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Artificial Neural Network and Kriging Surrogate Model for Embodied Energy Optimization of Prestressed Slab Bridges

Author

Listed:
  • Lorena Yepes-Bellver

    (Mechanics of Continuous Media and Theory of Structures Department, Universitat Politècnica de València, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Alejandro Brun-Izquierdo

    (School of Civil Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Julián Alcalá

    (Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Víctor Yepes

    (Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 Valencia, Spain
    These authors contributed equally to this work.)

Abstract

The main objective of this study is to assess and contrast the efficacy of distinct spatial prediction methods in a simulation aimed at optimizing the embodied energy during the construction of prestressed slab bridge decks. A literature review and cross-sectional analysis have identified crucial design parameters that directly affect the design and construction of bridge decks. This analysis determines the critical design variables to improve the deck’s energy efficiency, providing practical guidance for engineers and professionals in the field. The methods analyzed in this study are ordinary Kriging and a multilayer perceptron neural network. The methodology involves analyzing the predictive performance of both models through error analysis and assessing their ability to identify local optima on the response surface. The results show that both models generally overestimate the observed values. The Kriging model with second-order polynomials yields a 4% relative error at the local optimum, while the neural network achieves lower root mean square errors (RMSEs). Neither the Kriging model nor the neural network provides precise predictions but point to promising solution regions. Optimizing the response surface to find a local minimum is crucial. High slenderness ratios (around 1/28) and 40 MPa concrete grade are recommended to improve energy efficiency.

Suggested Citation

  • Lorena Yepes-Bellver & Alejandro Brun-Izquierdo & Julián Alcalá & Víctor Yepes, 2024. "Artificial Neural Network and Kriging Surrogate Model for Embodied Energy Optimization of Prestressed Slab Bridges," Sustainability, MDPI, vol. 16(19), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8450-:d:1487894
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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