Reliability-based design optimization of adaptive sliding base isolation system for improving seismic performance of structures

Peng, Yongbo; Ma, Yangying; Huang, Tianchen; Domenico, Dario De

doi:10.1016/j.ress.2020.107167

Cited by 47 publications

(19 citation statements)

References 48 publications

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“…In spite of this, the shear failure mode and flexure-shear failure mode cannot be avoided completely. Due to the insufficient transverse reinforcement, poor construction quality and boundary condition variation, the two types of failure modes are frequently observed in post-earthquake observation (Du et al, 2020; Peng et al, 2021; Wang et al, 2021). Therefore, how to account for the influence of failure modes in capacity prediction of RC columns remains one of the major concerns for structural engineers.…”

Section: Introductionmentioning

confidence: 99%

Failure-mode–independent prediction model for the peak strength of reinforced concrete columns using Bayesian neural network: A probabilistic approach

Ning

Wang

2022

Advances in Structural Engineering

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A reasonable prediction for the peak strength of reinforced concrete (RC) columns is paramount for the seismic performance evaluation of RC structures. The available prediction models are commonly dependent on the failure mode, and each of them is only applicable to the columns with a particular one. However, the failure mode of RC columns is difficult to be identified accurately in prior, leading to the inconvenience of predicting its peak strength. To overcome this shortcoming, a probabilistic approach was proposed using Bayesian neural network (BNN) to develop a failure-mode–independent model for predicting the peak strength of RC columns directly. The results indicated that the developed model produces reasonable prediction for the peak strength of RC columns failing in different modes. For the training subset, the mean prediction accuracy of the flexure-dominated, flexure-shear-dominated, and shear-dominated columns is 0.997, 0.997, and 0.998, respectively. For the testing subset, the corresponding mean prediction accuracy is 0.957, 0.952, and 0.943. Compared to existing probabilistic models, the developed model exhibits better performance in reducing the uncertainties in peak strength prediction. Compared to existing deterministic models, the developed model could predict the peak strength of RC columns in terms of the confidence interval. In particular, if the confidence interval of peak strength is defined as the mean plus and minus two times standard deviation, 98.9% and 98.4% of the training subset and testing subset are covered. Therefore, the developed model is beneficial for engineers to address the confusion, namely, which peak prediction is the most probable one, when several deterministic models exist for a specific specimen.

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Section: Introductionmentioning

confidence: 99%

Failure-mode–independent prediction model for the peak strength of reinforced concrete columns using Bayesian neural network: A probabilistic approach

Ning

Wang

2022

Advances in Structural Engineering

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“…Yazdani et al [3] developed an optimization method for RBDO problems of reinforced concrete structures considering soil-structure interaction effects, in which the MCS method was adopted to calculate the reliability and the support vector machine (SVM) approach was employed to evaluate the structural response. Peng et al [7] proposed an RBDO framework for base isolation systems, which used sensitivity analysis to identify critical design parameters and employed probability density evolution method to analyze the reliability of structures. In the studies of Refs.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Design Optimization of Structures Considering Uncertainties of Earthquakes Based on Efficient Gaussian Process Regression Metamodeling

Xiao

Yue

Wang

et al. 2022

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The complexity of earthquakes and the nonlinearity of structures tend to increase the calculation cost of reliability-based design optimization (RBDO). To reduce computational burden and to effectively consider the uncertainties of ground motions and structural parameters, an efficient RBDO method for structures under stochastic earthquakes based on adaptive Gaussian process regression (GPR) metamodeling is proposed in this study. In this method, the uncertainties of ground motions are described by the record-to-record variation and the randomness of intensity measure (IM). A GPR model is constructed to obtain the approximations of the engineering demand parameter (EDP), and an active learning (AL) strategy is presented to adaptively update the design of experiments (DoE) of this metamodel. Based on the reliability of design variables calculated by Monte Carlo simulation (MCS), an optimal solution can be obtained by an efficient global optimization (EGO) algorithm. To validate the effectiveness and efficiency of the developed method, it is applied to the optimization problems of a steel frame and a reinforced concrete frame and compared with the existing methods. The results show that this method can provide accurate reliability information for seismic design and can deal with the problems of minimizing costs under the probabilistic constraint and problems of improving the seismic reliability under limited costs.

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“…Recent research works about seismic isolation have led to the definition of several modeling strategies for isolators, models which can be adopted in time history analyses for the assessment to building and bridge structural systems, by reducing significantly the uncertainty of the structural behavior prediction ( [1], [7], [16]). The design of the proper period elongation, internal forces in the superstructure elements can be significantly reduced; on the other hand a large displacement demand may occur at the isolation level, even though lower values can be achieved, by means of the hysteretic characteristics of the implemented devices.…”

Section: Introductionmentioning

confidence: 99%

Modeling Strategies for the Lateral Response of Curved Surface Slider Devices Under Extreme Displacement Demands

Furinghetti¹,

Pavese²

2021

Proceedings of the 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Base isolation represents one of the most efficient strategy for the reduction of the structural vulnerability of buildings and bridges. Design procedures generally aim to provide the proper period shift, in order to reduce spectral acceleration values and, consequently, the base shear and internal forces. On the other hand, high displacement demands can be achieved, which can be partially limited by providing dissipative capacity through hysteretic behaviors. Although design procedures allow to fairly estimate the design displacement of the adopted devices, extreme seismic event can occur, and displacement higher than the design value can be experienced. Especially for Curved Surface Slider devices, if the displacement demand exceeds a certain geometrical limit, non-negligible damage can occur at the sliding pad, and variations in the force response are consequently noticed. In this work modeling strategies for the computation of the seismic response of base-isolated buildings are presented, by considering extreme earthquake loading conditions. Analytical models are reported for Curved Surface Slider devices, calibrated through the experimental outcomes of tests performed at the Laboratory of EUCENTRE Foundation in Pavia (Italy). In addition, simplified dynamic systems are defined, which allow fast assessments of the global response of a base-isolated structure, even though extreme seismic events are applied. Results have been compared to the response returned by an experimental hybrid simulation, in order to evaluate the accuracy of the presented dynamic systems.

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Reliability-based design optimization of adaptive sliding base isolation system for improving seismic performance of structures

Cited by 47 publications

References 48 publications

Failure-mode–independent prediction model for the peak strength of reinforced concrete columns using Bayesian neural network: A probabilistic approach

Failure-mode–independent prediction model for the peak strength of reinforced concrete columns using Bayesian neural network: A probabilistic approach

Reliability-Based Design Optimization of Structures Considering Uncertainties of Earthquakes Based on Efficient Gaussian Process Regression Metamodeling

Modeling Strategies for the Lateral Response of Curved Surface Slider Devices Under Extreme Displacement Demands

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