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Redefining structural soundness in concrete constructions: A groundbreaking technique for water–cement ratio assessment in sustainable building integrated with explainable artificial intelligence

Authors :
Mahmud M. Jibril
Umar Jibrin Muhammad
Musa Adamu
Yasser E. Ibrahim
Mishal H. Aljarbou
Source :
AIP Advances, Vol 14, Iss 6, Pp 065226-065226-15 (2024)
Publication Year :
2024
Publisher :
AIP Publishing LLC, 2024.

Abstract

Predicting concrete’s compressive strength (CS) is a crucial and challenging task in civil engineering as it directly impacts the longevity and structural integrity of infrastructure initiatives. Precise estimation of the water–cement ratio (W/C) is essential for guaranteeing the structural integrity of structures since it is a critical parameter that greatly affects concrete’s CS. This study carries out an extensive investigation of the prediction of the W/C of concrete, utilizing the enormous potential of machine learning, including the backpropagation neural network (BPNN), bilayer neural network, boosted tree algorithm, bagged tree algorithm (BGTA), and support vector regression (SVR), using 108 datasets. We integrate artificial intelligence models with traditional engineering techniques to develop a reliable, precise, and efficient forecasting system. The study input includes curing days (D), fiber (F), cement (C), fine and coarse aggregate (FA and CA), density (Den), CS, water (W), and W/C as the output variables. The result shows that, in comparison to the other models, BGTA-M3 achieved the best performance evaluation criterion. In the calibration and verification phases, NSE, PCC, R, and WI = 1 and MAPE = 0.00, respectively. BPNN-M3 had an MAPE of 0.0004 in the verification phase. The study uses SHapley Additive exPlanations (SHAP), an explainable artificial intelligence (AI) technique, to improve decision-making in complex systems, with cement “C” significantly contributing to higher predictions in SVR-M2. Future studies should expand the dataset to include information from diverse geographic areas, environmental conditions, and concrete mixes to enhance the applicability and dependability of the models.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
21583226
Volume :
14
Issue :
6
Database :
Directory of Open Access Journals
Journal :
AIP Advances
Publication Type :
Academic Journal
Accession number :
edsdoj.4e5171e7d9444013b051e1fb649d8eb6
Document Type :
article
Full Text :
https://doi.org/10.1063/5.0203867