Research on Hyperparameter Optimization of Concrete Slump Prediction Model Based on Response Surface Method

Chen, Yuan; Wu, Jiaye; Zhang, Yingqian; Fu, Lei; Luo, Yan; Liu, Yong; Li, Lindan

doi:10.3390/ma15134721

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Configuration of algorithms and baselines. We compare PGS against baselines including COMs (Trabucco et al 2021), NEMO (Fu and Levine 2021), ROMA (Yu et al 2021a), BDI (Chen et al 2022), BONET (Krishnamoorthy, Mashkaria, and Grover 2022b) and other baselines from design-bench (Trabucco et al 2022). We took the results for all baselines from their respective papers.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…We took the results for all baselines from their respective papers. Since NEMO and ROMA don't report normalized scores, and their original papers lack a few tasks, we take their results from the stateof-the-art BDI paper (Chen et al 2022). This is reasonable because all baselines use the same design-bench benchmark and evaluation methodology.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Offline Model-Based Optimization via Policy-Guided Gradient Search

Chemingui,

Deshwal,

Hoang

et al. 2024

AAAI

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Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize an unknown function given only its offline evaluation at a fixed set of inputs. A naive solution to this problem is to learn a surrogate model of the unknown function and optimize this surrogate instead. However, such a naive optimizer is prone to erroneous overestimation of the surrogate (possibly due to over-fitting on a biased sample of function evaluation) on inputs outside the offline dataset. Prior approaches addressing this challenge have primarily focused on learning robust surrogate models. However, their search strategies are derived from the surrogate model rather than the actual offline data. To fill this important gap, we introduce a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem. Our proposed policy-guided gradient search approach explicitly learns the best policy for a given surrogate model created from the offline data. Our empirical results on multiple benchmarks demonstrate that the learned optimization policy can be combined with existing offline surrogates to significantly improve the optimization performance.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Offline Model-Based Optimization via Policy-Guided Gradient Search

Chemingui,

Deshwal,

Hoang

et al. 2024

AAAI

View full text Add to dashboard Cite

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“…Finally, the predictive slump model, trained with 19,536 data instances, achieved R 2 = 0.84 and RMSE = 11.3 mm during 10-fold-cross-validation. In the study outlined in [35], modeling with a neural network on 103 data instances using a 70-30% train-test split yielded an R 2 of 0.95 and an RMSE of 2.781 cm (equivalent to 27.81 mm). We consider that the results are not directly comparable due to significant differences in the quantity of data.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete

Vargas,

Oviedo,

Ortega

et al. 2024

Applied Sciences

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The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes.

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