Indian geotechnical journal, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 5, 2024
Language: Английский
Indian geotechnical journal, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 5, 2024
Language: Английский
Structural Concrete, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 7, 2025
Abstract Non‐structural components are difficult to recycle into fresh concrete due their high porosity, water absorption, and low strength. This study uses aerated waste (ACW) as a case investigate the effect of recycled aggregate on performance eco‐friendly foamed mortar (EFM). The results show that while incorporating ACW reduces fluidity mechanical properties its porous structure, it enhances lightweight thermal insulation capabilities EFM, making suitable for non‐structural applications. When 25% is applied, 28‐day compressive strength (CS) plain EFM decreases by 64.82%, hardened unit weight conductivity decrease 17.49% 30.85%, respectively. addition PPF compensates loss from ACW, with bridging inhibiting crack formation interlocking aggregates cement paste, though further fluidity. 0.5% 7‐ flexural increased 77.35% 30.54%, but this resulted in 22.22% reduction presents feasible approach recycling low‐grade construction production, contributing development sustainable materials.
Language: Английский
Citations
3Case Studies in Construction Materials, Journal Year: 2025, Volume and Issue: unknown, P. e04463 - e04463
Published: March 1, 2025
Language: Английский
Citations
2Journal of Materials Science, Journal Year: 2025, Volume and Issue: 60(6), P. 3178 - 3199
Published: Jan. 23, 2025
Language: Английский
Citations
1Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107042 - 107042
Published: March 1, 2025
Language: Английский
Citations
0Minerals, Journal Year: 2025, Volume and Issue: 15(4), P. 405 - 405
Published: April 11, 2025
A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) applied existing AI soft computing techniques, using AdaBoost, random forest (RF), SVM, ANN. Data were arbitrarily separated into training (70%) test (30%) sets. Results confirm that AdaBoost RF have best prediction accuracy, with a correlation coefficient (R2) 0.957 between these sets AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 64-bit), PyQt5 achieve machine learning model computation software function interface development, this can quickly property CTF specimens, which saves time costs efficiently backfill researchers developing new eco-efficient components.
Language: Английский
Citations
0Structures, Journal Year: 2025, Volume and Issue: 77, P. 108996 - 108996
Published: May 2, 2025
Language: Английский
Citations
0Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110630 - 110630
Published: Sept. 1, 2024
Language: Английский
Citations
2Construction and Building Materials, Journal Year: 2024, Volume and Issue: 456, P. 139193 - 139193
Published: Nov. 20, 2024
Language: Английский
Citations
1Indian geotechnical journal, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 5, 2024
Language: Английский
Citations
1