Bamboo, as a naturally fast-growing renewable resource, is abundant in China and supported by a wellestablished
industrial foundation, making it a crucial material for promoting green building development.
Modern engineered bamboo structures are typically fabricated using industrialized processes with
engineered bamboo-based panels as raw materials, which reduce environmental impact while enabling
standardization and prefabrication of building components. These structures can effectively meet
performance requirements in terms of safety, economy, and comfort. Glue-Laminated Bamboo (glubam), a
representative type of engineered bamboo, features a high strength-to-weight ratio, low adhesive content,
and stable physical and mechanical properties. As an innovative and sustainable construction material,
glubam has shown great potential for application in modern structural engineering. Compared to traditional
building materials such as timber and steel, glubam offers significant advantages in strength-to-weight
performance, renewability, and environmental friendliness. Despite its excellent material properties,
research on the hysteretic behavior of glubam structural joints and the seismic performance of glubam
composite truss systems remains insufficient. Comprehensive design theories and reliable numerical
modeling approaches are still lacking. Moreover, integrating artificial intelligence (AI) optimization
algorithms into the seismic performance analysis of glubam structures represents a promising yet
underexplored research direction.
In response to these challenges, this study systematically investigates the mechanical behavior of
glubam joints and their corresponding truss assemblies under cyclic loading through a combination of
experimental testing, numerical simulation, and AI-based optimization methods. The main research
contents and findings are summarized as follows: First, according to relevant testing standards, two types
of glubam joints and their corresponding planar truss and roof truss structures were subjected to quasi-static
cyclic loading tests to evaluate their hysteretic behavior and failure modes. Based on the experimental
observations, both high-fidelity three-dimensional finite element models and simplified low-fidelity
hysteresis models were developed to capture the nonlinear mechanical responses of the two joint types. For
the simplified models, two parametric hysteresis constitutive models were proposed to reproduce critical
features observed under cyclic loading, such as pinching effects, asymmetry, and strength degradation.
Three representative AI optimization algorithms—Genetic Algorithm (GA), Bayesian Inference (BI), and
Neural Network (NN)—were introduced to perform parameter identification and model calibration,
significantly improving the accuracy and generalizability of the models. Finally, using the calibrated
hysteresis models, a macro-scale numerical model of the glubam truss structure was constructed by
combining the joint models with beam-column elements. Structural-level model updating was then
performed using AI algorithms, and the optimized model was used to analyze the structural response of
glubam trusses under cyclic loading. The detailed research tasks and contributions of this study are
summarized as follows: This study first conducted axial monotonic and cyclic loading tests on two types of glubam joint
connections with distinct configurations: the steel-insert glubam joint and the steel-plate clamped glubam
joint. The fasteners used in these joints were designed with varying geometric dimensions. Through
systematic experimentation, the mechanical behavior of both joint types under cyclic loading was
comprehensively analyzed, including characteristics of their hysteresis curves, stiffness degradation
patterns, energy dissipation capacity, and typical failure modes. The test results demonstrated that both
types of glubam joints exhibited favorable hysteretic behavior and excellent energy dissipation performance.
Their failure processes were primarily ductile in nature, indicating promising seismic resistance potential.
In addition, the influence of geometric parameters of the fasteners on the mechanical performance of the
joints was further investigated. It was found that these parameters significantly affect the joints' load-bearing
capacity, initial stiffness, and energy dissipation efficiency.
Building upon the joint performance investigation, planar truss and roof truss systems were designed
using the two connection types (steel-insert and steel-plate clamped) and subjected to quasi-static cyclic
loading tests. The study systematically evaluated the global hysteretic performance, energy dissipation
capacity, and seismic behavior of the two types of truss systems under cyclic loads. Test results indicated
that glubam truss systems exhibited good deformation capacity and high energy dissipation efficiency,
meeting the basic requirements of seismic design.
In the numerical simulation component of this study, high-fidelity three-dimensional finite element
(FE) models were developed for both types of glubam joint configurations. A novel modeling approach was
proposed by coupling the "element deletion method" with the Hill yield criterion, enabling simultaneous
characterization of the orthotropic mechanical properties and crack propagation behavior of glubam. These
constitutive mechanisms were implemented via a user-defined material subroutine (UMAT) in Abaqus and
successfully applied to the high-fidelity 3D finite element model of the steel-insert glubam joint. The
simulated load–displacement curves closely matched the experimental results, validating the model’s
accuracy and reliability in capturing the nonlinear hysteretic response of the joints.
To enable more efficient simulation at the structural (macro) scale, two sets of low-fidelity simplified
hysteretic models were further developed for the aforementioned joint configurations. These models
innovatively combined multiple types of spring elements—each representing distinct mechanical behaviors
such as ideal elastoplasticity, pinching, and gap characteristics—through series and parallel arrangements.
This approach significantly reduced computational cost in structural analysis and facilitated subsequent
parameter identification and model updating. The simplified hysteretic models systematically incorporated
key nonlinear features observed during cyclic loading, including stiffness degradation, unloading stiffness
recovery, strength deterioration, and energy dissipation. Comparison with experimental data demonstrated
that the simulated load–displacement curves agreed closely with test results, confirming the proposed
hysteretic models’ accuracy and engineering applicability.
To ensure that the numerical hysteresis models accurately capture the actual cyclic behavior of glubam
joints, it is essential to identify and calibrate multiple key model parameters. However, due to the high dimensionality of these parameter sets, manual tuning is inefficient and often fails to yield stable and reliable
results. To address this issue, this study incorporates three mainstream artificial intelligence (AI)
optimization algorithms—Genetic Algorithm (GA), Bayesian Inference (BI), and Neural Networks (NN)—
into the finite element (FE) simulation workflow, thereby establishing an intelligent parameter identification
framework. By conducting a comparative analysis of the three algorithms in terms of accuracy, convergence
speed, and robustness, the most suitable optimization strategy was selected. The resulting calibrated
numerical hysteresis models exhibit both high accuracy and strong stability, and are capable of faithfully
reproducing the cyclic behavior of the joints under repeated loading, providing a reliable basis for
subsequent structural-level modeling. Building on this foundation, the calibrated simplified joint models
were embedded into macro-scale glubam truss models, enabling simulation of the coupled behavior between
the joints and the overall structural system. To further improve the predictive accuracy of the structural mo
dels under realistic loading conditions, an advanced model updating procedure was implemented using
optimization techniques. The updated models were validated through systematic comparisons between
numerical simulations and experimental results, confirming the accuracy and practical value of the proposed
model updating methodology.
The research findings demonstrate that glubam joints and their corresponding truss systems exhibit
excellent energy dissipation capacity and mechanical stability under cyclic loading, highlighting their
significant potential in seismic design and sustainable construction. This dissertation not only systematically
uncovers the hysteresis evolution characteristics of glubam joints and truss systems but also proposes a
comprehensive modeling and optimization framework—from constitutive joint modeling and parameter
identification to structural-level model integration and updating. These contributions lay a solid theoretical
and technical foundation for the engineering application of glubam-based structural systems in seismic
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