# From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties

arXiv: https://arxiv.org/abs/2312.03607 · HTML: https://mlsanity.com/paper/2312.03607/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 73 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Monte Carlo Gradient Estimation in Machine Learning](https://mlsanity.com/paper/1906.10652/) (2019)
- [Learning To Simulate](https://mlsanity.com/paper/1810.02513/) (2018)
- [Adversarial Variational Optimization of Non-Differentiable Simulators](https://mlsanity.com/paper/1707.07113/) (2017)
- [A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty](https://mlsanity.com/paper/2307.02432/) (2023)
- [Stochastic Variational Optimization](https://mlsanity.com/paper/1809.04855/) (2018)
- [A trust-region method for derivative-free nonlinear constrained stochastic optimization](https://mlsanity.com/paper/1703.04156/) (2017)
- [Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators](https://mlsanity.com/paper/2311.15137/) (2023)
- [ParaDRAM: A Cross-Language Toolbox for Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo Simulations](https://mlsanity.com/paper/2008.09589/) (2020)

## Abstract (arXiv)

Designing civil structures such as bridges, dams or buildings is a complex task requiring many synergies from several experts. Each is responsible for different parts of the process. This is often done in a sequential manner, e.g. the structural engineer makes a design under the assumption of certain material properties (e.g. the strength class of the concrete), and then the material engineer optimizes the material with these restrictions. This paper proposes a holistic optimization procedure, which combines the concrete mixture design and structural simulations in a joint, forward workflow that we ultimately seek to invert. In this manner, new mixtures beyond standard ranges can be considered. Any design effort should account for the presence of uncertainties which can be aleatoric or epistemic as when data is used to calibrate physical models or identify models that fill missing links in the workflow. Inverting the causal relations established poses several challenges especially when these involve physics-based models which most often than not do not provide derivatives/sensitivities or when design constraints are present. To this end, we advocate Variational Optimization, with proposed extensions and appropriately chosen heuristics to overcome the aforementioned challenges. The proposed methodology is illustrated using the design of a precast concrete beam with the objective to minimize the global warming potential while satisfying a number of constraints associated with its load-bearing capacity after 28days according to the Eurocode, the demoulding time as computed by a complex nonlinear Finite Element model, and the maximum temperature during the hydration.