12 Jun 2021
School of Civil Engineering
Report on Webinar “Introduction to Business Analytics/ Data Science”
Venue: MS Teams, Reva University
Date: Thursday, 12th June 2021
Coordinator: Mrs. Minakshi Mishra
Title of the event: “Introduction to Business Analytics/Data Science”
The School of Civil Engineering, REVA University-Bangalore has organized a webinar on topic of Introduction to Business Analytics/Data Science on 12th June 2021 at 11.30am to 12.30pm. This event had a motive to impart knowledge on the out of curriculum context domains of civil engineering.
Director, Awesome Stats Consulting and Freelance Consultant.
Alumnus of IIM Bangalore.
The main take away of this webinar were key fundamentals aspects of Introduction to Business Analytics/Data Science. There was overwhelming response for the webinar evident through 80 plus participants.
Students and faculty coordinators on behalf of School of Civil Engineering, Thank Honourable Chancellor Dr. P. Shyama Raju sir and Vice Chancellor Dr. M. Dhanamjaya sir.
We also extend our acknowledgement to our Director Dr. Y. Ramalinga Reddy sir, School of Civil Engineering and also our Asst. Director Dr. Rajasekhara S L.
We also thank all the faculties of School of Civil Engineering, REVA University for their continuous support in organizing the events.
Data Science Usage in Civil Engineering:
Data is taking over almost each and every sector, even the ones that you may not immediately relate it to. One of those is construction/civil engineering. Today, however, that data is complemented by a huge amount of information generated by sources such as building engineering logs, cranes, construction workers, earth movers and materials logistics. Before AI revolution, construction software did a great job at recording static information, such as Computer Aided Drafting (CAD) schematics, expenses, invoices, employee details, and project details. Today, on the other hand, construction professionals and civil engineers need the kind of answers hidden in unstructured data, like free-form text (for example email and Word documents), printed documents and analog sensor data.
Through AI-powered algorithms, the civil sector is overcoming setbacks it used to face, thus improving productivity and overall efficiency. By making project development faster and more cost-effective, Machine learning has made a niche for itself in the civil sector. The construction industry is at a point where it is poised at a technological breakthrough in its processes. Accordingly, investing in artificial intelligence is bound to provide a significant edge to anyone who’s building a career in civil engineering.
The civil construction sector has a net worth of more than $10 trillion a year and data science has been playing its part here too.
Construction firms utilize deep-learning techniques to improve the level of quality of their construction processes. Image recognition of pictures collected through drones is used to identify risk areas and is compared against current blueprints to detect any possible construction defects. Further, through reinforcement learning, AI algorithms also makes use of trial and error techniques to identify the best processes to be followed. Stakeholders can also make use of neural networks and laser-generated images to gain insights on the progress of individual construction projects
Optimizations in design
Civil construction firms and contractors are also making full use of AI-powered recommendation systems. These systems use supervised learning to study design charts to suggest relevant improvements. For instance, suggestions can be made in the choice of bolted or welded connections, choice of architectural finishes, etc. As a result, construction firms have a more planned approach about which design elements will best suit a project at any given point in time.
Machine learning, a subset of AI can be used to maintain construction processes through the use of robotic arms. Civil engineers run simulations on common maintenance processes that can teach these robotic arms to do essential support functions with pin point accuracy. Through input data from all other sources, the AI algorithms can take care of process maintenance, thus ensuring a ceaseless project flow. For example, depending on the project timeline and execution speed, AI bots can also take care of inventory management to make sure that there’s no pause in the construction process.
Artificial neural networks (ANN) through artificial intelligence are proving to be useful measures for risk control as they interpret a collection of construction information to draw meaningful conclusions. ANN help construction firms to predict the likelihood of possible failures, thus preparing them to come up with appropriate contingency plans.