Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber
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)...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90924 |
| _version_ | 1848765462110797824 |
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| author | Zhang, G. Ding, Z. Wang, Yufei Fu, G. Wang, Y. Xie, C. Zhang, Y. Zhao, X. Lu, X. Wang, Xiangyu |
| author_facet | Zhang, G. Ding, Z. Wang, Yufei Fu, G. Wang, Y. Xie, C. Zhang, Y. Zhao, X. Lu, X. Wang, Xiangyu |
| author_sort | Zhang, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:35:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-90924 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:38Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909242023-05-11T06:57:12Z Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber Zhang, G. Ding, Z. Wang, Yufei Fu, G. Wang, Y. Xie, C. Zhang, Y. Zhao, X. Lu, X. Wang, Xiangyu Science & Technology Physical Sciences Technology Chemistry, Physical Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Physics, Applied Physics, Condensed Matter Chemistry Materials Science Physics cement stabilized soil fiber-reinforced soil mechanical strength waste utilization Back Propagation Neural Network Random Forest beetle antennae search COMPRESSIVE STRENGTH CONCRETE REGRESSION HYDRATION BEHAVIOR COLUMNS SULFATE Back Propagation Neural Network Random Forest beetle antennae search cement stabilized soil fiber-reinforced soil mechanical strength waste utilization 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/90924 10.3390/ma15124250 English http://purl.org/au-research/grants/arc/LP180100222 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Physical Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Physics, Applied Physics, Condensed Matter Chemistry Materials Science Physics cement stabilized soil fiber-reinforced soil mechanical strength waste utilization Back Propagation Neural Network Random Forest beetle antennae search COMPRESSIVE STRENGTH CONCRETE REGRESSION HYDRATION BEHAVIOR COLUMNS SULFATE Back Propagation Neural Network Random Forest beetle antennae search cement stabilized soil fiber-reinforced soil mechanical strength waste utilization Zhang, G. Ding, Z. Wang, Yufei Fu, G. Wang, Y. Xie, C. Zhang, Y. Zhao, X. Lu, X. Wang, Xiangyu Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber |
| title | Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber |
| title_full | Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber |
| title_fullStr | Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber |
| title_full_unstemmed | Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber |
| title_short | Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber |
| title_sort | performance prediction of cement stabilized soil incorporating solid waste and propylene fiber |
| topic | Science & Technology Physical Sciences Technology Chemistry, Physical Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Physics, Applied Physics, Condensed Matter Chemistry Materials Science Physics cement stabilized soil fiber-reinforced soil mechanical strength waste utilization Back Propagation Neural Network Random Forest beetle antennae search COMPRESSIVE STRENGTH CONCRETE REGRESSION HYDRATION BEHAVIOR COLUMNS SULFATE Back Propagation Neural Network Random Forest beetle antennae search cement stabilized soil fiber-reinforced soil mechanical strength waste utilization |
| url | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90924 |