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Weizhong Chen

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2 papers

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EAAI Journal 2026 Journal Article

Ensemble modeling via entropy weight method and technique for order preference by similarity to an ideal solution for powder factor optimization toward targeted blast fragmentation

Weizhong Chen
Bo Liu
Xianyang Qiu
Wenbo Shen
Hongjie Qiu
Xiuzhi Shi

Accurate prediction of the powder factor (Pf) is crucial for optimizing blasting efficiency and cost in open-pit mining. To overcome the limitations of single-model approaches—such as poor stability and low interpretability—this study, utilizing 161 field datasets from the Mirador Copper Mine in Ecuador, innovatively integrates the Entropy Weight Method with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to construct an objective weighted fusion framework for seven machine learning (ML) models. Furthermore, it creatively combines SHapley Additive exPlanations (SHAP) with three-dimensional (3D) partial dependence plots (PDP) to decode the complex nonlinear interaction mechanisms among key features influencing the powder factor. The results show that the proposed model achieved superior performance with a coefficient of determination (R2) of 0. 921, a mean squared error (MSE) of 0. 003, and a mean absolute error (MAE) of 0. 046. SHAP analysis identified the 80% passing fragment size (D80), the burden-to-diameter ratio (B/D), rock density (R0), uniaxial compressive strength (UCS), and elastic modulus (E) as the most influential features, collectively accounting for 78. 65% of the total contribution. Three-dimensional PDP further revealed key nonlinear interactions: such as Pf exceeds 0. 65 when D80 > 0. 6 and B/D > 35, whereas it stabilizes between 0. 35 and 0. 45 when D80 < 0. 4. Field trials confirmed the system's practical applicability, with relative errors of only 3. 1%–5. 24% between target and measured fragmentation. This study offers a transparent, data-driven artificial intelligence (AI)methodology for Pf prediction applicable to geologically complex open-pit mines, enhancing both economic and safety outcomes.

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EAAI Journal 2026 Journal Article

Tunnel alignment design integrating physics-informed deep learning and enhanced optimization algorithm

Hui Li
Weizhong Chen
Xianjun Tan
Hongming Tian
Yaoyao Meng

The design of tunnel alignment based on mechanical stability principles faces significant challenges, including the arduous and time-consuming nature of conventional reliable data generation, limitations in efficient deformation prediction, and reliance on empirical approaches or engineering analogy for optimization. These challenges hinder efficient stability evaluation and optimal design. To this end, a hybrid framework is raised to implement automatic parametric modelling and data generation, efficient stability prediction, and intelligent optimization of tunnel design. Within this work, secondary development on existing software enables artificial data generation. A novel machine learning algorithm with the consideration of physics-informed and data-driven loss functions for higher precision is developed. The coordinates of four control points on the tunnel alignment are sought by an enhanced intelligent optimization algorithm. Validation using a numerical engineering case indicates that: (1) the secondary development for parametric modelling and automatic simulation is effective, reducing the time cost in data generation, (2) the proposed Transformer-Convolutional Neural Network (Trans-CNN) algorithm achieves satisfying performance in vault deformation prediction, with an R 2 value of 0. 9832 and a mean error around −0. 11%, outperforming the conventional CNN algorithm, (3) introducing of loss function revealing the vault deformation features ensures the precision of optimization process. Application of this framework on the design and optimization suggestions of underground engineering traversing complicated geological conditions holds immense potential. The mathematical framework underlying this study offers a transferable paradigm for other engineering domains requiring simulation-based optimization under physical constraints.

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