Doctoral Candidate | Anhui University of Science and Technology | China
Mr. Jialiang Zhu from Anhui University of Science and Technology, Huainan, China, focuses research efforts on advanced cement-based materials with a particular interest in optimizing their performance for engineering applications. Zhu’s work integrates computational modeling with experimental techniques to enhance material durability, bonding strength, and toughness—critical parameters for improving the reliability of construction materials. The recent publication titled “Multi-objective optimization of toughness and bonding strength in cement-based sealing materials using RSM coupled with NSGA-II hybrid approach: Performance evaluation and engineering application analysis,” published in Construction and Building Materials (2025), highlights Zhu’s expertise in applying multi-objective optimization algorithms to analyze and refine the mechanical and structural performance of cementitious systems. The study demonstrates a strong focus on the practical engineering relevance of materials, where computational intelligence tools such as the Response Surface Methodology (RSM) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are utilized to achieve optimal trade-offs between conflicting performance metrics. This integrative methodology contributes to developing sustainable and resilient building materials with improved structural integrity and sealing capabilities. Zhu’s research underscores a commitment to bridging theoretical modeling and real-world engineering implementation, addressing contemporary challenges in construction materials’ environmental efficiency and structural optimization. Although currently represented by two Scopus-indexed publications, the work exhibits significant methodological innovation and interdisciplinary value, combining material science, computational optimization, and civil engineering applications. The research direction indicates promising potential for future studies focusing on smart materials, eco-efficient concrete technologies, and computational design frameworks for performance prediction. The integration of data-driven optimization with experimental validation sets a strong foundation for future advancements in sustainable infrastructure materials. 0 Citations 2 Documents 0 h-index.