Constitutive Parameters Research Articles

Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles: A genetic programming approach

Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerical modeling to address the intricacies of soil-pile interaction. Despite recent advancements in machine learning techniques, there is a persistent need to establish data-driven models that can predict these parameters without using numerical simulations due to the difficulties in conducting correct numerical simulations and the need for constitutive modelling parameters that are not readily available. This research presents novel lateral displacement and bending moment predictive models for closed and open-ended pipe piles, employing a Genetic Programming (GP) approach. Utilizing a soil dataset extracted from existing literature, comprising 392 data points for both pile types embedded in cohesionless soil and subjected to earthquake loading, the study intentionally limited input parameters to three features to enhance model simplicity: Standard Penetration Test (SPT) corrected blow count (N60), Peak Ground Acceleration (PGA), and pile slenderness ratio (L/D). Model performance was assessed via coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), with R2 values ranging from 0.95 to 0.99 for the training set, and from 0.92 to 0.98 for the testing set, which indicate of high accuracy of prediction. Finally, the study concludes with a sensitivity analysis, evaluating the influence of each input parameter across different pile types.

The anti-dogbone: Evaluating and designing optimal tensile specimens for deep learning of constitutive relations

Traditional tensile testing with “dogbone”-shaped specimen (ASTM E8, first standardized in 1924) strives for strain uniformity. Multiple tests with such samples help fit simple constitutive relation parameters on real materials. With the development of deep learning, the concept of employing entirely data-driven constitutive relations to capture more intricate material behavior has arisen. Nevertheless, these methods demand experimental data that are distributed throughout the complete stress–strain configuration space to effectively train the machine learning models. This is particularly crucial for mechanisms like hardening, which are time-dependent and sensitive to loading history. In this work, we investigate the potential to efficiently gather a wider range of experimental data points in the stress–strain configuration space using non-uniform samples and displacement-field mapping, leveraging advancements in computer vision techniques. We developed a metric to quantify stress state diversity in 2D tensile experiments and used it to optimize the shape of the sheet sample. The goal was to increase stress–strain diversity obtained within a single test, particularly in the linear elastic regime. Additional geometric constraints can be introduced on the design features, considering factors such as size and curvature to adapt to the microstructural characteristics of the sample material.

A Self-Optimized Machine Learning Approach for Constitutive Parameters Identification of Aortic Walls

The accurate identification of constitutive parameters is considered the key challenge for the study of mechanical properties of biological soft tissues. The popular machine learning (ML)-based inverse identification frameworks always require large amounts of training datasets. This work proposes an ML framework called self-optimized ML for accurate identifying the constitutive parameters of aortic walls under few training datasets conditions. The self-optimized ML includes three steps: Step 1: the forward physical FEM models first simulate the nonlinear deformation of aortic walls subject to uniaxial tension tests and are used to establish the nonlinear relationship datasets, Step 2: the carefully designed inverse random forest (RF) of the ML model can offer rapid identification by learning the established nonlinear relationship datasets, and Step 3: forward physical FEM models are recalled to evaluate the error between the identification results in Step 2 and real values, and then the accuracy are embedded into RF for guiding optimization directions to ensure that the final identification results are accurately and physically reasonable. The accuracy and robustness validation of proposed framework was conducted by constitutive parameters identification of uniaxial tension experiment samples of bovine aortic walls. The approach proposed achieves the R-squared exceeding 96.90% in longitudinal direction and 98.30% in circumferential direction, which is better than directly ML approach and gradient-based approach under the same amounts of datasets, respectively. The comparison results show that self-optimized ML can not only achieve accurate identification of the constitutive parameters of aortic walls, but also can decrease the identification results dependency of the initial number of sampling data effectively. The identification approach developed herein provides a common and convenient framework for constitutive parameters identification of biological soft tissues.

Loss Analysis of the Electromagnetic Metamaterial Applied to Antenna Size Reductions

The research of transformation electromagnetics uses spatial coordinate transformation method to transform Maxwell’s equations for the propagations of electromagnetic waves in space, thereby achieving the goal of designing the path of electromagnetic waves, which eventually leads to a more complex tensor design of its constitutive parameters of metamaterials. This demand for artificial design of material parameters has been combined with the rapid development of micro-scale, molecular-scale, and atomic-scale experimental engineering techniques in recent years, enabling metamaterials to be developed, fabricated, and applied in various frontier fields. In this paper, the metamaterial electromagnetic wave concentrator used to reduce the antenna size is mainly discussed and detailed simulations and researches on the electromagnetic lossy conditions of the metamaterial parameters are given, which can better verify the electromagnetic scattering performance under actual engineering conditions. Solutions and quantitative simulations for the attenuation characteristics are also provided, which can deepen the research on the engineering performance of the metamaterial electromagnetic transformation devices.

