Integrating electro-mechanical impedance data with machine learning for damage detection and classification of blended concrete systems

R Gomasa, V Talakokula, SKR Jyosyula… - Construction and Building …, 2024 - Elsevier
In recent years, the assessment of structural integrity and durability of concrete systems has
significantly advanced with the integration of novel sensing technologies and data-driven …

Exploring the association of ground motion intensity measures and demand parameters with ANN-based predictive modeling and uncertainty analysis

FM Wani, J Vemuri - Natural Hazards, 2025 - Springer
The study investigates the interdependency between the wide range of single parameter-
based ground motion intensity measures with demand parameters for low-rise reinforced …

High‐Fidelity Data Augmentation for Few‐Shot Learning in Jet Grout Injection Applications

PG Atangana Njock, ZY Yin… - International Journal for …, 2025 - Wiley Online Library
Contemporary geoengineering challenges grapple with the plateauing of both existing
algorithms and their depth of insights, a phenomenon exacerbated by the scarcity of high …

Forecasting duration characteristics of near fault pulse-like ground motions using machine learning algorithms

FM Wani, J Vemuri, KSKK Reddy… - … Research and Risk …, 2024 - Springer
The duration characteristics of near-fault earthquake ground motions play a significant role
in the dynamic response of a structure. Linear regression-based models are extensively …

Evaluation of Machine Learning Algorithms for Predicting Compressive Strength of Geopolymer Concrete at High Temperatures

A Gupta, P Sarda, FM Wani, J Vemuri - International Conference on …, 2024 - Springer
In recent years, fly ash and slag, both industrial by-products, have been integral to
sustainable geopolymer concrete, but their fire resistance lacks clarity. The complex …