Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

被引:37
作者
Shah, Muhammad Izhar [1 ]
Memon, Shazim Ali [2 ]
Khan Niazi, Muhammad Sohaib [3 ]
Amin, Muhammad Nasir [4 ]
Aslam, Fahid [5 ]
Javed, Muhammad Faisal [1 ]
机构
[1] COMSATS Univ Islamabad, Dept Civil Engn, Abbottabad Campus, Abbottabad 22060, Pakistan
[2] Nazarbayev Univ, Sch Engn & Digital Sci, Dept Civil & Environm Engn, Nur Sultan 010000, Kazakhstan
[3] Qurtuba Univ Sci & Informat Technol, Civil Engn Dept, Dera Ismail Khan, Pakistan
[4] King Faisal Univ KFU, Coll Engn, Dept Civil & Environm Engn, PO 380, Al Hufuf 31982, Al Ahsa, Saudi Arabia
[5] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Civil Engn, Al Kharj 11942, Saudi Arabia
关键词
UNCONFINED COMPRESSIVE STRENGTH; PARTICLE SWARM OPTIMIZATION; ARTIFICIAL NEURAL-NETWORK; HIGH-PERFORMANCE CONCRETE; SUGARCANE BAGASSE ASH; DURABILITY PROPERTIES; BLENDED-CEMENT; REGRESSION; CONSTRUCTION; REPLACEMENT;
D O I
10.1155/2021/6682283
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment.
引用
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页数:15
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