Research on dynamic sealing performance of sealer structure under the influence of temperature

Affected by the extreme environment of high temperature and high pressure downhole, the mechanical behavior of the sealer rubber sleeve undergoes significant changes during service, leading to a sharp decline in the mechanical properties of the rubber material within the high-temperature environment. To address the issue of seal failure in the sealer rubber barrel under high-temperature loads, experimental investigations were conducted to analyze the compression properties of rubber materials at various temperatures. The constitutive parameters of the rubber were then fitted using the Reduce-Polynomial model. Subsequently, a finite element model was employed to study the sealing performance and mechanical response of the three-compartment rubber-tube structure sealer at different temperatures. The findings demonstrate that temperature fluctuations have a substantial impact on the sealing performance and bearing strength of the sealer. Consequently, this research holds crucial guiding significance for the structural design of the sealer rubber cylinder under high-temperature conditions.

A granular micromechanic-based model for Ultra High Performance Fiber-Reinforced Concrete (UHP FRC)

There is a growing interest in the application of UHP FRC (Ultra-High Performance Fiber-Reinforced Concrete) which has been under vigorous development since its inception over two decades ago. The advantages of these advanced materials is that they overcome the problems that plague conventional concrete, these include among others low residual strength and low tensile strength, poor crack control and resistance to crack propagation, which leads corrosion of the rebars and spalling. The aim of this paper is to provide a granular micromechanics-based model to describe the deformation behavior of UHP FRC material as it gives a robust method to link the micro-scale mechanisms with the macro-scale performance of materials with granular textures. In this model, the micro-scale is described by damage-elasto-plastic spring elements that represent the effective grain pair interactions decomposed into the so-called normal and tangential directions. Since the grain-pairs are variously oriented, the macro-scale response is obtained by integrating these interactions over the orientational space. Here we specialize the granular micromechanic model for UHP FRC by introducing a novel expression for the parameterized dissipation energy at the micro or the grain-pair scale. The newly introduced constitutive parameters are identified using experimental results for uniaxial extension and compression tests. The model is then applied to simulate the case of homogeneous compression, extension and shear to show the directional evolution of damage and plasticity and the consequent emergent anisotropy.

Temporal evolution of shear-induced dilatancy of rock fractures: controls from surface roughness and normal stress

SUMMARY Understanding the shear-induced dilatancy of rock fractures is important for assessing the permeability evolution and seismic hazard in shale and geothermal reservoirs. The displacement dependence of fracture dilation has been well studied, while the influence of slip velocity is poorly constrained. In this study, we combined displacement- and velocity-dependent aperture models to reproduce the transient shear-induced dilatancy of fractures in sandstone in 16 normal stress unloading tests. Our results show that the combined aperture model can describe the transient fracture aperture evolution during accelerating slip induced by normal stress unloading better than the model dependent only on slip displacement. Slip velocity could enhance the aperture increase on smoother fractures at lower normal stresses and higher slip velocities. Both the dilation factor and characteristic slip distance decrease with increasing normal stress and surface roughness, signifying reduced contribution of slip velocity to transient shear-induced dilatancy at higher normal stresses and surface roughness. The dilation angle increases with the increase of surface roughness, and this increase diminishes at higher normal stresses primarily attributable to more severe asperity wear. These findings highlight the importance of slip velocity in controlling the transient evolution of aperture and permeability of a rock fracture. Our study also provides constraints on the constitutive parameters in the combined aperture model for describing transient shear-induced fracture dilatancy. We suggest that it is crucial to incorporate the velocity-dependent aperture model to simulate the nonlinear evolution of fracture aperture in future analytical and numerical models involving coupled hydromechanical processes in geoenergy systems.

