Effect of Graphene Oxide and Crumb Rubber on the Drying Shrinkage Behavior of Engineered Cementitious Composite (ECC): Experimental Study, RSM�Based Modelling and Optimization

• Main Authors: Abdulkadir, I., Mohammed, B.S., Liew, M.S., Wahab, M.M.A.
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Online Access: http://scholars.utp.edu.my/id/eprint/28913/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121651497&doi=10.1007%2f978-981-16-5041-3_3&partnerID=40&md5=232b67254918706e3e39a63abff54031
• Institution: Universiti Teknologi Petronas

Despite the amazing ductility of ECC, excessive shrinkage remains one of its disadvantages. This is mainly due to the lack of coarse aggregate and high cementitious material content of the mix. This paper aims to explain the findings of an investigation on the effect of Graphene oxide (GO) and crumb rubber (CR) on the drying shrinkage of ECC and also develop a response-based model and optimization using response surface methodology (RSM). The input factors for the RSM are the GO at 0, 0.02, 0.04, 0.06, and 0.08 addition by cement weight and CR at 1, 3, and 5 replacement levels of fine aggregate. Using the �User Defined� option of the RSM, 15 mixes were developed containing varying combinations of the GO and CR on which the response (shrinkage) was investigated for 3 months. The results indicated that the drying shrinkage of the composite significantly reduced with an increase in the GO but with completely opposite effect for CR replacement. There was a 30, 30.5, and 38.8 decrease in the drying shrinkage at 1, 3 and 5 CR contents between mixes having 0 and 0.08 GO, respectively. An increase of 13.6�96.6 in drying shrinkage was recorded at 1 to 5 CR replacement for mixes having 0 GO. The Response predictive model developed has an R2 of 98. Using the optimized levels of the dependent variables, the model was experimentally validated and there is significant agreement between the predicted and the experimental drying shrinkage values with less than 5 error. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.