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Cited by 24Geotechnical Engineering and Soil StabilizationInnovative concrete reinforcement materialsMaterials Engineering and Processing

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Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber

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Zhang, Genbao, Ding, Zhiqing, Wang, Yufei, Fu, Guihai, Wang, Yan, Xie, Chenfeng, Zhang, Yu, Zhao, Xiangming, Lu, Xinyuan, Wang, Xiangyu·2022·Materials
DOI 10.3390/ma15124250PMC PMC9229405
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Abstract

Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable.

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References 58 · Cited by 24

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Metadata

Authors
Zhang, G., Ding, Z., Wang, Y., Fu, G., Wang, Y., Xie, C., Zhang, Y., Zhao, X., Lu, X., & Wang, X.
Affiliations
  • Hunan City University (CN)
  • Chongqing University (CN)
  • Curtin University (AU)
  • Urban and Rural Construction and Investment Group Limited, Putian 351100, China
  • General Contracting Company of CCFED, Changsha 410000, China
  • Nanjing Institute of Technology (CN)
Corresponding author
  • Wang, Yufei
  • Wang, Xiangyu
ORCID
Venue
Materials
Publisher
MDPI AG
Published
2022-06-15
Volume
15
Issue
12
Pages
4250
ISSN
1996-1944 (ISSN-L)
DOI
10.3390/ma15124250
PMC
PMC9229405
OpenAlex
W4283030667
Cited by
24
References
58
Citations by year
2026: 4 · 2025: 7 · 2024: 6 · 2023: 5 · 2022: 2
FWCI
2.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Materials (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eSpace (Curtin University) (submittedVersion, cc-by)
Funders
  • Key Project of Hunan Education Department (21A0511, 51908201, 51978254, 2020JJ5024, 2021JJ50142, 2021NK4273, LP180100222, SKLGDUEK2105)
  • National Natural Science Foundation of China (21A0511, 51908201, 51978254, 2020JJ5024, 2021JJ50142, 2021NK4273, LP180100222, SKLGDUEK2105)
  • Natural Science Foundation of Hunan Province (21A0511, 51908201, 51978254, 2020JJ5024, 2021JJ50142, 2021NK4273, LP180100222, SKLGDUEK2105)
  • Hunan Provincial Science and Technology Plan (21A0511, 51908201, 51978254, 2020JJ5024, 2021JJ50142, 2021NK4273, LP180100222, SKLGDUEK2105)
  • Academic Research Council of Australia Linkage Projects for Asset Intelligence: Maximising Operational Effectiveness for Digital Era (21A0511, 51908201, 51978254, 2020JJ5024, 2021JJ50142, 2021NK4273, LP180100222, SKLGDUEK2105)
  • State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology/China University of Mining & Technology (21A0511, 51908201, 51978254, 2020JJ5024, 2021JJ50142, 2021NK4273, LP180100222, SKLGDUEK2105)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Geotechnical Engineering and Soil Stabilization
  • Physical Sciences › Engineering › Civil and Structural Engineering › Innovative concrete reinforcement materials
  • Physical Sciences › Engineering › Building and Construction › Materials Engineering and Processing
Keywords
soil-cement, construction and demolition waste, polypropylene fibers, sodium sulfate, unconfined compressive strength, flexural strength, performance prediction, BP neural network, random forest, beetle antennae search algorithm, hyperparameter optimization, mixture design, mechanical properties

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