Numerical simulation of multi-material hybrid lines for offshore mooring

In the last decades, great attention is being given to the development and improvement of station-keeping systems of offshore structures, such as wind turbines, energy converters, and platforms. In deep- and ultra-deepwaters installations, the use of synthetic, light-weight materials in lieu of chains and wire ropes is almost mandatory because of the deadweight of the entire mooring system. Several materials are useful for such purpose, such as polyester (PET), aramid, high modulus polyethylene (HMPE), and polyamide. However, since each material has its own advantages and disadvantages, an alternative is to design multi-material lines, with inserts of different materials aligned in series in one complete single line. In this sense, numerical tools are of utmost importance to the simulation of the behavior of the ropes in service conditions, and the quality of the results depend on the accurate definition of material properties. Here, we propose a constitutive framework for the finite element simulation of multi-material mooring ropes and test it to five configurations of multifilament samples combining PET and HMPE. We show that the model is capable of simulating the mechanical behavior of all configurations with a single set of constitutive parameters.

A dislocation density-based crystal plasticity constitutive model: comparison of VPSC effective medium predictions with ρ-CP finite element predictions

This work presents a dislocation density-based crystal plasticity constitutive model for glide kinetics, strengthening and dislocation density evolution, implemented in the effective medium-based visco-plastic self consistent (VPSC) framework and the spatially resolved, ρ-CP crystal plasticity finite element framework. Additionally, a distribution of intragranular stresses is introduced in the VPSC framework, instead of the conventionally used mean value of grain stress for effective medium calculations. The ρ-CP model is first calibrated to predict the mechanical response of a bcc ferritic steel with an initial rolled texture. The same set of constitutive model parameters are then used in VPSC to predict the aggregate stress–strain response and total dislocation densities. For these VPSC simulations, the interaction parameter governing the interaction between the grain and the effective medium in the Eshelby inclusion formalism, and a scalar parameter representative of the distribution of intragranular stresses within a grain, are used to calibrate the VPSC predictions in order to match the predictions of the ρ-CP model. A parametric study is performed to understand the effect of these two parameters on the VPSC predictions. Further, simulations are also performed for a random untextured polycrystal to identify the corresponding VPSC simulation parameters for predicting a similar response as the ρ-CP model. The novelty of the work is in the same set of constitutive models and associated parameters have been implemented in VPSC and ρ-CP to predict similar aggregate stress–strain response and total dislocation densities. This finite element-calibrated effective medium crystal plasticity approach reduces the computational time by at least two orders of magnitude and represents an advance towards the development of multiscale crystal plasticity modeling tools.

NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials

Abstract. In soil sciences, parametric models known as constitutive models (e.g., the Modified Cam Clay and the NorSand) are used to represent the behavior of natural and artificial materials. In contexts where liquefaction may occur, the NorSand constitutive model has been extensively applied by both industry and academia due to its relatively simple critical state formulation and low number of input parameters. Despite its suitability as a good modeling framework to assess static liquefaction, the NorSand model still is based on premises which may not perfectly represent the behavior of all soil types. In this context, the creation of data-driven and physically informed metamodels emerges. The literature suggests that data-driven models should initially be developed using synthetic datasets to establish a general framework, which can later be applied to experimental datasets to enhance the model's robustness and aid in discovering potential mechanisms of soil behavior. Therefore, creating large and reliable synthetic datasets is a crucial step in constructing data-driven constitutive models. In this context, the NorSand model comes in handy: by using NorSand simulations as the training dataset, data-driven constitutive metamodels can then be fine-tuned using real test results. The models created that way will combine the power of NorSand with the flexibility provided by data-driven approaches, enhancing the modeling capabilities for liquefaction. Therefore, for a material following the NorSand model, the present paper presents a first-of-its-kind database that addresses the size and complexity issues of creating synthetic datasets for nonlinear constitutive modeling of soils by simulating both drained and undrained triaxial tests. Two datasets are provided: the first one considers a nested Latin hypercube sampling of input parameters encompassing 2000 soil types, each subjected to 40 initial test configurations, resulting in a total of 160 000 triaxial test results. The second one considers nested quasi-Monte Carlo sampling techniques (Sobol and Halton) of input parameters encompassing 2048 soil types, each subjected to 42 initial test configurations, resulting in a total of 172 032 triaxial test results. By using the quasi-Monte Carlo dataset and 49 of its subsamples, it is shown that the dataset of 2000 soil types and 40 initial test configurations is sufficient to represent the general behavior of the NorSand model. In this process, four machine learning algorithms (Ridge Regressor, KNeighbors Regressor and two variants of the Ridge Regressor which incorporate nonlinear Nystroem kernel mappings of the input and output values) were trained to predict the constitutive and test parameters based solely on the triaxial test results. These algorithms achieved 13.91 % and 16.18 % mean absolute percentage errors among all 14 predicted parameters for undrained and drained triaxial test inputs, respectively. As a secondary outcome, this work introduces a Python script that links the established Visual Basic implementation of NorSand to the Python environment. This enables researchers to leverage the comprehensive capabilities of Python packages in their analyses related to this constitutive model.

