Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19
Published: April 26, 2025
Language: Английский
Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19
Published: April 26, 2025
Language: Английский
Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 416, P. 137843 - 137843
Published: June 18, 2023
Language: Английский
Citations
44Cleaner Materials, Journal Year: 2023, Volume and Issue: 9, P. 100204 - 100204
Published: Aug. 16, 2023
Cement asphalt emulsion mixture (CAEM) is an environmentally sustainable substitute for hot mix and can trigger a substantial economic benefit. This paper systematically reviews the interactions between organic-inorganic composites their influence on performance of CAEM. First, (AE) cement are introduced. Next, demulsification AE hydration in CAEM system analyzed. Finally, fresh properties CA paste, static dynamic mechanical mortar its applications ballastless slab tracks, road pavement construction discussed. review allows better understanding interaction composite thus has strategy to regulate promote practical application.
Language: Английский
Citations
42Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 452, P. 142203 - 142203
Published: April 10, 2024
Language: Английский
Citations
22Construction and Building Materials, Journal Year: 2024, Volume and Issue: 425, P. 136103 - 136103
Published: April 1, 2024
Language: Английский
Citations
20Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 91, P. 109734 - 109734
Published: May 24, 2024
Language: Английский
Citations
17Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 7, 2025
To improve the utilization rates of soda residue (SR) and fly ash (FA), reduce environmental pollution, enhance mechanical properties marine clay (MC), this study proposes mixing SR, FA, MC with cement /or lime to prepare residue-fly stabilized soil (SRFSS). Using an orthogonal design for proportions, analyzes compaction performance, unconfined compressive strength (UCS), shear SRFSS. The influence various factors on SRFSS was investigated through range variance analyses. mechanism revealed from perspectives grading cementation. results indicate that SR FA significantly impact analysis are consistent: content 30% 70% has most significant performance UCS, respectively, while 20% greatest effect strength. recommended base proportion is + 10% MC. gradation cementitious jointly microstructure SRFSS, G8 lowest planar porosity, at only 0.89%. calcium (Ca) in specimens different proportions shows variation, 5.0 53.6 wt%, silicon (Si)/Al ratio (0.76–2.73) relatively small fluctuations. primary hydration products include hydroxide (Ca(OH)2), silicate hydrate (C-S-H), ettringite (AFt).
Language: Английский
Citations
2Construction and Building Materials, Journal Year: 2023, Volume and Issue: 406, P. 133433 - 133433
Published: Sept. 22, 2023
Language: Английский
Citations
35Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 449, P. 141752 - 141752
Published: March 12, 2024
Language: Английский
Citations
14Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 107, P. 114838 - 114838
Published: Dec. 10, 2024
Language: Английский
Citations
9Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03083 - e03083
Published: March 28, 2024
Sustainable development in the building industry can be achieved through use of versatile cementitious composites. Thus, incorporating nanoparticles into cement composites create materials with enhanced performance and numerous applications. The utilization carbon nanotubes (CNTs) construction has great promise for developing efficient solutions to establish a sustainable ecosystem diverse characteristics. However, forecasting characteristics these is significant challenge due their intricate composite structure nonlinear behavior. Designing conducting laboratory experiments on samples across multiple age groups challenging, time-consuming, costly. Moreover, there presently lack model that predict concrete's compressive strength (fc') nanoparticles. Three machine learning (ML) techniques, K-nearest neighbor (KNN), linear regression (LR), artificial neural network (ANN), were used fc' nanocomposites containing CNTs this research. A thorough database consisting 282 data entities CNTs-based concrete model's reliability was assessed using R2 test statistical error analysis. ANN had most outstanding value 0.885, while KNN LR models values 0.838 0.744, respectively. RReliefF analysis utilized evaluate primary components predicting outcomes. This research shows properties CNT-based are greatly affected by water-to-binder ratio, followed proportions coarse aggregates. ML algorithms exhibited superior generalization capabilities, suggesting potential accurate predictions properties.
Language: Английский
Citations
8