Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting strength of concrete containing waste foundry sand and glass waste using artificial neural network
https://orcid.org/https://orcid.org/0000-0002-0495-435X
This research paper investigates the partial replacement of natural fine aggregate with waste foundry sand (WFS) and coarse aggregate with glass waste (GW) in concrete. Various proportions of WFS and GW (10%, 20%, and 30%) were examined to determine their effects on concrete properties. The results indicate that the optimal replacement proportion for both WFS and GW is 20%. At this proportion, the concrete exhibits enhanced strength, durability, and workability. Additionally, the study explores the impact of varying the percentage of glass waste while keeping WFS constant at 20%. The findings reveal that the combination of 20% WFS and 20% GW offers the best overall performance. The artificial neural network technique applied to compare the results reveals a good correlation between observed and predicted compressive strength values. These findings demonstrate the potential of utilizing WFS and GW as sustainable alternatives in concrete production, with implications for cost savings and reduced environmental impact.
Predicting strength of concrete containing waste foundry sand and glass waste using artificial neural network
https://orcid.org/https://orcid.org/0000-0002-0495-435X
This research paper investigates the partial replacement of natural fine aggregate with waste foundry sand (WFS) and coarse aggregate with glass waste (GW) in concrete. Various proportions of WFS and GW (10%, 20%, and 30%) were examined to determine their effects on concrete properties. The results indicate that the optimal replacement proportion for both WFS and GW is 20%. At this proportion, the concrete exhibits enhanced strength, durability, and workability. Additionally, the study explores the impact of varying the percentage of glass waste while keeping WFS constant at 20%. The findings reveal that the combination of 20% WFS and 20% GW offers the best overall performance. The artificial neural network technique applied to compare the results reveals a good correlation between observed and predicted compressive strength values. These findings demonstrate the potential of utilizing WFS and GW as sustainable alternatives in concrete production, with implications for cost savings and reduced environmental impact.
Predicting strength of concrete containing waste foundry sand and glass waste using artificial neural network
https://orcid.org/https://orcid.org/0000-0002-0495-435X
This research paper investigates the partial replacement of natural fine aggregate with waste foundry sand (WFS) and coarse aggregate with glass waste (GW) in concrete. Various proportions of WFS and GW (10%, 20%, and 30%) were examined to determine their effects on concrete properties. The results indicate that the optimal replacement proportion for both WFS and GW is 20%. At this proportion, the concrete exhibits enhanced strength, durability, and workability. Additionally, the study explores the impact of varying the percentage of glass waste while keeping WFS constant at 20%. The findings reveal that the combination of 20% WFS and 20% GW offers the best overall performance. The artificial neural network technique applied to compare the results reveals a good correlation between observed and predicted compressive strength values. These findings demonstrate the potential of utilizing WFS and GW as sustainable alternatives in concrete production, with implications for cost savings and reduced environmental impact.
Predicting strength of concrete containing waste foundry sand and glass waste using artificial neural network
Asian J Civ Eng
Singh, Aditya Pratap (Autor:in) / Sharma, Abhishek (Autor:in)
Asian Journal of Civil Engineering ; 25 ; 787-804
01.01.2024
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Abrasion resistance and strength properties of concrete containing waste foundry sand (WFS)
| Online Contents | 2012
Abrasion resistance and strength properties of concrete containing waste foundry sand (WFS)
| British Library Online Contents | 2012