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Prediction of Compressive Strength for Gold-Tailings-Based Concrete by DP-CNN-GRU Model and its Engineering Application
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Xinzhong LIU, Shusen MA, Yan GE, Jin ZHAO
Mining Research and Development | 2025, 45(10) : 279 - 288
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Mining Research and Development | 2025, 45(10): 279-288
Prediction of Compressive Strength for Gold-Tailings-Based Concrete by DP-CNN-GRU Model and its Engineering Application
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Xinzhong LIU, Shusen MA, Yan GE, Jin ZHAO
Affiliations
  • School of Ecological Environment and Urban Construction, Fujian University of Technology, Fuzhou, Fujian 350118, China
Published: 2025-10-25
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As an environmentally friendly material, gold-tailings-based concrete has a wide range of potential applications. However, the complexity of the material composition of gold-tailings-based concrete, traditional prediction methods of compressive strength are often difficult to capture the nonlinear correlation and multivariate coupling characteristics within the material, resulting in insufficient prediction accuracy. Thus, a strength prediction model for gold-tailings-based concrete was proposed based on a deep learning binary fusion model (DP), a fusion Convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). Firstly, the mineral, chemical composition and particle size distribution of gold tailings were analyzed, and their leaching toxicity was tested according to relevant standards to ensure their safety and stability as concrete materials. Subsequently, the gold tailings concrete dataset was constructed through experiments and applied to the training and validation of the model. In order to further verify the predictive ability of the model, it was applied to real engineering cases. The results show that the proposed model exhibits high accuracy in both the training and testing process, and is capable of effectively predicting the compressive strength of the gold-tailings-based concrete. The actual engineering cases show that the error range between the predicted and measured compressive strength of concrete with 20%−40% gold tailings is −4.1%−5.7%, which further proves the potential of the model to be applied in engineering practice.

Gold tailings  /  Concrete  /  Convolutional neural network  /  Gated recurrent unit neural network  /  Hybrid optimization algorithm  /  Compressive strength prediction
Xinzhong LIU, Shusen MA, Yan GE, Jin ZHAO. Prediction of Compressive Strength for Gold-Tailings-Based Concrete by DP-CNN-GRU Model and its Engineering Application[J]. Mining Research and Development, 2025 , 45 (10) : 279 -288 .
Year 2025 volume 45 Issue 10
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  • Receive Date:2024-11-12
  • Online Date:2026-02-06
  • Published:2025-10-25
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  • Received:2024-11-12
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    School of Ecological Environment and Urban Construction, Fujian University of Technology, Fuzhou, Fujian 350118, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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