XTRACTIS FOR civil engineering

(R&D, predictive maintenance)

Prediction of Concrete Compressive Strength

Benchmark vs. Random Forests, Boosted Trees & Neural Networks

How to successfully predict the compressive strength of high-performance concrete, given the hyper-complexity of the phenomenon (strongly nonlinear function of age and ingredients)?

Goals & benefits

Allow domain experts and civil engineers to understand the causal relationships between concrete parameters and its compressive strength.

Find the really influential parameters to anticipate the compressive strength of the concrete and thus optimize its production.

Create new custom-designed concretes for specific uses.

XTRACTIS RESULTS

We get a Predictive Model that is:

Intelligible.

A Decision System composed of 86 unchained gradual rules, each rule using some of the 8 variables that XTRACTIS confirmed as significant.

Robust.

Good Real Performance on External Test.

EFFICIENT & OPERATIONAL.

Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

full use case

Use Case 2023/03 (v1.1)

Results by
XTRACTIS® GENERATE 12.2.44127 (2023/01)

DOCUMENT CONTENTS

  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 2 cases
  5. Top-Models Benchmark