New random intelligent chemometric techniques for sustainable geopolymer concrete: low-energy and carbon-footprint initiatives

Abstract

The construction industry, being a significant contributor to greenhouse gas emissions, facing considerable attention and demand on account of the increasing global apprehension regarding climate change and its adverse impacts on environments. Geopolymer shows itself as a viable and sustainable alternative to the Portland cement binder in civil infrastructure applications, offering a low-energy, low-carbon footprint solution. This study evaluates five models: Random Forest (RF), Robust Linear Regression (RL), Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and Regression Tree (RT). The RL and RT models were utilized in the prediction of GPC Compressive strength (CS), employing the Matlab R19a regression learner APP. The RNN model was implemented using the Matlab R19a toolkit. Furthermore, the RF model was developed using R studio version 4.2.2 programming code, and the RSM model was constructed using the Minitab 18 toolbox. EViews 12 software was utilized for both pre-processing and post-processing of the data. Additionally, it was employed to convert the non-stationary data into stationary data in order to obtain accurate results. The input variables included SiO2/Na2O (S/N), Na2O (N), Water/Binder Ratio (W/B), Curing Time (CT), Ultrasonic Pulse Velocity (UPV), and 28-day Compressive Strength (Mpa) (CS) as the target variable. The findings of the study indicate that the RMS-M3 model exhibited superior performance compared to all other models, demonstrating a high level of accuracy. Specifically, the Pearson correlation coefficient (PCC) was calculated to be 0.994, while the mean absolute percentage error (MAPE) was found to be 0.708 during the verification phase.

Chemometrics Feasibility for Modelling Chromatographic Behavior of Diazepam using Linear and Non-Linear Techniques: A Data Mining Based Approach

“…Uzun Ozsahin et al, 2022;Yassin et al, 2022). In the concept of data mining in pharmaceutical sector, most of the analysis involves the second type of data mining; even though, the detection of various signals occurs towards a known background dataset, hence, discussion regarding the first type is equally necessary (Abba, Pham, et al, 2020a;Abdulazeez et al, 2023;Ahmad et al, 2021;Farooqui & Mehra, 2018;Jibril et al, 2024;Jibril, Malami, et al, 2023;Mahmoud et al, 2021). Modern data mining employs the use of statistics with other tools, ideas and approaches from computer science, database technology, machine learning, deep learning as well as other classical analytical tools.…”