M NIDHI
Also known as: Nidhi M
National Institute of Technology, Puducherry, Jyothi Engineering College, National Institute of Technology, College of Engineering Trivandrum, Federal Institute of Science and Technology
Research areas: Engineering · Civil and Structural Engineering · Concrete and Cement Materials Research · Innovative concrete reinforcement materials · Structural Behavior of Reinforced Concrete
0000-0002-0808-7361
About
Dr. Nidhi M. is an accomplished academician and researcher with over 10 years of experience in the department of Civil Engineering, currently working as an Assistant Professor at the National Institute of Technology Puducherry. Her Ph.D Thesis entitled “Strength and Behaviour of HFRHPC Beam-Column-Slab Joints under Reverse Cyclic Loading” won ICI - Ultra Tech award 2017 for the outstanding thesis in the field of concrete. She is also a mentor for Indo German Science and Technology Centre PhD Industrial Exposure (IGSTC - PIEF fellowship 2025 for the project titled "Experimental Assessment of High Temperature Behaviour of Hybrid Fibre Reinforced Geopolymer Concrete and Prediction of Compressive Strength using Explainable Machine Learning Approaches". She has successfully completed Coalition for Disaster Resilient Infrastrucre (CDRI) fellowship 2022-23 for the project titled "Machine Learning Based Unified Seismic Vulnerability Prediction Model for Heritage Minarets of India". Her area of reseach interest includes structural health monitoring, vulnerability prediction modelling, forensic engineering, sustainable composites, application of machine learning techniques in the prediction of concrete properties.
Employment
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National Institute of Technology Puducherry Assistant Professor2021 - Present
- 2020 - 2021
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Jyothi Engineering College Associate Professor2018 - 2020
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Federal Institute of Science and Technology Assistant Professor2016 - 2018
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National Institute of Technology Calicut Research scholar2012 - 2016
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Universal Engineering College Assistant Professor2011 - 2012
Education
- 2012 - 2017
- 2009 - 2011
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Nair Service Society College of Engineering BTech2005 - 2009
Projects & Funding
Projects & funding information is unavailable.
Publications (19)
- Mechanical and microstructural response of slag-sugarcane bagasse ash geopolymer fibrous concrete after elevated temperature exposure Save
- A Novel Hybrid Model for Predicting Strength Characteristics in Fiber-Reinforced Geopolymer Concrete Based on Extreme Gradient Boosting and Particle Swarm Optimization Save
- A Review of the Influence of Fibres on the Fracture Properties of Sustainable Geopolymer Concrete Save
- Assessing Seismic Risks in Heritage Slender Masonry Minaret: A Case Study of Chand Minar Save
- An interpretable machine learning approach with graphical user interface for strength prediction of sustainable slag-based geopolymer concrete Save
- Compressive strength prediction and feature analysis for GGBS-Based geopolymer concrete using optimized XGBoost and SHAP: A comparative study of optimization algorithms and experimental validation Save
- Development and optimization of geopolymer concrete with compressive strength prediction using particle swarm-optimized extreme gradient boosting Save
- Seismic vulnerability assessment of slender masonry structures: A machine learning approach with vibration-based assessment and nonlinear dynamic analysis Save
- Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites Save
- A comparative analysis of tree-based machine learning algorithms for predicting the mechanical properties of fibre-reinforced GGBS geopolymer concrete Save
- Fundamental frequency formulation and modeling of masonry slender structures: A comparative study of machine learning and regression techniques Save
- INNOVATIVE APPROACHES TO SUSTAINABLE CONCRETE: A PERFORMANCE STUDY OF NEURAL NETWORK AND TREE-BASED MODELS FOR COMPRESSIVE STRENGTH PREDICTION IN GEOPOLYMER CONCRETE WITH GGBS Save
- Soft computing techniques for predicting the compressive strength properties of fly ash geopolymer concrete using regression-based machine learning approaches Save
- The Use of Artificial Neural Network Model to Predict the Compressive Strength of Sustainable Geopolymer Concrete: A Systematic Review Save
- A review on the material performance of geopolymer concrete as green building materials Save
- Influence of Fibres on the Strain Hardening Behaviour of Ultra-High-Performance Geopolymer Concrete: A Review Save
- Critical reviews on topology optimisation of reinforced concrete Save
- HFRHPC interior beam-column-joints with slab under reverse cyclic loading Save
- SFRHPC interior beam-column-slab joints under reverse cyclic loading Save