Publication

Matching metaheuristics to machine learning for concrete drying shrinkage prediction toward cost-effective mix design

2026

Abstract

Empirical models have limited accuracy for coupled mixture-environment-geometry-time effects on concrete drying shrinkage. An interpretable machine-learning framework was developed using 42,099 records from the NU Shrinkage-Creep Database, benchmarking nine algorithms under Bayesian optimization and four metaheuristics. GA-LightGBM achieved the best performance, with an RMSE 78.4% lower than GL2000. Bayesian optimization favored GBDT and CatBoost whereas PSO favored MLP, RF, and AdaBoost. SHAP analysis identified drying duration as the dominant variable, revealed a threshold effect of elastic modulus near 30,000 MPa, and showed step-like temperature effects between 20 and 25 °C. External validation on 116 data points from three independent studies yielded R 2 = 0.9627, RMSE = 37.7 με, and MAE = 31.4 με. In two shrinkage-constrained mix-design cases, the surrogate-assisted optimizer yielded mixtures with unit material costs 19.1% and 10.3% lower than feasible baselines under 365-day shrinkage constraints, supported by a graphical user interface.

Official source

https://infoscience.epfl.ch/entities/publication/3d00a7f3-fca7-4571-a484-03d659ecfc78

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2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3d00a7f3-fca7-4571-a484-03d659ecfc78

About this result

Related concepts (32)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

User interface

In the industrial design field of human–computer interaction, a user interface (UI) is the space where interactions between humans and machines occur. The goal of this interaction is to allow effective operation and control of the machine from the human end, while the machine simultaneously feeds back information that aids the operators' decision-making process. Examples of this broad concept of user interfaces include the interactive aspects of computer operating systems, hand tools, heavy machinery operator controls and process controls.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

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Matching metaheuristics to machine learning for concrete drying shrinkage prediction toward cost-effective mix design

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