| Project Id
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BITS025F001518
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| Project Detail
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| Project Title
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Programmable SmartCrete: AI-Optimized Sustainable Binders with Digital Twin Integration
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Dr. Mukund Lahoti
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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|
|
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Email ID
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mukund.lahoti@pilani.bits-pilani.ac.in
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| URL for more info
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https://www.bits-pilani.ac.in/pilani/mukundlahoti/profile
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
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| Please comment how many you are supervising
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3
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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|
| If yes, please elaborate
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| Senior Supervision Team (RMIT)
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| Supervisor name and Title
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A/Prof. Srikanth Venkatesan
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School or Department (or company, if applicable)
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STEM
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|
|
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Email ID
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Srikanth.venkatesan@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/profiles/v/srikanth-venkatesan
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| a) Are you currently supervising a BITS or RMIT HDR student?
|
YES
|
| Please comment how many you are supervising
|
4
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| b) Have you supervised an offshore candidate before?
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YES
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| If no, what support structures do you have in place?
|
|
| If yes, please elaborate
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I have supervised six offshore candidates from India, Sri Lanka, Middle East.
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| Other Supervisors (BITS)
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| Supervisor name and Title
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Tejasvi Alladi
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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| Phone Number (Optional)
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+91-1596-255767
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Email ID
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tejasvi.alladi@pilani.bits-pilani.ac.in
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| URL for more info
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https://www.bits-pilani.ac.in/pilani/tejasvi-alladi/
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| Other Supervisors (RMIT)
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| Supervisor name and Title
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Dr. Lei Hou
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School or Department (or company, if applicable)
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STEM
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| Phone Number (Optional)
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+61 3 9925 9531
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Email ID
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lei.hou@rmit.edu.au
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| URL for more info
|
https://www.rmit.edu.au/profiles/h/lei-hou
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| Field of Research (For Codes)
|
| 3107~1050 | Microbiology | 10.00 |
| 4005~1048 | Civil Engineering | 45.00 |
| 400505 | Construction materials | 40.00 |
| 401401~1141 | Additive manufacturing | 30.00 |
| 401605 | Functional materials | 30.00 |
| 4602 | Artificial Intelligence | 30.00 |
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| Project Description
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|
The construction sector faces an urgent need for low-carbon, intelligent materials. Ordinary Portland Cement (OPC), responsible for 7–8% of global CO2 emissions, is highly carbon-intensive. Sustainable alternatives such as Limestone-Calcined Clay Cement (LC3), geopolymers derived from fly ash, slag, and metakaolin, and polymer-reinforced “polycrete” have emerged as promising substitutes. LC3 alone can reduce emissions by up to 40% while enabling the valorization of industrial wastes.
Future materials, however, must not only be sustainable but also adaptive and multifunctional. At the same time, artificial intelligence (AI) and data-driven approaches are transforming material development. Machine learning models now surpass conventional mix-design methods, accurately predicting both fresh and hardened properties. Recent studies further highlight the need for smart material modelling and digital twin integration to accelerate the adoption of eco-binders such as geopolymers.
This project will therefore focus on AI-optimized smart composites: developing sustainable cementitious binders with embedded functionalities, applying AI/ML to predict and optimize their performance, and integrating digital twin platforms for real-time monitoring and lifecycle management.
Project Aims and Scope
The primary aim is to create a new class of AI-designed smart cementitious composites that are low-carbon, multifunctional, and digitally integrated. Key objectives include:
Sustainable Smart Binder Formulation
AI/ML-Driven Materials Modeling
Smart construction and Advanced Manufacturing
Digital Twin and Monitoring Integration
Durability and Lifecycle Analysis
Scalability and Industry Pathways
These efforts integrate materials science, AI/ML, and digital engineering. The methodology will include advanced characterization (mechanical tests, microstructure analysis, electrical measurements), computational modeling (feature selection, model training, multi-objective optimization), and digital systems (sensor instrumentation, IoT data streams, twin software).
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| Project Deliverable/Outcomes
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The project will deliver:
Optimized AI-Designed Binders
Demonstration Smart Prototypes
High-Fidelity Predictive Models
Digital Twin Monitoring Platform
Durability and LCA Results
Knowledge Transfer and Impact: The project will generate at least four high-impact journal articles and two international conference presentations on AI-driven material design and digital twin integration. We will seek patents for innovative smart-composite formulations and digital monitoring methods. Through partnerships with BITS and RMIT, results will feed into curricula and industry workshops. Ultimately, the candidate will emerge with industry-ready expertise in AI-enhanced materials and smart construction technologies.
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| Research Impact Themes
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| SUSTAINABLE DEVELOPMENT AND ENVIRONMENT
| CIRCULAR ECONOMY, CLIMATE CHANGE AND DECREASED URBAN POLLUTION |
| AI/ML and Data Analytics / Data Science with a focus on applications/translation in | Infrastructure & Urban Development |
| ADVANCED MATERIALS, MANUFACTURING AND FABRICATION | SPECIALISED MATERIALS |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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INDUSTRY, INNOVATION, AND INFRASTRUCTURE
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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|
URBAN FUTURES
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| Which RMIT Program code will this project sit under?
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|
DR218 PhD (CivilEng)
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| Student Capabilities and Qualifications
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Eagerness to learn and research, Good English writing skills
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Knowledge of AI; Material characterization
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BTech/Mtech/MSc
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| Preferred discipline of Student
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| Additive Manufacturing, Manufacturing, Automation |
| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Civil Engineering, Structural Engineering |
| Computer Science |
| Construction Eng/Management and Materials |
| Materials, Composites, Material Science, Functional Materials, Mettalurgical Engineering |
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