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 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.