基于AI的建材大数据平台构建及其教学科研双效实践

Construction of an AI-Based Big Data Platform for Building Materials and Its Dual Practice in Teaching and Scientific Research

摘要: 随着人工智能与大数据技术的快速发展，其在建筑材料领域的科研与教学改革中展现出显著潜力。本文以水泥混凝土材料为核心研究对象，结合省部级重点实验室、教学实验中心及科普基地的多平台资源，系统探讨AI与大数据技术在材料研发、实验管理及教学创新中的应用路径。研究通过构建建材大数据平台，整合材料性能数据库与实验过程数据流，实现水泥混凝土材料的成分–工艺–性能关联建模，提升材料设计的智能化水平。同时，基于机器学习算法开发实验参数优化模型，缩短传统试错周期，推动实验室向精准化、自动化转型。在教学改革方面，论文提出“数据驱动 + 虚实融合”的教学模式，通过智能实验管理系统与虚拟仿真平台，重构实验课程体系，强化学生数据分析与跨学科应用能力。研究进一步指出，技术应用需突破数据标准化、算法适配性及师资复合能力三大瓶颈，建议构建“产学研用”协同机制，深化平台的示范引领作用。本文为建筑材料领域的科研范式变革与教学模式创新提供了理论支撑与实践参考。

Abstract: With the rapid advancement of artificial intelligence and big data technologies, their potential to transform research and pedagogical practices in the field of construction materials has become increasingly evident. Focusing on cementitious concrete materials as the primary research subject, this study leverages the resources of provincial-ministerial key laboratories, teaching experiment centers, and science-popularization platforms to systematically examine the pathways through which AI and big data can be integrated into materials research, experimental management, and pedagogical innovation. By establishing a big-data platform for construction materials, the study consolidates databases of material properties with data streams generated during experimental procedures, enabling composition-process-performance correlation modeling for cementitious concrete materials and thereby enhancing the intelligence level of material design. Meanwhile, machine-learning-based models are developed to optimize experimental parameters, shortening traditional trial-and-error cycles and facilitating the laboratory’s transition toward precision-oriented and automated workflows. In terms of educational reform, the paper proposes a “data-driven and virtual-physical integrated” instructional framework. Through the deployment of intelligent experiment management systems and virtual simulation platforms, the experimental curriculum is restructured to strengthen students’ capabilities in data analysis and interdisciplinary application. Furthermore, the study identifies three key challenges that must be addressed for effective technological implementation-data standardization, algorithmic adaptability, and the development of faculty with integrated competencies-and recommends establishing an industry-academia-research-application collaborative mechanism to reinforce the platform’s role as a demonstrative model. This work provides both theoretical grounding and practical guidance for paradigm shifts in construction materials research and for innovations in teaching methodologies.