Long-term viscoelastic deformation monitoring of a concrete dam: A multi-output surrogate model approach for parameter identification. (1st September 2022)

• Record Type: Journal Article
• Authors: Lin, Chaoning; Li, Tongchun; Chen, Siyu; Yuan, Li; van Gelder, P.H.A.J.M.; Yorke-Smith, Neil
• Is Part Of: Engineering structures. Volume 266(2022)
• Publication Date: 2022-09-01
• Subjects: Concrete dam -- Inverse analysis -- Surrogate model -- Viscoelasticity -- Multi-output Gaussian process; ANNs Artificial neural networks -- DOE Design of experiment -- FEM Finite element modelling -- GP Gaussian process -- HST Hydrostatic-season-time -- LHS Latin hypercube sampling -- MOGP Multi-output Gaussian process -- MVO Multi-verse optimizer -- NLF Negative log-likelihood function -- PCE Polynomial chaos expansion -- RBF Radial basis function -- SHM Structural health monitoring -- SVR Support vector regression -- TDR Travelling distance rate -- WEP Wormhole existence probability; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114553
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel surrogate model-assisted inversion method is proposed for identifying viscoelastic parameters of dam system. The sensitivity of the training sample size, parameter range and output quantity of the MOGP surrogate model is investigated. The computational accuracy and efficiency of the proposed inversion method are proved. A physics-based monitoring model is calibrated for long-term deformation prediction of the concrete dam. Abstract: Dam safety monitoring has become an important topic and is critical for evaluating a dam's safety status. This study focuses on identifying the mechanical properties of a concrete dam from long-term viscoelastic deformation monitoring data. A novel inversion framework is proposed in which a surrogate model, instead of the finite element model, is placed inside the optimization loop. First, a multi-output surrogate model based on Gaussian process is trained by using data from a finite element simulation in the creep regime. In order to efficiently create a high-precision and reliable surrogate model, three test instances are conducted to investigate the impact of sample size, parameter range and output quantity on the performance of the surrogate model. Subsequently, a meta-heuristic optimization, multi-verse optimizer, is employed to identify the unknown viscoelastic parameters. The results illustrate that the identified properties allow predictions on dam displacement which are consistent with the monitoring data. Compared with the traditional inversion method based on finite element modelling, the proposed inversion method based on the multi-output surrogate model not only achieves accurate estimation of mechanical parameters but also greatly improves computational efficiency.