ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING

In the construction industry, acquiring the ideal concrete production strength is crucial for maintaining the durability and structural integrity of buildings and other public infrastructure. Concrete's compressive strength, a crucial mechanical characteristic must adhere to strict criteria for...

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Main Authors: Ashuvendra Singh, Sunit Kumar, Avinash Ranger
Format: Article
Language:English
Published: University of Kragujevac 2024-03-01
Series:Proceedings on Engineering Sciences
Subjects:
Online Access:https://pesjournal.net/journal/v6-n1/42.pdf
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author Ashuvendra Singh
Sunit Kumar
Avinash Ranger
author_facet Ashuvendra Singh
Sunit Kumar
Avinash Ranger
author_sort Ashuvendra Singh
collection DOAJ
description In the construction industry, acquiring the ideal concrete production strength is crucial for maintaining the durability and structural integrity of buildings and other public infrastructure. Concrete's compressive strength, a crucial mechanical characteristic must adhere to strict criteria for quality and sustainability. This research proposes a Genetic-Chimp Optimized Adaptable Gradient Boosting (GCO-AGB) refers to a revolutionary strategy developed by researchers in this field to improve the manufacturing strength of concrete through the use of fly ash-based procedures. This procedure is started by preprocessing gathered data on concrete manufacture using Z-score normalization to ensure the accuracy and consistency of the data. The effectiveness of the suggested strategy is examined by comparing the study findings to performance measures like Root Mean Square Error (RMSE), Coefficient of Determination (?2), Mean Absolute Error (MAE), and Duration (S). The study's findings show that it is possible and efficient to use machine learning techniques, in particular GCO-AGB, to detect and classify concrete production strength in the context of fly ash-based procedures. The construction industry's search for improved structural performance and sustainability has bright prospects because to this new approach's better efficiency in assessing and improving concrete manufacturing processes when compared to standard manufacturing techniques.
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spelling doaj.art-8776d92cc19345699cb88ef9226ad3d42024-03-23T15:34:06ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112024-03-016139740610.24874/PES.SI.24.02.022ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTINGAshuvendra Singh0https://orcid.org/0009-0008-8333-225XSunit Kumar1https://orcid.org/0009-0001-6061-5408Avinash Ranger2https://orcid.org/0009-0002-7588-1298Dev Bhoomi Uttarakhand University, Dehradun, IndiaDev Bhoomi Uttarakhand University, Dehradun, IndiaDev Bhoomi Uttarakhand University, Dehradun, IndiaIn the construction industry, acquiring the ideal concrete production strength is crucial for maintaining the durability and structural integrity of buildings and other public infrastructure. Concrete's compressive strength, a crucial mechanical characteristic must adhere to strict criteria for quality and sustainability. This research proposes a Genetic-Chimp Optimized Adaptable Gradient Boosting (GCO-AGB) refers to a revolutionary strategy developed by researchers in this field to improve the manufacturing strength of concrete through the use of fly ash-based procedures. This procedure is started by preprocessing gathered data on concrete manufacture using Z-score normalization to ensure the accuracy and consistency of the data. The effectiveness of the suggested strategy is examined by comparing the study findings to performance measures like Root Mean Square Error (RMSE), Coefficient of Determination (?2), Mean Absolute Error (MAE), and Duration (S). The study's findings show that it is possible and efficient to use machine learning techniques, in particular GCO-AGB, to detect and classify concrete production strength in the context of fly ash-based procedures. The construction industry's search for improved structural performance and sustainability has bright prospects because to this new approach's better efficiency in assessing and improving concrete manufacturing processes when compared to standard manufacturing techniques.https://pesjournal.net/journal/v6-n1/42.pdfmanufacturinggenetic-chimp optimized adaptable gradient boosting (gco-agb)concretestrengthfly ash (fa)
spellingShingle Ashuvendra Singh
Sunit Kumar
Avinash Ranger
ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING
Proceedings on Engineering Sciences
manufacturing
genetic-chimp optimized adaptable gradient boosting (gco-agb)
concrete
strength
fly ash (fa)
title ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING
title_full ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING
title_fullStr ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING
title_full_unstemmed ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING
title_short ENHANCING MANUFACTURING STRENGTH THROUGH FLY ASH-BASED PROCESSES USING GENETIC-CHIMP OPTIMIZED ADAPTABLE GRADIENT BOOSTING
title_sort enhancing manufacturing strength through fly ash based processes using genetic chimp optimized adaptable gradient boosting
topic manufacturing
genetic-chimp optimized adaptable gradient boosting (gco-agb)
concrete
strength
fly ash (fa)
url https://pesjournal.net/journal/v6-n1/42.pdf
work_keys_str_mv AT ashuvendrasingh enhancingmanufacturingstrengththroughflyashbasedprocessesusinggeneticchimpoptimizedadaptablegradientboosting
AT sunitkumar enhancingmanufacturingstrengththroughflyashbasedprocessesusinggeneticchimpoptimizedadaptablegradientboosting
AT avinashranger enhancingmanufacturingstrengththroughflyashbasedprocessesusinggeneticchimpoptimizedadaptablegradientboosting