Artificial Intelligence & Structural Integrity Analytics Lab (AISIA)
Embedded Files
Past Grants
Title: Advanced Energy Management and Data fusion for Smart Homes Grids/ Heating and Cooling and Predictive maintenance using AI. Grantor: Liteon Singapore, Amount $2.06 Million Dollars. 2024. (Technical Disclosure/ Patent filed).
Title: Evaluation of Piezo Sensors for effective use in Health Monitoring of Engineering Structures. Young Scientist, Fast track scheme. Nov 2011 - Nov 2014, [Grant No: SERC/ ET -0324/2010], Dept. Science & Technology, Ministry of Science & Technology, New Delhi, India, US $ 46,000, (INR 20.50 Lakhs), (Grant turned down as I moved from India to Singapore to expand my objectives to include components of mechanical structures with the help of Assoc. Prof. Pang Hock Lye, John, School of Mechanical & Aerospace Engg, NTU, and filed a Singapore Patent).
Details of Filed Singapore Patent Title: Electromechanical Charge Density (EMCD) and Wave Form Propagation (WFP) Methods for Diagnostics of Engineering Materials, Components and Structures.
Inventors: Venu Gopal Madhav Annamdas and Pang Hock Lye John (School of MAE, NTU Singapore). Interview Date: 16 May 2013, Lab Demonstration Date: 22 May 2013. Status: Patent Filed by A Star, [ETPL Ref.: SRC/Z/07571, NTU Ref.: TD/194/12. Protection Period: 14 Months. A*STAR SERC Grant No.: 112 290 4014], Singapore.
Clients: Singapore Mass Rapid Transit (SMRT) Railways, Continental Automotive Germany, A Star Agency Singapore, Defense Science & Technology Agency (DSTA) of Singapore, Singapore Prime Minister Office, Nanyang Technological University Singapore
Past Industry & Singapore Government Projects: Details
Past Industry & Singapore Government Projects: Details
1. Singapore Railways (SMRT): Problem & Existing Solution
Proposed Solution by us
2. Continental Automotive Germany (& Singapore): Cover Lens -Display Screens: Piezoelectric for Haptics Application in Cars (Application of Piezoelectric Sensors for Touch application)
[Experimental Trials]
Problem, Proposed Solution & Application
3. Email Multi-classification and Email Ranking model based "AI-Driven Eco-Friendly Email Management for Enhanced Productivity in the Automotive Industry”
Problem & Existing Solution
Problem & Existing Solution
Proposed Solution
Proposed Solution
Links of the Project:
https://github.com/venuannamdas/AI_Clean_Email_Modeling
Dashboard: Smart Continental Company details across the globe (loss details) https://raw.githack.com/venuannamdas/E_clean_AI/master/employee_wastage_map.html
4. A project on Prediction of Loan Default using AI modeling has been proposed. The link to the model is available on Github.
https://github.com/venuannamdas/Singapore_Bank_Loan_Project
Dashboard: Financial loss incurred by a representative bank on theMap:
Problem & Proposed Solution
Problem & Proposed Solution
5. Regression model to predict "median house value in California
https://github.com/venuannamdas/mini_proj2/blob/master/2_1.ipynb
Dashboard: House estimates at different locations.
https://raw.githack.com/venuannamdas/12july/main/Housing%20Estimates%20in%20California.html
6. Stroke a social awareness page- (classified model)
https://github.com/venuannamdas/strokeXmeeti
Stroke info on the map, Vienna, Austria .
https://raw.githack.com/venuannamdas/strokeXmeeti/master/STROKE_VIENNA_MITALY_SINGAPORE_VENU.html
7. Covid19 info details of selected countries and Linear regression models
https://github.com/venuannamdas/mini_proj/blob/master/
On the map:
8. Smart Sensor for Monitoring Excavation of Mass Rapid Transit Station in Singapore (Singapore Government Project Funded by Defense Science Technology Agency)
Smart Sensor for Monitoring
Smart Sensor for Monitoring
The safety and reliability of the Civil infrastructures remains the primary challenge and concern. Especially, for those structures which involve human traffic and huge investments such as Mass Rapid Transit (MRT) stations, Flyovers, Bridges. Therefore, there is a compelling need to have high quality online structural health monitoring (SHM). The development of a real-time, in-service and smart material based SHM method has recently attracted the interest of a large number of academic and industrial researchers. In the recent past, piezoceramic (PZT) transducer has evolved as an efficient smart material which is usually employed in electro mechanical impedance (EMI) and guided ultrasonic wave propagation techniques. The EMI models attempted to detect loadings on, and damages in, the structures to be monitored. This book reviews some of the advancements in the field of PZT based SHM made over the past two decades in engineering. It presents the research carried out in load monitoring using EMI technique with DSTA funding to Singapore's research team in Nanyang Technological University. Finally it presents, WORLD's first Application of PZT for MRT Excavation monitoring in Singapore.
