Evaluation of Multiple Influences on the Unconfined Compressive Strength of Fibre-Reinforced Backfill Using a GWO-LGBM Model.

Xin Chen, Yunmin Wang, Shengjun Miao, Shian Zhang, Zhi Yu, Linfeng Du

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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5 · Who and what money

Authors and funding

6 authors.

Xin ChenSinosteel Maanshan General Institute of Mining Research Co., Ltd., Ma'anshan 243000, China.ORCID 0000-0002-9293-8105

Yunmin WangSinosteel Maanshan General Institute of Mining Research Co., Ltd., Ma'anshan 243000, China.

Shengjun MiaoSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Shian ZhangSchool of Resources and Safety Engineering, Central South University, Changsha 410083, China.

Zhi YuZijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China.ORCID 0000-0002-6591-0393

Linfeng DuZijin School of Geology and Mining, Fuzhou University, Fuzhou 350116, China.

Funding

National Natural Science Foundation of China 52204165National Natural Science Foundation of China 52574186

6 · The paper itself

Abstract

Fibres can markedly enhance the uniaxial compressive strength (UCS) of cemented paste backfill (CPB). However, previous studies have mainly verified the effectiveness of polypropylene and straw fibres in improving the UCS of CPB experimentally, while systematic multi-factor evaluation remains limited. In this study, laboratory experiments were conducted on polypropylene- and straw fibre-reinforced CPB to construct a reliable dataset. The factors influencing the intensity of uniaxial compressive strength were divided into four aspects (mixture proportions, physical properties of the cement-tailings mixture, chemical characteristics of tailings, and fibre properties), and four intelligent models were developed for effectiveness analysis and UCS prediction. SHapley Additive exPlanations (SHAP) were employed to quantify the contributions of individual features, and the findings were experimentally validated. The GWO-LGBM model outperformed the SVR, ANN, and LGBM models, achieving

Indexed as

fibre-reinforced cemented paste backfillGrey Wolf OptimizerLight Gradient Boosting MachineSHapley Additive exPlanationsunconfined compressive strength

Identifiers

PMID41515864

DOI10.3390/ma19010200

PMCPMC12787078

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.