Identifying multiple synergistic factors on the susceptibility to stress relaxation cracking in variously heat-treated weldments

The 347H austenitic stainless steel has been widely used for pressure vessels and pipeline (PVP) applications due to its excellent creep and corrosion resistance, which fit ideally to the harsh conditions in petrochemical industries, fossil fuel or nuclear power plants, and modern energy storages. However, a failure mode has been commonly observed with cracks emerging at the heat affected zone (HAZ) of weldments during post-weld heat treatment (PWHT) or under intermediate to high temperature service conditions. This phenomenon is termed as Stress Relaxation Cracking (SRC) since the purpose of PWHT is to relieve the welding-induced residual stress fields, or as Stress Age Cracking (SAC) if failure happens during service. A leading literature explanation of this failure suggests that the residual stress relaxation and the precipitation dissolution and/or re-precipitation occur in the same temperature range, which can lead to locally high strains and thus to crack at the grain boundaries. Since in situ spatial measurements of residual stress fields, microstructural evolution, and failure processes are nearly infeasible, this work recourses to a micromechanical finite element framework that models the high temperature failure as the nucleation and growth of grain boundary cavities, whereas various parameters such as thermomechanical loading history and its evolution, the competition of grain-interior dislocation creep and grain-boundary diffusion in failure lifetime, and microstructural heterogeneities (such as the precipitate free zone near grain boundaries) can be quantitatively incorporated. It can be concluded from these microstructure-explicit simulations that an accurate knowledge of residual stress evolution and a carefully calibrated set of material constitutive parameters are the essential prerequisites for lifetime predictions. The understanding of individual governing factors also leads to a mechanistic interpretation of the observed SRC susceptibility C-curves. These results suggest that the criticality of residual stress evolution, but not the precipitation-induced local strains, be the leading factor for SRC.

Unidirectional horizontal cyclic behaviors of the monopile-wheel composite foundation in sand

The monopile-wheel composite foundation is an innovative type of offshore wind turbine (OWT) foundation with good bearing capacity. The foundation is subjected to cyclic loads from wind, waves, and tides, so it is necessary to study its horizontal cyclic characteristics. By introducing cyclic degradation constitutive and Rayleigh damping parameters, numerical models are generated and validated by comparing with laboratory experiment results. The laws of horizontal accumulated displacement of the composite foundation are then explored through changes in the cycle amplitude, number, wheel diameter, height of loading point and pre-vertical load. The main findings are that the composite foundation is more likely to reach a stable displacement under constant amplitude cyclic loading compared to that of monopile. Under transient dynamic response (74 % of the horizontal ultimate bearing capacity), accumulated displacement will increase rapidly regardless of the foundation form. The composite foundation has smaller hysteresis loop area and slower accumulation rate of bending moment. The soil pressure weakening of the monopile mainly occurs in front of the pile, while that of the composite foundation mainly occurs under the wheel. After cyclic loading, the V–H envelopes for the composite foundation contract unevenly inwards. Above these, prediction methods for the cumulative displacement, maximum bending moment and V–H envelopes for composite foundation considering cyclic degradation are proposed.

Study on the induced effect of bedding weakness in deep shale gas reservoir on hydraulic fractures propagation

Shale gas, as an important unconventional oil and gas resource, plays an important role in energy supply. Due to the strong mechanical heterogeneity and compactness, which requires the use of fracturing to crush the formation to obtain industrial production capacity. Therefore, it is very important to analyze shale’s mechanical properties and fracturing propagation laws. In this paper, the shale numerical model is established by adopting discrete element method (DEM). The mesoscopic constitutive parameters of shale with different matrix and bedding strength are determined based on rock samples tests. The reliability of the model is verified by finite element method. And the fracture propagation laws under the influence of shale beddings are studied. The results show that the existence of bedding fractures leads to the nonuniformity of fractures propagation in shale reservoirs. The stress difference of 5 MPa and the approach Angle of 75° are the key factors affecting the interaction between hydraulic fractures and natural fractures. As the bedding number increases, the borehole pressure increases and the total number of fractures’ propagation decreases. The results provide a theoretical basis for further understanding of fractures’ propagation in deep shale reservoirs, and have important guiding significance for optimization and improvement of fracture complexity in the subsequent construction.

