# Probabilistic selection and design of concrete using machine learning

arXiv: https://arxiv.org/abs/2304.11226 · HTML: https://mlsanity.com/paper/2304.11226/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 86 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Design of a nickel-base superalloy using a neural network](https://mlsanity.com/paper/1803.03039/) (2018)
- [Probabilistic design of a molybdenum-base alloy using a neural network](https://mlsanity.com/paper/1803.00879/) (2018)
- [Uncertainty-based information extraction in wireless sensor networks for control applications](https://mlsanity.com/paper/1401.0694/) (2014)
- [Unveil the unseen: Exploit information hidden in noise](https://mlsanity.com/paper/2209.08376/) (2022)

## Abstract (arXiv)

Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond.