# CAREER: Physics-Informed, AI-Driven Prediction and Optimization of Concrete for Resilient Infrastructure

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · William Marsh Rice University (TX) · $669,852

## Abstract

This Faculty Early Career Development Program (CAREER) award will advance the predictive design of concrete, the most widely used construction material, by developing new data-driven approaches to improve performance, cost efficiency, and resource utilization. Concrete production currently relies on empirical, trial-and-error methods that often lead to suboptimal mixtures and increased costs. These limitations are becoming more significant as the industry incorporates diverse supplementary materials derived from industrial byproducts and natural resources, introducing greater variability and complexity. This project will integrate data science with fundamental materials science to enable faster, more reliable, and more adaptable concrete design. By supporting the use of locally available materials and reducing reliance on standardized formulations, the work will enhance efficiency and flexibility in infrastructure development. The project also integrates research and education through a novel design challenge that engages undergraduate and high school students in solving real-world engineering problems while developing skills in data science and materials design. These efforts will expand participation in science and engineering and contribute to a future-ready workforce aligned with national priorities.

The project will develop physics-informed, data-driven frameworks for the predictive and inverse design of blended cement concrete systems. The research will: (1) build a large-scale, open-access data infrastructure through automated literature mining and guided experiments; (2) develop mechanistic descriptors of binder reactivity and porosity by integrating atomistic simulation, diffraction-based characterization, thermodynamic modeling, and machine learning; and (3) incorporate these descriptors into predictive models to enable accurate property prediction and multi-objective optimization of concrete mixtures. These models will support rapid identification of o

## Key facts

- **NSF award ID:** 2543840
- **Awardee organization:** William Marsh Rice University (TX)
- **SAM.gov UEI:** K51LECU1G8N3
- **PI:** Kai Gong
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** INFRASTRUCTURE MATERIALS, CAREER-Faculty Erly Career Dev
- **Estimated total:** $669,852
- **Funds obligated:** $669,852
- **Transaction type:** Standard Grant
- **Period:** 08/01/2026 → 07/31/2031

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2543840

## Citation

> US National Science Foundation, Award 2543840, CAREER: Physics-Informed, AI-Driven Prediction and Optimization of Concrete for Resilient Infrastructure. Retrieved via AI Analytics 2026-07-17 from https://api.ai-analytics.org/grant/nsf/2543840. Licensed CC0.

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