Swapnil Nayan received his MS Dual Degree in Civil Engineering (CE). His research work was supervised by Prof. Pradeep Ramancharla. Here’s a summary of Swapnil Nayan’s thesis Damage estimation of reinforced concrete buildings using machine learning.
Over the years, earthquakes have been one of the leading causes of life loss and destruction of properties. Man-made structures like buildings, roads, dams and bridges are severely affected due to earthquakes. Bhuj earthquake of 2001 killed nearly 15,000 people and damaged around 3.5 lakh buildings. The major contributors to the loss of so many buildings were the poor construction and maintenance practices. To address the deficiencies that lead to such catastrophic damage, major reforms have been introduced to the Indian Standard codes and numerous studies have been conducted to prescribe methods to curtail the damages further.
These studies can broadly be classified into earthquake prediction studies and earthquake preparedness studies. Earthquake prediction studies can be used to minimize the loss to life. But over the years, the research on earthquake prediction hasn’t provided satisfactory results, and Earthquake prevention looks like a far-fetched dream. Hence, earthquake preparedness is of utmost importance to minimize earthquake damage. Earthquake preparedness involves the construction of new buildings in compliance with the latest building construction codes of the country as well as fixing the deficiencies of the existing buildings. Hence, identifying the vulnerabilities in a building is a very crucial step towards earthquake risk mitigation. When the vulnerable buildings of a region are identified, the seismic risk of the region can be determined, and actions to mitigate these risks can be taken. But the identification of vulnerable buildings in a region is a very resource and time-intensive process.
A quick method to identify vulnerabilities in the buildings of a particular region is Rapid Visual Screening (RVS). This method can be used to find common vulnerabilities in the buildings, and the entire process of RVS of a building can be completed within 30 minutes by a single, trained person. But many buildings may require a detailed investigation in order to assess its safety. Detailed investigation requires the modelling and analysis of the building in finite element software. This requires the time and energy of people who are proficient in such software along with powerful machines to run such software. These problems make detailed investigation, time and resource-intensive process as well as very expensive. Hence, there is a need for an efficient and effective method to perform this task.
This thesis explores how the task of detailed investigation can be carried out using machine learning without seismic analysis of buildings in finite element software. A case study using 1296 building models has been presented, and maximum inter storey drift has been used as a measure of damage to the buildings. Inter storey drift ratio for a floor is the ratio of the absolute value of the difference between the horizontal displacements of that floor and the floor below it to the height of the storey.