applications of machine learning (ML) may be found in modern culture, such as the ability to forecast, classify, and resolve challenging mathematical issues in the field of civil engineering. In recent decades, deep learning, fuzzy logic systems, neural networks, evolutionary computation, and image processing applications have all seen tremendous advancements in machine learning methodologies and approaches. Lately, academics have been paying particular attention to machine learning (ML) algorithms and they have also been successfully used to solve civil engineering challenges. For instance, providing information on autonomous, intelligent, and completely automated urban and regional planning, rainfall forecasting, hydrological issues, and the development of new technologies for use in engineering design, building, maintenance, and disaster relief.
The purpose of this special issue is to publish original research and review papers on the use of machine learning (ML) in civil engineering structures, as well as the use of contemporary testing rigs and solutions, with a focus on applications in structures subjected to extreme loading or environmental conditions.
Possible subjects consist of but are not restricted to
Machine Learning for contemporary fixes
Machine Learning for tracking structural health
Machine Learning to forecast the behavior of engineering materials
Machine Learning with high-loading
Machine Learning for civil engineering modeling
Challenges with categorization in civil engineering
Machine Learning in the field of civil engineering
Machine Learning to forecast hydrological issues
intelligently designed buildings and materials
clever materials and structural design
What makes machine learning crucial for civil engineers?
Prediction and data analytics are essential in the field of civil engineering. It is employed for much more than just data analysis from surveys to predict the lifespan of concrete. The terminology, regulations, and expressions listed in the IS coding guidelines are extremely difficult to implement in any activity that involves a large amount of data from laboratory testing and site investigations with numerous variables. The construction sector uses machine learning and other transdisciplinary technologies for data management in order to stay up with the rest of the world and other technical domains. Discover the significance of machine learning in the field of civil engineering by reading on.
Using a range of techniques
In machine learning, a variety of techniques are employed to classify, identify, and forecast data. Among these techniques are decision trees and artificial neural networks. When starting small-scale initiatives, civil engineering students can apply machine learning ideas. These may consist of:
Applying Sieve Analysis to ascertain the kind of oil
Being aware of the thermal expansion coefficient
Getting a sense of concrete’s compressive strength after a few days
Using the Plasticity Index and Liquid Limit to classify soil
estimating energy factors, etc., using building data.
A few other tools, like Weka, MATLAB, Anaconda, and Google Co-Labs, may also be utilized for ML projects. Additionally, Keras implementation is possible.
Greater range of growth
In the modern era, a civil engineer who has knowledge of these interdisciplinary fields can excel in his career and contribute to the growth of the construction sector. The absence of transdisciplinary technologies in the construction sector resulted in a number of losses and high unemployment rates in the previous year. Therefore, it is an excellent moment to start preparing civil engineering students from the ground up. In the subject of civil engineering, interdisciplinary technologies such as machine learning ought to be employed.
Artificial intelligence applications in top engineering
Artificial Intelligence has a wide range of possible uses in the field of civil engineering. Artificial intelligence (AI) has the potential to have a big impact on the industry by automating tasks, cutting waste, and improving product design, among other things. These are the engineering fields where AI shows the greatest promise
Smart construction design
Construction process orchestration
Smart cities
Construction 3D printing
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