Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete

• Published in: Construction & building materials 2020-01, Vol.230, p.117021, Article 117021
• Main Authors: Azimi-Pour, Mohammad, Eskandari-Naddaf, Hamid, Pakzad, Amir
• Format: Article
• Language: English
• Subjects: Compressive strength; Fresh properties; High volume fly-ash (HVF); Self-compacting concrete (SCC); Support vector machine (SVM); Various volume fly-ash (VVF)
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2019.117021

•Properties of HVF-SCC predicted from VVF-SCC data by using SVM method.•Appropriate inputs was used and kernel coefficients calculated by grid search.•New models were compared by previous methods and validated by experimental results. Support vector machines (SVMs) have recently been used to model the properties of low volume fly ash self-compacting concrete (LVF-SCC) by means of kernel functions to minimize the experimental work. Appropriate linear and non-linear SVM models with different kernels (linear, polynomial, radial basis and sigmoid) were proposed in this paper to predict the fresh properties and compressive strength of high volume fly ash SCC (HVF-SCC) from various volume fly ash SCC (VVF-SCC). Since most available data contain relative low volume fly ash, new SVM models were trained on VVF-SCC mixture proportions adapted from literature, and the prediction results were compared to those of previous models for HVF-SCC and LVF-SCC. Moreover, an experimental plan containing six different SCC mixtures was established and the proposed SVM models were validated by experimental results, including compressive strength, L-box, slump, U-box, and V-funnel. Results showed that new SVM models provide better outcomes if considering appropriate input vector and wide range data to obtain the proper kernel function coefficient to predict the various properties of HVF-SCC. Among the kernel functions, prediction results of the SVM – RBF model were more accurate compared to other kernels.