Journal: https://doi.org/10.1002/stc.418
9. Electromechanical impedance of Smart materials especially piezoelectric transducers for monitoring metallic and non-metallic structures: A review of wired, wireless and energy-harvesting methods (Topmost downloaded article of the Year 2013)
Journal: Most Downloaded Papers 2013
Electromechanical impedance–based structural health monitoring method had attracted several researchers in the recent past for aerospace, civil, mechanical, timber and biological structures. Smart materials such as piezoelectric (lead zirconate titanate as shown in Figure) are either surface bonded or embedded inside the host structure to be monitored. These smart materials with an applied input sinusoidal voltage interact with the structure, to sense, measure, process and detect any change in the selected variables (stress, damage) at critical locations. These can be categorized as wire-based ‘advanced non-destructive testing’, wireless-based ‘battery-powered lead zirconate titanate/macro fibre composite’ and energy harvesting–based ‘self-powered lead zirconate titanate/macro fibre composite’ methods. Most importantly, the effectiveness of these electromechanical impedance–methods can be classified into active and passive based on the properties of the material, the component and the structure to be monitored.
10. Characterization of Smart PZT Transducer and Admittance Signatures using PZT-Structure Interaction Models for Structural Health Monitoring
10. Characterization of Smart PZT Transducer and Admittance Signatures using PZT-Structure Interaction Models for Structural Health Monitoring
The monitoring of existing aero, civil and mechanical (ACM) structures has become a regular feature after the world witnessed the various recent deadly failures and damages, due to natural calamities or continuous usage of structures causing wear and tear. The last few years have witnessed rapid development in the areas of nondestructive evaluation (NDE) based Structural health monitoring (SHM) by the emergence of the electromechanical impedance (EMI) technique. This technique employs piezoelectric-ceramic (PZT) transducers for the prediction of structural response known as electromechanical (EM) admittance. Engineering structures can be classified into two categories based on their stiffness, those which are more stiffer and those which are less stiff than the PZT material. Surface bonded and Embedded PZT transducers are more efficient when they are more stiffer and less stiff than the host structure respectively. Both types of PZT transducers are important in the EMI based NDE of the two categories of engineering structures. However, surface bonded PZT transducers have seen more prominent applications in the recent past in SHM. This research developed both surface bonded and embedded PZT- structure interaction models for SHM of existing and future ACM structures.
The monitoring of existing aero, civil and mechanical (ACM) structures has become a regular feature after the world witnessed the various recent deadly failures and damages, due to natural calamities or continuous usage of structures causing wear and tear. The last few years have witnessed rapid development in the areas of nondestructive evaluation (NDE) based Structural health monitoring (SHM) by the emergence of the electromechanical impedance (EMI) technique. This technique employs piezoelectric-ceramic (PZT) transducers for the prediction of structural response known as electromechanical (EM) admittance. Engineering structures can be classified into two categories based on their stiffness, those which are more stiffer and those which are less stiff than the PZT material. Surface bonded and Embedded PZT transducers are more efficient when they are more stiffer and less stiff than the host structure respectively. Both types of PZT transducers are important in the EMI based NDE of the two categories of engineering structures. However, surface bonded PZT transducers have seen more prominent applications in the recent past in SHM. This research developed both surface bonded and embedded PZT- structure interaction models for SHM of existing and future ACM structures.
Links: Thesis, Presentation Slides
Highlights of Selected AI Projects
Highlights of Selected AI Projects
A project on Prediction of Loan Default using AI modeling has been proposed. (Click on different colour bars with Zoom-in and Zoom-out options to view clearly the Data insights)
Email Multi-classification and Email Ranking model based "AI-Driven Eco-Friendly Email Management for Enhanced Productivity in the Automotive Industry” (Click on different Indicators with Zoom-in and Zoom-out options to view clearly the Data insights)
Regression model to predict "median house value in California" (Click on different Indicators with Zoom-in and Zoom-out options to view clearly the Data insights)
Stroke a social awareness page - [classified model] (Click on NUMBERS with Zoom-in and Zoom-out options to view clearly all other numbers and the Data insights).
Covid19 info details of selected countries and Linear regression models (Click on different Indicators with Zoom-in and Zoom-out options to view clearly the Data insights)
Calling All AI Enthusiasts: Launch Your Career in Our Cutting-Edge AI Lab!
Calling All AI Enthusiasts: Launch Your Career in Our Cutting-Edge AI Lab!
Are you passionate about Artificial Intelligence and eager to gain real-world experience? Do you dream of working on groundbreaking projects alongside leading researchers? Then look no further!
Our AI Lab at [ERI@N, NTU] is offering exciting opportunities for highly motivated students to participate in:
Unpaid Online Internships: Work remotely with our team on exciting AI projects, gaining valuable experience and honing your skills.
Self-Financed Final Year Projects: Conduct your final year project within our state-of-the-art AI Lab, tackling a problem that sparks your passion and pushing the boundaries of the field.
Why Choose Our AI Lab?
Work on Real-World Projects: Contribute to cutting-edge research with practical applications, making a difference in the field.
Learn from the Best: Gain mentorship from leading AI researchers and professors, expanding your knowledge and expertise.
Flexible Opportunities: Choose an online internship or self-funded final year project that fits your schedule and interests.
Boost Your Resume: Gain valuable experience that sets you apart from the competition in the competitive AI job market.
Who We're Looking For:
Highly motivated students with a strong interest in Artificial Intelligence.
Excellent problem-solving and analytical skills.
Strong programming abilities (experience with Python, TensorFlow, etc. a plus).
A desire to learn and collaborate in a dynamic research environment.
Ready to Take the Next Step?
If you're a passionate and dedicated student with a thirst for knowledge, we encourage you to apply!
To Apply:
Send a resume and cover letter outlining your AI interests and project ideas (for final year projects) to [Dr. Venu Gopal Madhav Annamdas].
NTU_RECORD.gifPage updated
Google Sites
Report abuse