Abstract Neuro-fuzzy systems (NFS) can explicitly represent and model the input–output relationships of complex problems and non-linear systems, like those inherent in real-world construction engineering and management (CEM) problems. This paper contributes three things previously lacking in CEM literature: a systematic review and content analysis of published articles related to NFS topics in CEM research; identification of criteria to evaluate different NFS; and recommendations to researchers and industry practitioners in choosing a suitable subset of NFS techniques for solving different types of CEM problems. The literature review reveals that NFS classification methods are based on NFS architecture, learning algorithm, fuzzy method, and application area. This paper systematically categorizes CEM application domains (decision making, prediction/forecasting, evaluation/assessment, system modeling and analysis, simulation, and optimization) and maps them to NFS based on their suitability, which is determined using the performance evaluation criteria of convergence speed, computational complexity, interpretability, accuracy, and local minima trapping.

中文翻译：

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建筑工程和管理研究中的神经模糊系统

摘要 神经模糊系统 (NFS) 可以明确表示和建模复杂问题和非线性系统的输入-输出关系，就像现实世界建筑工程和管理 (CEM) 问题中固有的那些。本文贡献了 CEM 文献中以前缺乏的三件事：对 CEM 研究中与 NFS 主题相关的已发表文章的系统回顾和内容分析；确定评估不同 NFS 的标准；并建议研究人员和行业从业者选择合适的 NFS 技术子集来解决不同类型的 CEM 问题。文献综述表明，NFS 分类方法基于 NFS 架构、学习算法、模糊方法和应用领域。本文系统地对 CEM 应用领域（决策、