Study on the Mechanical Properties of Polyurethane-Cement Mortar Containing Nanosilica: RSM and Machine Learning Approach

作者：M. S. M. Al-kahtani, Han Zhu, Yasser E. Ibrahim, Sadi Ibrahim Haruna, SultanAli Alqahtani · 发表于：Applied Sciences · 年份：2023 · DOI：10.3390/app132413348 · 被引用次数：18 · 研究领域：Innovative concrete reinforcement materials、Concrete and Cement Materials Research、Asphalt Pavement Performance Evaluation

Polymer-modified cement mortar has been increasingly used as a runway/road pavement repair material due to its improved bending strength, bonding strength, and wear resistance. The flexural strength of polyurethane–cement mortar (PUCM) is critical in achieving a desirable maintenance effect. This study aims to evaluate and optimize the flexural strength of PUCM involving nano silica (NS) using a central composite design/response surface methodology (CCD/RSM) to design and establish statistical models. The PU binder and NS were utilized as input parameters to evaluate the responses, such as compressive and flexural strength. Moreover, machine learning (ML) algorithms including artificial neural networks (ANN) and Gaussian regression process (GPR) were used. The PUCM mixtures were prepared by adding a PU binder at 0%, 10%, 15%, and 25% by weight of cement. At the same time, NS was incorporated into the mortar mixes at 0 to 3% (interval of 1%) by cement weight. The results showed that the simultaneous effect of PU binder at the optimal content and NS improved the performance of PUCM. Adding NS to the mortar mixture mitigated some of the strength lost due to the PU binder, which remarkably reduces the strength properties at a high content. The optimized PUCM can be obtained by partly adding 3.5% PU binder and 2.93% NS particles by the weight of cement. The performance of the machine learning algorithms was tested using performance indicators such as the determination of coefficie...