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Neural network ensemble-based parameter sensitivity analysis in civil engineering systems

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Abstract

The use of artificial neural networks for parameter sensitivity analysis in civil engineering systems is an emerging research focus of increased interest. Existing methods are generally based on a single neural network, but are inadequate as a basis for parameter sensitivity analysis because of the instability of a single neural network. To address this deficiency, this study develops a neural network ensemble-based parameter sensitivity analysis paradigm. This paradigm features use of a set of preselected superior neural networks to make decisions about parameter sensitivity by synthesizing sensitivity analysis results of individual neural networks. The proposed paradigm is employed to address two classic civil engineering problems: (1) identification of critical parameters in the fracture failure of notched concrete beams and (2) recognition of the most significant parameters in the lateral deformation of deep foundation pits. The results show that tensile strength and modulus of elasticity are the critical parameters in the fracture failure of the notched concrete beam, and elasticity modulus of soil, Poisson’s ratio and soil cohesion are the most significant influential factors in the lateral deformation of the deep foundation pit. The proposed method provides a common paradigm for analysing the sensitivity of influential parameters, shedding light on the underlying mechanisms of civil engineering systems.

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Acknowledgments

This study was partially supported by the Key Program of National Natural Science Foundation of China (Grant No. 11132003), and the Projects (Grant No. LH14334 and Grant No. LO1408) under the National Sustainability Programme I, granted by the Ministry of Education of the Czech Republic.

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Authors and Affiliations

  1. Department of Engineering Mechanics, College of Mechanics and Materials, Hohai University, Nanjing, 210098, People’s Republic of China

    M. S. Cao, L. X. Pan & Z. C. Ding

  2. Institute of Fluid-Flow Machinery, Polish Academy of Sciences, 14 Fiszera St., 80-231, Gdańsk, Poland

    M. S. Cao

  3. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, 210098, People’s Republic of China

    Y. F. Gao

  4. Faculty of Civil Engineering, Institute of Structural Mechanics, Brno University of Technology, 60200, Brno, Czech Republic

    D. Novák & D. Lehký

  5. Department of Civil Engineering, College of Civil and Transportation Engineering, Hohai University, Nanjing, 210098, People’s Republic of China

    X. L. Li

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  1. M. S. Cao
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  2. L. X. Pan
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  3. Y. F. Gao
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  4. D. Novák
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  6. D. Lehký
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  7. X. L. Li
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Correspondence to M. S. Cao.

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Cao, M.S., Pan, L.X., Gao, Y.F. et al. Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Comput & Applic 28, 1583–1590 (2017). https://doi.org/10.1007/s00521-015-2132-4

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  • DOI: https://doi.org/10.1007/s00521-015-2132-4

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