Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning

A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures

Recently, the demand for residence and usage of urban infrastructure has been increased, thereby resulting in the elevation of risk levels of human lives over natural calamities. The occupancy demand has rapidly increased the construction rate, whereas the inadequate design of structures prone to more vulnerability. Buildings constructed before the development of seismic codes have an additional susceptibility to earthquake vibrations. The structural collapse causes an economic loss as well as setbacks for human lives. An application of different theoretical methods to analyze the structural behavior is expensive and time-consuming. Therefore, introducing a rapid vulnerability assessment method to check structural performances is necessary for future developments. The process, as mentioned earlier, is known as Rapid Visual Screening (RVS). This technique has been generated to identify, inventory, and screen structures that are potentially hazardous. Sometimes, poor construction quality does not provide some of the required parameters; in this case, the RVS process turns into a tedious scenario. Hence, to tackle such a situation, multiple-criteria decision-making (MCDM) methods for the seismic vulnerability assessment opens a new gateway. The different parameters required by RVS can be taken in MCDM. MCDM evaluates multiple conflicting criteria in decision making in several fields. This paper has aimed to bridge the gap between RVS and MCDM. Furthermore, to define the correlation between these techniques, implementation of the methodologies from Indian, Turkish, and Federal Emergency Management Agency (FEMA) codes has been done. The effects of seismic vulnerability of structures have been observed and compared

Obesity: Pathogenesis and emerging targets for drug development

Obesity leads to premature death and impairs quality of life. In general, weight loss is responsible and reduced the risk of cardiovascular disease that is associated with obesity. One of the recognized and well establishment treatment option for weight loss i.e. pharmacotherapy where cardiovascular safety issues with some previous weight loss drugs raise concerns for newly approved pharmacotherapies. Previously, number of anti-obesity drugs that are already withdrawn from the market because of adverse side effects. In the present study, our group focused only on antiobesity drugs that are currently under investigation and also understand its pathogenesis.  Although lot of anti-obesity drugs are in the pipeline, the process for getting such type of drugs to be marketed has recently proven so difficult

Automated Lung Semantic Segmentation on X-Ray Using Convolutional Models

Towards the culmination of 2019, an outburst of coronavirus disease 2019 (COVID-19) pandemic struck mankind, which was instigated due to severe acute respiratory syndrome (SARS) which originally transpired from Wuhan City, China. Myriad of people have acceded to this disease. The effects of the pandemic have been more severe on the populous nations of the world. In India, although over 1.5 billion vaccinations have been provided to the inhabitants, as of 21 February 2022, the pandemic has barely diminished, as seen in Figure 1.1. While restrictions are being somewhat relaxed, the chances of the ominous ’4th wave’ materialises large. In such scenarios, it is of supreme importance to have apparatus for rapid testing and diagnosis of the disease, to expedite a much faster process. This paper will give an insight of a model that can efficiently and precisely predict the presence of COVID-19 by using a CT scan of the lungs

A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings

A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings

A vast number of existing buildings were constructed before the development and enforcement of seismic design codes, which run into the risk of being severely damaged under the action of seismic excitations. This poses not only a threat to the life of people but also affects the socio-economic stability in the affected area. Therefore, it is necessary to assess such buildings’ present vulnerability to make an educated decision regarding risk mitigation by seismic strengthening techniques such as retrofitting. However, it is economically and timely manner not feasible to inspect, repair, and augment every old building on an urban scale. As a result, a reliable rapid screening methods, namely Rapid Visual Screening (RVS), have garnered increasing interest among researchers and decision-makers alike. In this study, the effectiveness of five different Machine Learning (ML) techniques in vulnerability prediction applications have been investigated. The damage data of four different earthquakes from Ecuador, Haiti, Nepal, and South Korea, have been utilized to train and test the developed models. Eight performance modifiers have been implemented as variables with a supervised ML. The investigations on this paper illustrate that the assessed vulnerability classes by ML techniques were very close to the actual damage levels observed in the buildings

Proceedings of International Conference on Women Researchers in Electronics and Computing

This proceeding contains articles on the various research ideas of the academic community and practitioners presented at the international conference, “Women Researchers in Electronics and Computing” (WREC’2021). WREC'21 was organized in online mode by Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab), INDIA during 22 – 24 April 2021. This conference was conceptualized with an objective to encourage and motivate women engineers and scientists to excel in science and technology and to be the role models for young girls to follow in their footsteps. With a view to inspire women engineers, pioneer and successful women achievers in the domains of VLSI design, wireless sensor networks, communication, image/ signal processing, machine learning, and emerging technologies were identified from across the globe and invited to present their work and address the participants in this women oriented conference. Conference Title: International Conference on Women Researchers in Electronics and ComputingConference Acronym: WREC'21Conference Date: 22–24 April 2021Conference Location: Online (Virtual Mode)Conference Organizers: Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, INDI