Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17582
ชื่อเรื่อง: Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials
ผู้แต่ง: Ahmad W.
Ahmad A.
Ostrowski K.A.
Aslam F.
Joyklad P.
Zajdel P.
Keywords: Adaptive boosting
Blast furnaces
Compressive strength
Decision trees
Errors
Fly ash
Forecasting
Gene expression
Mean square error
Sensitivity analysis
Slags
Supervised learning
Coefficient of determination
Compressive strength of concrete
Gene-expression programming
Machine learning approaches
Machine learning techniques
Mean absolute error
Performance
Properties of concretes
Supervised machine learning
Supplementary cementitious material
Concretes
วันที่เผยแพร่: 2021
บทคัดย่อ: The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2 ), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
URI: https://ir.swu.ac.th/jspui/handle/123456789/17582
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116491580&doi=10.3390%2fma14195762&partnerID=40&md5=e180412dbe43cbb64af688fe3feb1a01
ISSN: 19961944
Appears in Collections:Scopus 1983-2021

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