Author name cluster

Guannan Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

EAAI Journal 2025 Journal Article

Predicting the ultimate strength of rectangular concrete-filled steel tube columns under eccentric loading using a knowledge-enhanced machine learning framework

Junbin Lou
Yixuan Li
Shicheng Zheng
Qian Feng
Guannan Wang
Rongqiao Xu
Xudong Qian

Rectangular concrete-filled steel tubes (RCFST) are widely used as structural columns in the field of structural engineering due to their exceptional mechanical properties. In practice, RCFST columns often experience eccentric loads, resulting in combined compression and bending. Although several empirical equations describe the mechanical behavior of CFST columns in terms of mechanical mechanism, their predictive ability is still limited by the necessary simplifying assumptions and application scope. This paper presents the development of a knowledge-enhanced machine learning (KeML) framework, with 15 input parameters, to address the limitations of conventional deterministic approaches and existing machine learning approaches, both suffering from low accuracy and robustness. To facilitate this, a comprehensive database consisting of 447 experimental data is established and divided into training and test sets, enabling the training and evaluation of the proposed KeML model. Comparative analyses against original machine learning models reveal that embedding knowledge can improve predictive accuracy and weak robustness, as well as reduce the required number of training data. Furthermore, a sensitivity analysis demonstrates that the cross-section size predominantly influences the ultimate strength of the RCFST columns subjected to eccentric loading. Finally, based on the sensitivity analysis and correlation analysis, a predictive equation for the ultimate strength of RCFST columns is developed using genetic programming.

Details DOI

EAAI Journal 2025 Journal Article

Prediction of the load–moment interaction curve for rectangular concrete-filled steel tube columns using composite multi-fidelity neural network

Yixuan Li
Junbin Lou
Qian Feng
Guannan Wang
Rongqiao Xu

Rectangular concrete-filled steel tubes (RCFSTs) are widely utilized structural solutions due to their exceptional mechanical behavior under combined compression and bending, characterized by the load–moment (N–M) interaction curve. However, with the advancement of high-performance materials, existing codes are inapplicable, and the scarcity of pure bending experimental data constrains the development of general calculation formulae. In this study, a composite multi-fidelity neural network (CMFNN) is proposed to predict the parabolic N–M curve by incorporating a high-fidelity network and an empirical bending strength method. A total of 1005 axial compression tests and 401 eccentric compression tests collected from a comprehensive literature review are employed to train the proposed model. Compared with specifications and a back propagation neural network, the high-fidelity network, which predicts axial strength through the sequential acquisition of concrete and steel coefficients, demonstrates superior accuracy, with a mean absolute percentage error (MAPE) of 6. 04%. To capture the curve's transition with slenderness, the eccentric compression data is subdivided into three subsets. This approach significantly improves prediction performance, especially in high-slenderness tests. Each subset exhibits a coefficient of determination (R2) above 95% and a MAPE below 8. 72%, outperforming traditional methods. The analysis of the feature importance conducted by the SHapley Additive exPlanations (SHAP) algorithm indicates that the primary factors affecting the curve shape shift from the strength and thickness of steel tube to concrete strength and specimen length as slenderness increases. Additionally, sensitivity analysis demonstrates that height feature predominantly impacts eccentric strength, providing effective guidance for practical engineering applications.

Details DOI

JMLR Journal 2024 Journal Article

Nonparametric Regression for 3D Point Cloud Learning

Xinyi Li
Shan Yu
Yueying Wang
Guannan Wang
Li Wang
Ming-Jun Lai

In recent years, there has been an exponentially increased amount of point clouds collected with irregular shapes in various areas. Motivated by the importance of solid modeling for point clouds, we develop a novel and efficient smoothing tool based on multivariate splines over the triangulation to extract the underlying signal and build up a 3D solid model from the point cloud. The proposed method can denoise or deblur the point cloud effectively, provide a multi-resolution reconstruction of the actual signal, and handle sparse and irregularly distributed point clouds to recover the underlying trajectory. In addition, our method provides a natural way of numerosity data reduction. We establish the theoretical guarantees of the proposed method, including the convergence rate and asymptotic normality of the estimator, and show that the convergence rate achieves optimal nonparametric convergence. We also introduce a bootstrap method to quantify the uncertainty of the estimators. Through extensive simulation studies and a real data example, we demonstrate the superiority of the proposed method over traditional smoothing methods in terms of estimation accuracy and efficiency of data reduction. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

PDF Details