Prediction of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious material (SCM): Optimization and hyper-tuning DOI Creative Commons
Muhammad Nasir Amin,

Kaffayatullah Khan,

Abdullah Mohammad Abu Arab

et al.

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 25, P. 1495 - 1536

Published: June 6, 2023

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of robust cementitious matrix with utmost properties. The strength HPC when subjected compression test is determined by combination and quantity materials used its production. Thus, making mixed design process challenging ambiguous. objective this research forecast containing RHA, using diverse range machine learning techniques, including both individual ensemble learners such bagging boosting. This study will cause significant implications for sustainable construction practices facilitating development an efficient effective method forecasting HPC. Individual (ML) algorithms are incorporated methods bagging, adaptive boosting, random forest algorithms. These techniques use create twenty different sub-models. Afterward, these sub-models train optimized achieving best possible value R2. were further fine-tuned obtain In order assess or evaluate quality, reliability, generalizability data, K-Fold cross-validation utilized. Furthermore, index measuring statistical performance models validate compare assessment models. findings indicate that boosting enhances prediction accuracy weak models, minimum errors R2 > 0.92 achieved decision tree forest. general, model learner (ML).

Language: Английский

An integration of geospatial and fuzzy-logic techniques for multi-hazard mapping DOI Creative Commons

Mausmi Gohil,

Darshan Mehta, Mohamedmaroof Shaikh

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101758 - 101758

Published: Jan. 10, 2024

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness other health consequences, property damage, social and economic crisis environmental degradation. Many places world are at risk from one more disasters. Although many studies have concentrated on single hazards, there need for integrated evaluations multi-hazards effective land management. selection datasets methods, such as meteorological data, satellite images, GIS, were used to create assessment maps. The parameters multi-hazard mainly considered rainfall, slope, elevation, use/land cover map in GIS For particular region, can be produced by integrating maps several assessments. objective this study an integration geospatial fuzzy-logic techniques mapping. Extensive parts Gujarat state (India) experience wide range hazards: floods, soil erosion, drought, earthquakes. This research creates evaluates individual group visualize spatial variation hazards state, India. calculated four been categorised into five classes: very-low, low, moderate, high, very high. multi has classified sixteen classes using unsupervised. aims improve disaster preparedness, enhance management, guide decision-making reduction. helpful future engineers, planners, local governments field planning

Language: Английский

Citations

16
Toward Explainable Flood Risk Prediction: Integrating A Novel Hybrid Machine Learning Model DOI
Yongyang Wang, Pan Zhang, Yulei Xie

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106140 - 106140

Published: Jan. 1, 2025

Language: Английский

Citations

2
Prioritizing Post-Disaster Reconstruction Projects Using an Integrated Multi-Criteria Decision-Making Approach: A Case Study DOI Creative Commons
Zahra Mohammadnazari, Mobina Mousapour Mamoudan, Mohammad Alipour-Vaezi

et al.

Buildings, Journal Year: 2022, Volume and Issue: 12(2), P. 136 - 136

Published: Jan. 27, 2022

As the destructive impacts of both human-made and natural disasters on societies built environments are predicted to increase in future, innovative disaster management strategies cope with emergency conditions becoming more crucial. After a disaster, selecting most critical post-disaster reconstruction projects among available is challenging decision due resource constraints. There strong evidence that success many compromised by inappropriate decisions when choosing projects. Therefore, this study presents an integrated approach based four multi-criteria decision-making (MCDM) techniques, namely, TOPSIS, ELECTRE III, VIKOR, PROMETHEE, aid makers prioritizing Furthermore, aggregation (linear assignment) used generate final ranking vector since various methods may provide different outcomes. In first stage, 21 criteria were determined sustainability. To validate performance proposed approach, obtained results compared artificial neural network (ANN) algorithm, which was applied predict projects’ rates. A case assess application model. The show selected case, project selection quality, robustness, customer satisfaction. findings can contribute growing body knowledge about have implications for key stakeholders involved provides valuable information national countries limited experience where consequences environment increasing.

Language: Английский

Citations

45
Flood risk mapping and urban infrastructural susceptibility assessment using a GIS and analytic hierarchical raster fusion approach in the Ona River Basin, Nigeria DOI
Felix Ndidi Nkeki, Innocent Bello,

Ishola Ganiy Agbaje

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2022, Volume and Issue: 77, P. 103097 - 103097

Published: June 3, 2022

Language: Английский

Citations

39
Prediction of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious material (SCM): Optimization and hyper-tuning DOI Creative Commons
Muhammad Nasir Amin,

Kaffayatullah Khan,

Abdullah Mohammad Abu Arab

et al.

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 25, P. 1495 - 1536

Published: June 6, 2023

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of robust cementitious matrix with utmost properties. The strength HPC when subjected compression test is determined by combination and quantity materials used its production. Thus, making mixed design process challenging ambiguous. objective this research forecast containing RHA, using diverse range machine learning techniques, including both individual ensemble learners such bagging boosting. This study will cause significant implications for sustainable construction practices facilitating development an efficient effective method forecasting HPC. Individual (ML) algorithms are incorporated methods bagging, adaptive boosting, random forest algorithms. These techniques use create twenty different sub-models. Afterward, these sub-models train optimized achieving best possible value R2. were further fine-tuned obtain In order assess or evaluate quality, reliability, generalizability data, K-Fold cross-validation utilized. Furthermore, index measuring statistical performance models validate compare assessment models. findings indicate that boosting enhances prediction accuracy weak models, minimum errors R2 > 0.92 achieved decision tree forest. general, model learner (ML).

Language: Английский

Citations

39