AI-Powered Comprehensive Framework for UHPC: From Material Optimization to Seismic Resilience of Bridges
A multi-scale AI framework spanning UHPC mix optimization, constitutive modeling, performance-based seismic design, and bridge resilience assessment.
Overview
This project develops a comprehensive AI-powered framework that addresses ultra-high-performance concrete (UHPC) across multiple scales, from material optimization to structural resilience.
Multi-Scale Research Pipeline
Novel Contributions
19-Objective UHPC Optimization
Multi-objective framework simultaneously optimizes compressive strength, cost, and environmental impacts of UHPC mixes.
Generative AI for Constitutive Modeling
Hybrid ML with CTGAN for data augmentation enabling accurate peak and ultimate stress-strain prediction of confined UHPC.
First UHPC Drift Limit States
Explainable ML-powered drift ratio limit states for UHPC bridge columns, proposed for the first time.
Bridge-Specific Vulnerability
Hybrid ML-enabled multivariate probabilistic seismic demand model for bridge-specific fragility and resilience assessment.
Technology Stack
Related Publications
A Novel Framework for Developing Environmentally Sustainable and Cost-Effective UHPC
Wakjira, T.G., Kutty, A.A. & Alam, M.S.
Construction and Building Materials, 416, 135114
Peak and Ultimate Stress-Strain Model of Confined UHPC Using Hybrid ML with Conditional Tabular GAN
Wakjira, T.G. & Alam, M.S.
Applied Soft Computing, 154, 111353
Hybrid ML Model and Predictive Equations for Compressive Stress-Strain Constitutive Modelling of Confined UHPC
Wakjira, T.G., Abushanab, A. & Alam, M.S.
Engineering Structures, 304, 117633
Performance-Based Seismic Design of UHPC Bridge Columns with Design Example
Wakjira, T.G. & Alam, M.S.
Engineering Structures, 314, 118346
Hybrid ML-Enabled Multivariate Bridge-Specific Seismic Vulnerability and Resilience Assessment of UHPC Bridges
Wakjira, T.G. & Alam, M.S.
Resilient Cities and Structures
Interested in This Research?
For code access, collaboration opportunities, or questions about this project, please contact the PI (Dr. Tadesse Wakjira) directly.