Identification of the Constitutive and Friction Models Parameters via a Multi-Objective Surrogate-Assisted Algorithm for the Modeling of Machining—Application to Arbitrary Lagrangian Eulerian Orthogonal Cutting of Ti6Al4V

Abstract The evolution of high-performance computing facilitates the simulation of manufacturing processes. The prediction accuracy of a numerical model of the cutting process is closely associated with the selection of constitutive and friction models. The reliability and the accuracy of these models highly depend on the value of the parameters involved in the definition of the cutting process. Direct of inverse methods are used to determine these model parameters. However, these identification procedures often neglect the link between the parameters of the material and the friction models. This article introduces a novel approach to inversely identify the best parameters value for both models at the same time and by taking into account multiple cutting conditions in the optimization routine. An artificial intelligence (AI) framework that combines the finite element modeling with an adaptive Bayesian multi-objective evolutionary algorithm (AB-MOEA) is developed, where the objective is to minimize the deviation between the experimental and the numerical results. The arbitrary Lagrangian–Eulerian (ALE) formulation and the Ti6Al4V alloy are selected to demonstrate its applicability. The investigation shows that the developed AI platform can identify the best parameters values with low computational time and resources. The identified parameters values predicted the cutting and feed forces within a deviation of less than 4% from the experiments for all the cutting conditions considered in this work.

Effects of Geo-Stress and Natural Fracture Strength on Hydraulic Fracture Propagation in a Deep Fractured Tight Sandstone Reservoir.

Hydraulic fracturing technology has been widely used in the tight reservoir reconstruction. Unfortunately, with the deepening of mining depth and the increase of geo-stress, the propagation mechanism of medium-pressure fractures in the reservoir is significantly different from that of conventional shallow reservoirs. Based on the combined finite discrete element method, this paper conducts numerical simulation research on deep tight sandstone reservoirs in the west. The discrete fracture network modeling method is used to establish a tight sandstone reservoir model with natural bedding, and the influence of geo-stress difference and natural fracture strength on hydraulic fracture propagation law in a high geo-stress environment is discussed in detail. The results show that the difference between geo-stress and the strength of natural fractures has a significant effect on the shape and expansion of hydraulic fractures under the high geo-stress conditions. The greater the difference in ground stress, the more obvious the tendency of the main fractures of the reservoir, and the shorter the branch fractures. With the increase of natural fracture strength, the changes in propagation pressure, fracture length, area, and width, which can be fitted with a linear function with a goodness of fit as high as 0.99. In addition, the morphological results of hydraulic fractures in the simulation are not only affected by the constitutive parameters of the model but also may be affected by the randomness of the natural fracture network, thus, showing a certain degree of dispersion. Therefore, it is extremely necessary to build a reservoir fracturing model in a specific area based on more detailed geological monitoring data to guide actual construction. The above achievements have certain reference significance for the field operation of deep tight sandstone reservoirs.

A finite strain integral model for the creep behavior of vaginal tissue

Vaginal delivery causes significant stretching of the vagina and surrounding muscles, potentially leading to the development of pelvic floor disorders and other maternal morbidities. Despite the extended duration of labor, little experimental and theoretical work has been done to characterize the long-term viscoelastic behavior of vaginal tissue. To mathematically capture the creep response of rat vaginal tissue measured, this study presents a new anisotropic finite-strain constitutive model within the single integral Pipkin-Rogers viscoelastic framework. The constitutive parameters are computed by curve-fitting the model to strain versus time data collected from ex vivo inflation testing along the two primary anatomical directions of the vagina, the longitudinal and circumferential directions. The results showed good agreement between theory and experiments, suggesting that the proposed model could be used to advance our understanding of the time-dependent deformations that are experienced by the vagina during delivery. This modeling framework represents a first step toward the development of accurate computational tools that can predict the safety of vaginal deliveries, reducing unnecessary Cesarean sections and their related complications.

Study of Dynamic Failure Behavior of a Type of PC/ABS Composite.

