Assessment of a Mining-Waste Dump of Galena Mine in the East of Morocco for Possible Use in Civil Engineering

Khazanti, Faiçal El; Rachid, A.; Harrou, Achraf; Nasri, Hicham; Ettayea, Yassine; Ouahabi, Meriam El; Gharibi, Elkhadir

doi:10.12911/22998993/146585

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Among them, user portraits and product information can be regarded as fixed-length feature modules, and user behavior sequences correspond to variable-length feature modules, and it is also necessary to mine the deep interaction between the user side, the product side, and the user behavior sequence. In this way, the deep learning model of the recommender system can be used to process the static features of the structure [20].…”

Section: Padcn Modelmentioning

confidence: 99%

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Liu

Zhang

et al. 2022

Scientific Programming

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This study proposes the first fully deep learning-based structural response intelligent computing framework for civil engineering. For the first time, from the data side to the model side, the structural information of the structure itself and any loading system is comprehensively considered, which can be applied to materials, components, and even structures, system and other multi-level mechanical response prediction problems. First, according to the characteristics of structural calculation scenarios, a unified data interface mode for structural static characteristics is formulated, which preserves the original structural information input and effectively reduces manual intervention. On this basis, an attention mechanism and a deep cross network are introduced, and a structural static feature representation learning model PADCN is proposed, which can take into account the memory and generalization of structural static features, and mine the coupling relationship of different structural information. Then, the PADCN model is integrated with the dynamic feature prediction model Mechformer and connected with the designed general data interface to form an end-to-end data-driven structural response intelligent computing framework. In order to verify the validity of the framework, numerical experiments were carried out with the steel plate shear wall structure as the carrier, in which a data augmentation algorithm suitable for the field of structural calculation was proposed to alleviate the problem of lack of structural engineering data. The results show that the deep learning model based on this framework successfully predicts the whole-process nonlinear response of specimens with different structures, the simulation accuracy is better than that of the fine finite element model, and the computational efficiency exceeds the traditional numerical method by more than 1000 times, achieving a qualitative improvement. It is proven that the intelligent computing framework has excellent accuracy and efficiency.

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Section: Padcn Modelmentioning

confidence: 99%

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Liu

Zhang

et al. 2022

Scientific Programming

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Investigating the reprocessing potential of abandoned zinc-lead tailings ponds: A comprehensive study using physicochemical, mineralogical, and 3D geometallurgical assessments

El Aallaoui,

El Ghorfi,

Elghali

et al. 2024

Minerals Engineering

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Exploring Phytoremediation Potential: A Comprehensive Study of Flora Inventory and Soil Heavy Metal Contents in the Northeastern Mining Districts of Morocco

Oujdi,

Chafik,

Boukroute

et al. 2024

Plants

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Mining activities produce waste materials and effluents with very high metal concentrations that can negatively impact ecosystems and human health. Consequently, data on soil and plant metal levels are crucial for evaluating pollution severity and formulating soil reclamation strategies, such as phytoremediation. Our research focused on soils and vegetation of a highly contaminated site with potentially toxic metals (Pb, Zn, and Cu) in the Touissit mining districts of eastern Morocco. Vegetation inventory was carried out in three mine tailings of the Touissit mine fields using the “field tower” technique. Here, 91 species belonging to 23 families were inventoried: the most represented families were Poaceae and Asteraceae, and the biological spectrum indicated a predominance of Therophytes (55.12%). From the studied areas, 15 species were selected and collected in triplicate on the tailings and sampled with their corresponding rhizospheric soils, and analyzed for Pb, Zn, and Cu concentrations. Reseda lutea, lotus marocanus, and lotus corniculatus can be considered as hyperaccumulators of Pb, as these plants accumulated more than 1000 mg·kg−1 in their aerial parts. According to TF, these plant species could serve as effective plants for Pb phytoextraction.

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Assessment of a Mining-Waste Dump of Galena Mine in the East of Morocco for Possible Use in Civil Engineering

Cited by 3 publications

References 17 publications

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Corresponding Intelligent Calculation of the Whole Process of Building Civil Engineering Structure Based on Deep Learning

Investigating the reprocessing potential of abandoned zinc-lead tailings ponds: A comprehensive study using physicochemical, mineralogical, and 3D geometallurgical assessments

Exploring Phytoremediation Potential: A Comprehensive Study of Flora Inventory and Soil Heavy Metal Contents in the Northeastern Mining Districts of Morocco

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