Predicting the strength of microsilica lime stabilized sulfate sand using hybrid machine learning models optimized with sparrow search algorithm.

Shufeng Chen, Boli Liu, Huanhuan Li, Jingjing Nan

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Article in Scientific reports, 2025. 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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5 · Who and what money

Authors and funding

4 authors.

Shufeng ChenShaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi'an, 710123, China.

Boli LiuShaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi'an, 710123, China.

Huanhuan LiSchool of Civil Engineering and Architecture, NingboTech University, No.1 Qianhu South Road, Ningbo City, 315100, Zhejiang Province, China. huanhuanli17@163.com.

Jingjing NanPostdoctoral Research Station of Geological Resource and Geological Engineering, Xi'an University of Science and Technology, Xi'an, 710054, Shaanxi, China.

Funding

China Postdoctoral Science Foundation 2024MD764012he National Natural Science Foundation of China 12102367the Special Fund for the Launch of Scientific Research in Xijing University XJ23T01

6 · The paper itself

Abstract

Accurately predicting the unconfined compressive strength (UCS) of microsilica-lime stabilized sulfate sand (MSLSS) is critical for the safe and efficient design of infrastructure in arid regions, yet it remains challenging due to the highly nonlinear relationships among influencing factors. This study pioneers the development of hybrid machine learning (ML) models, integrating the Sparrow Search Algorithm (SSA) with XGBoost (XGB), Random Forest, and Decision Tree, for predicting UCS of MSLSS. These models were trained and tested on experimental datasets incorporating input variables: lime content, microsilica content, curing days, curing condition, optimum moisture content (OMC), and maximum dry density. Comprehensive performance evaluation using metrics such as R

Indexed as

Machine learningSoil stabilizationSparrow search algorithmSulfate sandUnconfined compressive strength

Identifiers

PMID41413089

DOI10.1038/s41598-025-27974-y

PMCPMC12714745

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LicenceCC BY-NC-ND

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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.