PC/ABS composites are commonly used in airbag covers. In this paper, uniaxial tensile experiments of a PC/ABS composite at different temperatures and strain rates were conducted. The results showed that the temperature and loading rate affect the mechanical properties of the PC/ABS composite. As the temperature increases, the yield stress decreases and the strain at the moment of fracture increases, but the strain rate at the same temperature has a relatively small effect on the mechanical properties, which are similar to ductile materials. The experimental results were applied to the Abaqus model which considered thermal effects and the exact Johnson-Cook constitutive parameters were calculated by applying the inverse method. Based on the constitutive model and the failure analysis findings acquired by DIC, the uniaxial tensile test at the room temperature and varied strain rates were simulated and compared to the test results, which accurately reproduced the test process. The experiment on target plate intrusion of the PC/ABS composite was designed, and a finite-element model was established to simulate the experimental process. The results were compared with the experiments, which showed that the constitutive and the failure fracture strains were valid.

Study on Plastic Constitutive Relation and Ductile Fracture Criterion of AM60B Magnesium Alloy.

It is currently a challenge to accurately predict the deformation and fracture behavior of metal parts in automobile crashes. Many studies have shown that the deformation and fracture behavior of materials are significantly affected by the stress state during automobile crashes with complex stress state characteristics. In order to further promote the application of die-cast magnesium alloys in automobiles, it is particularly important to study the material deformation and fracture behavior of die-cast magnesium alloys. In this paper, the mechanical properties of the AM60B die-cast magnesium alloy sheet under four stress states (shear, tension, R10 notch tension, and cupping) were designed and tested. Based on the von Mises isotropic constitutive model and Swift weighted Hockett-Sherby hardening model, the plastic constitutive model of die-cast magnesium alloy was established. Based on the plastic model and the fracture model (JC, MMC, and DIEM) considering the influence of three stress states, the deformation and fracture behavior of the AM60B die-cast magnesium alloy front-end members in three-point bending were predicted by experiments and finite element simulation. The experimental results show that the deformation mode and loading-displacement curve trend of the AM60B die-cast magnesium alloy front members are the same, the crack initiation point and crack initiation time are the same, and the crack shape is similar. The results show that the complex stress state constitutive model parameters and the DIEM fracture model obtained in this paper can accurately predict the deformation and fracture failure behavior of the AM60B die-cast magnesium alloy sheet.

Equivalent micropolar model for porous guided bone regeneration mesh: Optimum design for desired mechanical properties

In the present work, a micropolar continuum model is adopted to homogenise a heterogeneous porous model of Guided Bone Regeneration (GBR) meshes. GBR meshes are used in dentistry as mechanical barriers to isolate and protect the area of bone loss from the surrounding tissue while allowing new bone growth. The mechanical constants of the continuum are derived based on the strain energy equivalence of a periodic porous plate with its equivalent ortho-tetragonal micropolar model under prescribed boundary conditions. The effects of various architectural features such as pore shapes, patterns and sizes on the material parameters are investigated. The results show that the micropolar theory provides a better prediction of the response of the 2D porous geometries considered for the GBR mesh, compared to the classical Cauchy theory. The collected equivalent material parameters are further used for GBR mesh design, considering both mechanical and biomedical requirements. As an example, different materials and arrangements are analysed to find micropolar constitutive parameters that are comparable to bone parameters reported in the literature. This allows the GBR mesh to possess the mechanical performance that matches the adjacent bones.

An EGO-based online model updating method for hybrid simulation

Hybrid simulation combines experimentally testing of physical substructures with computational simulation of numerical substructures to replicate structural responses under earthquakes. When numerical substructure involves parts that are similar to physical substructure, measured responses of physical substructure are often used to update similar parts within numerical substructure, aiming to improve accuracy of seismic assessment. This study proposes an efficient global optimization based approach for online model updating in hybrid simulation (HSMU-EGO) involving multiple key components yet with only one physically tested in laboratory. Kriging meta-model is constructed for the response error between the numerical model for updating and the experimental substructure, while efficient global optimization and optimal Latin hypercube design are integrated for parameter estimation to minimize the response error. Computational simulations are conducted for a simple steel braced frame and a complex concrete continuous bridge to comprehensively evaluate the performance of HSMU-EGO in the presence of constitutive parameter and model errors. The proposed method is demonstrated to effectively and efficiently improve the accuracy of hybrid simulation in almost all cases. Compared with Unscented Kalman Filter (UKF), this proposed HSMU-EGO provides more suitable alternative when prior information is limited for the physical substructure.